1]Northeastern University 2]Institute of General and Physical Chemistry,
Belgrade 3]University of California Los Angeles 4]University of Florida
5]University of California Davis 6]University of Wisconsin-Madison
7]University of Belgrade

# Longevity Studies of CSC Prototypes Operating with Ar+CO2 Gas Mixture and
Different Fractions of CF4

Emanuela Barberis  Nebojsa Begovic  Nicholas Haubrich  Mikhail Ignatenko
Andrey Korytov  Ota Kukral  Ekaterina Kuznetsova  Armando Lanaro  Andrew
MacCabe  Predrag Milenovic  Dubravka Milovanovic  Guenakh Mitselmakher
Aleksandra Radulovic  Boris Rajcic  Jake Rosenzweig  Bingran Wang  Jian Wang
Andrew Wisecarver  Darien Wood  Emma Yeager

###### Abstract

Studies of Cathode Strip Chamber longevity, comparing Ar+CO2 gas mixtures with
fractions of 5%, 2%, and 0% CF4, were performed using several small cathode
strip prototype chambers. In each trial, a localized source of radiation was
used to irradiate up to an accumulated charge of about 300 mC/cm.
Additionally, longevity of a uniformly irradiated prototype operating with 2%
CF4 was studied at the CERN Gamma Irradiation Facility GIF++. Post-hoc
analysis of the chamber electrodes using spectroscopy techniques was also
done.

###### keywords:

MWPC, Cathode Strip Chamber, CMS, Longevity, CF4

##  1 Introduction and Motivation

###  1.1 The Cathode Strip Chamber system of the Compact Muon Solenoid

The CMS detector features a powerful system for muon detection. The base
component of this system in forward “endcap” regions (0.9$<|\eta|<$2.4)
comprises 540 Cathode Strip Chambers (CSCs), which are multiwire proportional
chambers [1]. The CSCs are used for muon identification, triggering, and track
measurements [2]. The chambers are arranged in 4 muon stations located between
steel disks of each CMS endcap. CSCs have trapezoidal shape and are grouped in
rings. A muon station consists of 2 or 3 rings located at different radial
distances from the LHC beam pipe. Rings of CSCs are labeled as ME$S/R$, where
$S$ indicates the station position in $|z|$ and $R$ gives the ring number. All
chamber types, except for the most forward ME1/1 [3], differ only in the size
of the active area and are constructed of the same material using the same
assembly technology. The CSC system was installed before the startup of the
LHC, except for ME4/2 chambers, which were produced and installed during the
LHC long shutdown in 2013-2014. The CSCs have operated successfully since the
beginning of CMS operations, and continue to do so. A future upgrade of the
accelerator complex to High-Luminosity LHC (HL-LHC) will increase luminosity
to reach (5-7.5)$\times 10^{34}$ cm-2s-1. After the readout electronics
upgrade performed during the LHC long shutdown 2018-2021, the CSC system is
intended to continue efficient operation through the HL-LHC era.

Each CSC consists of six detecting layers with two cathode planes made of
copper-cladded FR4 [4]. One of the cathode planes has the copper coating
milled into strips of several millimeters width (Fig. 1, left) so the charge
induced on the cathode is shared among several readout channels (Fig. 1,
right) in order to provide a good track spatial resolution in azimuthal plane.
Radial track coordinates are measured using anode signals. Gold-plated
tungsten anode wires have 50 $\mu$m diameter and are perpendicular to the
cathode strips for the non-ME1/1 CSC types.

Figure 1: The construction of a full-sized CSC, with part of the top layer cut
away to show part of the wire plane (in gold) with the cathode strips below
(left) and the depiction of signal induction on the cathode strips created by
an ionizing particle traversing the chamber volume (right).

The CSC gas mixture contains 40% Ar, 50% CO2, and 10% CF4 and provides stable
chamber operation in the proportional mode with the average gas gain around
6x104.

The relative fraction of Ar in the mixture controls the charge amplification
of the initial energy deposit (gas gain) and consequently the minimum voltage
required to operate the chamber with a reasonable gas gain. CO2 is primarily
used for its quenching effect that allows the detection volume to recover
after the passage of an ionizing particle by absorbing high-energy photons
produced in the avalanche. This shortens the “dead-time” after the initial
signal has passed and protects the chamber from damage due to sparking, caused
by a positive feedback loop of secondary and tertiary reactions.

Adding 10% CF4 to the Ar/CO2 mixture extends the chamber lifetime preventing
anode wire aging [5]. Though CF4 also increases the drift velocity of the
electrons in a gas mixture, this effects depends on the electrical field
configuration and for the CSC geometry is negligible if the CF4 fraction in
the gas mixture is below 20% [6]. Thus at the nominal CSC operation voltages
the main purpose of CF4 is extension of the chamber longevity.

###  1.2 Chamber aging

In a CSC the avalanche charge amplification of several ten thousand times
occurs in vicinity of a thin anode wire. The avalanche can be considered as
short plasma discharge of microscopic size where ions and free radicals of the
gas atoms and molecules are produced, with some radicals having sufficient
lifetime to reach CSC electrodes. Plasma chemical reactions may cause
modification of the chamber electrode surfaces either due to direct reactions
of the plasma products with the electrode material, or due to formation of
deposits on the electrodes. This may lead to degradation of the chamber muon
detection performance and so may limit its long-term operation.

The most dangerous type of aging is modification of the anode surface. Any
deposit on the anode wire may increase its diameter causing non-uniform
reduction of the gas gain and, in case of the cathode strip chambers, a
degradation of the spatial resolution. Upon significant decrease of the gas
gain, the induced signal may become too low to provide efficient muon
detection with the nominal readout electronics. Sharp needle-like deposits
cause increase of the single layer noise rate and, in the worst cases, may
result in appearance of sparks or even electrical breakdowns.

Thin insulating deposits on cathode surface may cause increase of secondary
electron emission resulting in high noise rate or even in rather strong
persistent currents (so-called Malter effect [7, 8]). If cathode deposits are
conductive and cover gaps between CSC strips, they may affect the spatial
resolution.

At the same time, the plasma conditions may be used to protect the chamber
against degradation, as it is done using a small quantity of CF4 in the
working gas mixture of the CMS CSCs. Fluorine radicals created in the plasma
discharges react with silicon or carbon -based molecules, which may be present
in various quantities in chamber material or gas impurities, and are proven to
provoke aging [9, 10]. Fluorine radicals prevent polymerization of organic
molecules and also bond silicon into volatile SiF4 molecules, which are
removed from the chamber with the gas flow.

###  1.3 Motivation for reducing or eliminating CF4

CF4 is an expensive gas and its release into the atmosphere is detrimental to
the environment. It belongs to a class of fluorocarbon gases that have a
negative impact on atmospheric chemistry, leading to greenhouse warming
effects on the Earth’s surface. To be specific, CF4 has a Global Warming
Potential (GWP) of 6630 over 100 years [11].

CF4 is subject to the European Union F-gas regulation (Regulation (EU) No
517/2014, [12]) which aims to the reduction of the F-gas emission by two-
thirds by 2030 compared with 2014 levels. Moreover, recently the European
market started to experience periodic issues in supply chains that makes CF4
more difficult and expensive to obtain regularly. So, either lowering the
fraction of CF4 or excluding it entirely from the CSC gas mixture is well-
motivated.

One of the approach for reducing the CF4 exhaust is recirculating and
recuperating the gas. Recirculation was implemented from the beginning of the
CSC operation using the closed-loop gas system with 10% replenishment rate.
Additionally, a CF4 recuperation system was developed during last decade by
the CERN EP-DT group and brought to operation with about 60% efficiency by the
beginning of Run3.

In addition to that, studies of the possible reduction of the CF4 content in
the CSC gas mixture were started.

##  2 Previous Studies of CSC gases

In the early stages of design and testing of the CSCs, before their initial
installation in CMS, the longevity of chambers was studied [5] by operating
test chambers while being radiated by a strong 90Sr source. These chambers
were constructed from mostly the same materials as those intended for the
production chambers. A notable exception was the use of silicon-based RTV
sealant first applied before the chamber was closed, allowing a possible leak
of RTV inside the chamber, creating a source of silicon in the gas volume.

For two gas mixtures containing CF4, Ar/CO2/CF4 40%/50%/10% and 30%/50%/20%,
the irradiation was continued up to a local accumulation of about 13 C/cm on
the anode wire. Even with this large accumulated charge, no appreciable
decrease was observed in the gas gain. Some increase of the dark current was
observed, which indicated aging effects on the cathodes, but the overall
chamber performance was not degraded significantly.

For a 70%/30% Ar/CO2 mixture without CF4, however, a gas gain drop of a factor
of two was observed for each 0.25 C/cm accumulated. In a post-mortem
examination of the test chambers, a thick coating was found on the anode
wires. An elemental analysis of this coating revealed a major component of
silicon. The source of the silicon was likely the RTV, and the probable
conclusion was that the CF4 was effective in preventing the build-up of
silicon-based polymers on the anode wires.

In the years 2000 and 2001, two full-size chambers were studied for aging [13]
using the 740 GBq radioactive 137Cs source (0.662 MeV gammas) at the Gamma
Irradiation Facility (GIF) at CERN. The gas mixture used was Ar/CO2/CF4
40%/50%/10%, with an open-loop gas system in 2000 and a closed loop system in
2001. The accumulated charges were 0.3-0.4 C/cm. In both cases, no appreciable
decrease in gain was observed.

In anticipation of the HL-LHC, a new campaign of aging studies was launched
[14] with full-sized spare CSCs operated with the nominal gas mixture
Ar/CO2/CF4 40%/50%/10%. The irradiation was performed at the upgraded Gamma
Irradiation Facility (GIF++) with 14 TBq 137Cs source [15].

The gain and resolution of the chambers were monitored and showed no sign of
deterioration up to the accumulated charge of 0.33 C/cm, which corresponds to
the charge expected at the HL-LHC conditions with a safety factor of at least
1.5, depending on the chamber type and its position in the CMS detector.

##  3 Experimental Design and Procedure

As detailed in Section 1, aging can be defined as a significant chemical
change on the surface of electrodes inside the detection volume of a gaseous
proportional chamber. Depending on the type of chemical reactions occurring,
operation stability, gas gain, efficiency or resolution may be affected; these
parameters can be monitored through non-destructive techniques. Additionally,
if the chambers can be disassembled after exposure, spectroscopic techniques
can be used to study the change of the electrode material itself.

To allow rapid and flexible tests, and to facilitate post-mortem analysis of
the wires and cathode surfaces, a set of small CSC prototypes, or “miniCSCs”,
was constructed. The aging tests with the miniCSCs were carried out in three
locations: the CERN 904 lab (B904), the GIF++ radiation facility at CERN, and
the Petersburg Nuclear Physics Institute (PNPI) in Russia.

###  3.1 miniCSC chamber design

A miniCSC is a small prototype of the full-size CMS CSC, and an example is
shown in Fig. 2. These chambers were built by using the same materials as used
for the construction of the ME4/2 CSCs in 2012-2013, following the same
assembly procedures and quality checks. The dimensions of the miniCSCs are 30
cm$\times$30 cm, and they have two layers instead of six, with a volume of
about 0.5 L for a single gas gap. As for the standard CSCs, the cathodes of
the miniCSCs are composed of copper-clad FR4 with strings milled into the
copper, and the wires are gold-plated tungsten of 50 $\mu$m diameter. The
geometry of the chamber was inherited from the original CSC and is specified
in Table 1. To provide efficient irradiation and allowing control measurements
with a 109Cd X-ray source, cylindrical holes were drilled into the FR4 of the
outer cathode layer, still preserving the integrity of the cathode surface.

Figure 2: Front view of a miniCSC chamber.  gas gap, $2h$ [mm] | 9.53
---|---
wire spacing, $s$ [mm] | 3.13
wire diameter, $d$ [$\mu$m] | 50
wire plane-to-plane spacing, [mm] | 25.4
strips per plane (*) |  $\approx$10
strips pitch [mm] (*) |  $\approx$10
Table 1: Construction parameters of miniCSC. The parameters marked as (*)
differ from the production chamber ones.

###  3.2 Test stand and measurement procedures

####  3.2.1 Irradiation

A 90Sr source with a nominal activity of about 34 MBq was used to locally
irradiate the top chamber layer. The distance between this source and the
underlying cathode plane inside the gas volume was a few cm. The beamspot is
circular with a diameter of 2.3 cm on the anode and 3.5 cm on strip planes, as
estimated from the measured spatial charge distribution.

During the irradiation procedure, the top miniCSC layer ("irradiated layer")
was supplied with the nominal CSC high voltage of 3600 V, while the voltage of
the bottom, "reference", layer was kept off. The current was about 1 $\mu$A
and the total wire length in the irradiated spot was estimated as 13.4 cm. The
charge was accumulated at the rate of 4.3 mC/cm per day, yielding an
“acceleration factor” of 100, corresponding to a radiation flow 100 time
higher than the maximal maximal expected irradiation rate for the ME2/1 CSC
operating under HL-LHC conditions. The rate of the HL-LHC charge accumulation
was estimated using the background hit rates and currents measured for CSC in
Run 2 and corrected with FLUKA predictions for presence of a new calorimeter
expected to be installed by the LH-LHC era. The target accumulated charge for
each trial was set at 300 mC/cm, which is about 2.7 times of that expected for
an ME2/1-type chamber at a 10 years-equivalent of HL-LHC operation.

For the gas system, an “open-loop” setup with Polyamide 11 tubing (Rilsan) was
used. The flow of gas through the chamber was directed first through the
reference layer and then into the irradiated one at a flow rate of 0.5 L/hour,
or one layer volume per hour. Though the local irradiation conditions are
difficult to scale to the uniform irradiation of CSCs at the CMS, the gas
refreshment rate per a single layer was chosen to correspond to the nominal
CSC flow rate of one chamber volume per six hours. No additional scaling
factor was introduced to account for the accelerated irradiation.

####  3.2.2 Control measurements

After each irradiation session, a set of measurements was performed in order
to monitor chamber characteristics sensitive to aging effects as functions of
the accumulated charge. Depending on the characteristic type, the measurements
performed for the irradiated zone were compared to the ones taken for a non-
irradiated part of the same layer, or for the reference layer.

Single-layer dark rates as well as the charge and rate of signals induced by
an X-ray source were measured with the original CSC electronics. The dark
current was measured using a Keithley 487 picoammeter.

To monitor the stability of the gas gain, control measurements with a 109Cd
source were performed in several points across the layer. Reproducibility of
the source position in different measurement sessions was guaranteed by the
small diameter of windows in the upper chamber panel. The standard CSC
electronics allows read out of the matched discriminated signals from the
cathode strips and anode wire groups and, if such a coincidence occurs, the
charge induced on the strips. The charge is shared among three neighbour
strips forming a cluster. The measurements were done for irradiated and the
control non-irradiated points of the same layer, and the ratio of those values
represents the relative gas gain free of the pressure and temperature
fluctuations. The rate of the signals was measured as a function of the high
voltage and provides the additional information on the gas gain stability and
also on the presence of after-pulses or sparks related to the avalanche. The
rate measurements were done for the whole layer and were compared to the rate
measured when no source is present, i.e. dark rate.

The dark rates and dark currents were measured for the voltage range of
2.5-3.8 kV. The dark current was measured between the joined strips and
ground. Each measurement was performed for the period 10–15 min, and the
average value of the current after settling was taken as the result. To
control the humidity influence, the corresponding current measurements were
done for the non-irradiated layer during the same day.

Cathode aging may appear as a deposition of a thin film which can increase
conductivity across adjacent cathode strips causing degradation of the spatial
resolution of the chamber. The strip-to-strip resistance was monitored
measuring current between two adjacent strips when 300 V was applied to one of
them and second was grounded.

If cathode deposits are insulating and thin, and the chamber occupancy is
relatively high, the effect of Malter currents may appear. This type of
current is caused by emission from the cathode surface when a positive charge
on the insulating film is high enough, and may be persistent even when the
irradiation is stopped. During the irradiation, the current was monitored and
checked for unstable behaviour and spikes. Additionally, every time the 90Sr
source was removed from the chamber, the dark current was monitored for 10-15
minutes.

###  3.3 Additional studies

To cross-check the local irradiation test results, an additional miniCSC was
irradiated at GIF++ with 2% CF4 gas mixture. GIF++ provides nearly uniform
radiation over the whole chamber, which is more representative of the exposure
in CMS. The irradiation acceleration factor of this test was of about 30. The
control measurements procedure for this prototype was the same as in the case
of local irradiation.

Additionally a local irradiation test with a CSC prototype was performed at
PNPI for the nominal CSC gas mixture. The accumulated charged collected during
the test was 1.2 C/cm. No change in the prototype detection performance was
observed [16].

##  4 Longevity Results with varying CF4 fractions

The irradiation and measurement procedures described in Section 3 were carried
out on miniCSCs with three different gas mixtures of Ar/CO2/CF4: 40%/55%/5%,
40%/58%/2%, and 40%/60%/0%. It should be noted that the 2% CF4 trial was
performed after the 5% CF4 one using the same chamber layer. For the 2% CF4
test the irradiation zone was displaced to a distant point of the layer, and
the small size of the irradiated area ensured that the influence of the
previous exposure was negligible. The reference layer was the same in both
trials. For the 0% CF4 study a different miniCSC was used. No significant
change in the characteristics of the miniCSC under test was observed in any of
the tests. Similar results were obtained in the additional uniform irradiation
test with 2% CF4 mixture at GIF++.

###  4.1 Gas gain

Figure 3 shows changes in the relative gas gain obtained as a ratio of the
cluster charge peak position measured for the irradiated and control areas,
for gas mixtures with 5, 2, and 0% CF4. The error bars represent statistical
uncertainties. Stable behaviour was observed during the whole irradiation
period up to 240 mC/cm.

Figure 3: Relative gas gain as the peak position of a 109Cd X-ray spectrum
measured in the irradiated area and normalized to that of non-irradiated
reference zones for gas mixtures with 5, 2, and 0% CF4 (left, center, right).

###  4.2 Dark rate and Cd rate measurements

Figure 4 shows the dark rate per miniCSC layer as a function of the high
voltage measured for different values of the accumulated charge $Q$ for gas
mixtures with 5, 2, and 0% CF4. There is an overall decrease in dark rates in
the 2% CF4 trial compared to the 5% trial. This is possibly an effect of the
“HV training” of the chamber layer, since this same layer was used in both
trials. The differences in dark rate are insignificant, both as a function of
the accumulated irradiation dose and gas mixture type. Thus we conclude that
the dark rate is stable with respect to exposure for all three gas mixtures.

Figure 4: Dark rates for gas mixtures with 5, 2, 0% CF4 (left, center, right)
as a function of high voltage measured for different accumulated charge $Q$.

Figure 5 shows the source rates as a function of the high voltage, collected
for gas mixtures with 5, 2, and 0% CF4, and monitored as a function of the
accumulated charge $Q$. The contribution from dark rates, measured separately
and as shown in Fig. 4, was subtracted. Then the remaining source rates were
corrected for the decrease in the 109Cd source radioactivity. The shape of the
efficiency curve and the value of the plateau are very similar for the three
cases. The slight increase in the count for the 0% CF4 measurements can be
explained by a smaller thickness of the window for positioning the 109Cd
source. No significant change in the efficiency dependence was observed during
irradiation for all three gas mixtures. Figure 6 shows the source count rate
at the operating point of 3.6 kV, as a function of the accumulated charge.

Figure 5: Single-layer count rates for gas mixtures with 5, 2, and 0% CF4
(left, center, right) obtained with a 109Cd X-ray source as functions of the
high voltage for different accumulated charges. The measurements are corrected
for the dark rate contribution and for the decrease of 109Cd source intensity
over the period of the three tests.

Figure 6: Single-layer count rates for gas mixtures with 5, 2, 0% CF4 (left,
center, right) obtained with a 109Cd X-ray source for the operating point of
3.6 kV as functions of the accumulated charge.

###  4.3 Dark current and strip-to-strip resistance

The dark current was measured using the procedure outlined in Section 3. The
values at the working point were of order 102 to 103 pA for all trials and
mostly defined by the leakage currents. Neither current spikes nor dependence
on the accumulated charge was observed.

Figure 7 shows electrical resistance measured between pairs of adjacent
cathode strips, each on a single chamber layer, shown for gas mixtures with 5,
2, and 0% CF4.

Figure 7: Electrical resistance as measured between pairs of adjacent cathode
strips on a single chamber layer, shown for gas mixtures with 5, 2, and 0% CF4
(left, center, right). Strips are selected in the irradiated area (red),
outside the irradiated area but at the same layer (blue) and at the reference
layer (ligh blue and green). The systematic difference in the measured
resistances for different layers are defined by the CSC construction.

##  5 Analysis of Irradiated Electrodes

After irradiation up to about 300 mC/cm, the miniCSCs were disassembled and
visually inspected. Then, samples of anode wires and cathode planes, both from
the irradiated and non-irradiated zones, were prepared for optical and
spectroscopic analyses, as well as virgin samples of anode wires and cathode
planes. Preliminary analysis was performed at CERN MM-MME department, while
final measurements were done at the Physical Chemistry Faculty of the Belgrade
University.

###  5.1 Optical inspection of the irradiated electrodes

Photos of the exposed cathodes, taken just after opening the irradiated layer
of the miniCSC, for the 5, 2, and 0% CF4 trials are shown in Fig. 8. In all
cases, there is a circular spot that corresponds to the primary area of
irradiation where the 90Sr was placed on top of the layer. Also, a circular
area around the irradiated spot is showing a radial color-gradient feature.
The size of the spot decreased as the CF4 fraction in the gas mixture
decreased. The most important observation was visibly darkened anode wires in
the areas irradiated with 2% CF4 and 0% CF4 while no such effect was noticed
for the 5% CF4 irradiation zone.

Figure 9 shows snapshots of the optical microscope images comparing anode
wires irradiated with 5, 2, 0% CF4 gas mixtures and a virgin sample. The
darkening is clearly seen for the 0% CF4 sample and for the 2% CF4 locally
irradiated sample. Wire darkening can be noticed also for 2% CF4 uniform
irradiation, while no color change is seen for the 5% CF4 sample compared to
the virgin wire.

Figure 8: Photos of the irradiated layer for the miniCSC chambers used in the
5, 2, 0% CF4 trials, with the total accumulated charge indicated. Effects of
irradiation can be easily seen on the cathode.  Figure 9: Optical microscopy
snapshots for anode wires irradiated with 5, 2, 0% CF4 gas mixtures compared
to a virgin sample.

###  5.2 Elemental analysis of the electrodes

Figure 10: SEM image and the EDS spectra of different areas of the 2% CF4
anode wire sample.

Figure 11: EDS measurement results for anode wires: relative weight fractions
for gold (left) and carbon (middle) along the wire length and the average
values for carbon, silicon, oxygen, fluorine and tungsten as functions of the
CF4 fraction in the gas mixture.

Morphological and micro-elemental analysis of the electrodes was done using
Scanning Electron Microscopy (SEM) along with Energy Dispersive Spectrometry
(EDS). Preliminary analysis of the anode wires showed presence of carbon for
2% and 0% CF4 samples, as can be seen in Fig. 10.

Figure 12: Relative weight fraction of carbon, oxygen and fluorine on $1\times
1$ mm2 area from the irradiation zone of 0% (top left), 2% (top right) and 5%
(bottom left) cathodes measured in point across samples as shown in the SEM
images in the top of each plot. The values measured from the virgin cathode
sample are provided as dashed lines. The resulting averaged values are shown
as function of the CF4 fraction in the gas mixture together with the XRD
results (bottom right).

The EDS technique is considered to be semi-quantitative but suitable for
comparative studies. To obtain reliable comparisons of different samples, a
multi-point EDS analysis approach was introduced. The final EDS measurements
were done in sets of 50 points equally distributed over a sample area of
$1200\times 1200$ $\mu$m2. This allows measurement of the average content of
detected elements on the electrode surface and thus enables comparative
studies. This method is used to study the distribution of deposits over the
irradiated area and to compare samples from different longevity trials.

Figure 11 shows the results of such analysis applied to anode wires. The left
plot shows the averaged relative weight fraction of gold measured for
different irradiated wires and compared to the average value obtained for the
virgin one. The presence of other elements can be identified as the difference
between the gold fractions measured for the virgin and an irradiated wire. The
anodes from 2% and 0% CF4 longevity tests are clearly seen as more strongly
affected than the one from the 5% CF4 trial. The carbon distribution along the
wires is shown in the middle plot and has a prominent peak in the irradiation
area of the 2 and 0% CF4 samples. The results for carbon, silicon, oxygen,
fluorine and tungsten were averaged over the wire length and are shown in Fig.
11, right, as functions of the CF4 content of the gas mixture.

A similar approach was applied to the cathode samples. The area of $1\times 1$
mm2 from the centre of irradiation zones were measured in 50 points, as shown
in Fig. 12 (top left, top right, bottom left). In addition to EDS
measurements, X-ray diffraction (XRD) techniques were used to analyze chemical
structure of possible crystalline deposits. Figure 12 (bottom right) shows the
resulting averaged weight fractions for carbon, oxygen, fluorine and silicon
as function of the CF4 together with the XRD results. Hydroxide and hydrate
groups may indicate presence of water molecules in the gas mixture during the
irradiation as a consequence of Rylsan pipes of the gas supply. Carbon
concentration is found to be the highest for the 0% CF4 sample, while fluorine
presence increases with CF4 fraction. In the XRD analysis carbon was found in
CuCO3*CuO with small contributions of graphene oxide phases, and fluorine was
identified in Cu(OH)F compound.

##  6 Summary

Aging proprieties of the CMS Cathode Strip Chambers (CSCs) were studied using
miniCSCs, small prototypes built by using the same materials as for the
production chambers. The main purpose was to explore the possibilities of
reducing the fraction of CF4 in the gas mixture with respect to the nominal
10% CF4. Longevity studies with 5%, 2%, and 0% CF4 were performed up to the
accumulated charge of 300 mC/cm. Additional tests were done with similar
prototypes with uniform irradiation and 2% CF4, and with the nominal gas
mixture and local irradiation up to 1.2 C/cm. No noticeable detection
performance degradation was seen in any of those tests.

However, the electron material analysis done after the longevity tests showed
a clear difference in presence of carbon and fluorine on anode and cathode
surfaces for irradiation trials with different CF4 fractions. An increase in
carbon concentration on anode wire surfaces was clearly seen for the samples
from 2%, and 0% CF4 tests. Silicon presence on anode wires and carbon
contamination on cathode surfaces was observed for 0% CF4. Fluorine was found
on cathode samples in quantities increasing with the CF4 fraction in the gas.

Although no longevity issues were observed during the irradiation, the
material analysis indicates that the gas mixtures containing less or equal to
2% of CF4 are potentially dangerous for long-term CSC operation due to carbon
deposition on anode wires.

Acknowledgments

We thank the CERN EN-MME-MM group, and especially Ana Teresa Perez Fontenla,
for initial set of the material analysis and optical microscopy, CMS
mechanical workshop and Valentin Sulimov (PNPI) for help in samples
preparation. We are grateful to the CMS CSC group for supporting the studies
and to the GIF++ team for help in the irradiation test organization. We
gratefully acknowledge financial support from MoSTDI contract number
No.451-03-47/2023-01/200051 (Serbia), DOE (USA) and NSF (USA).

Data Availability Statement The measurement results and analysis procedures
are stored at the common data space of the CMS experiment and can be obtained
from the corresponding author on reasonable request.

## References

  *   * Sauli [1977] Sauli, F.: Principles of operation of multiwire proportional and drift chambers. Technical report, Geneva (1977). https://doi.org/10.5170/CERN-1977-009 . CERN, Geneva, 1975 - 1976. https://cds.cern.ch/record/117989
  * Chatrchyan et al. [2008] Chatrchyan, S., et al.: The CMS Experiment at the CERN LHC. JINST 3, 08004 (2008) https://doi.org/10.1088/1748-0221/3/08/S08004
  * Ershov et al. [2006] Ershov, Y.V., et al.: Cathode strip chamber for CMS ME1/1 endcap muon station. Phys. Part. Nucl. Lett. 3, 183–187 (2006) https://doi.org/10.1134/S154747710603006X
  * Layter [1997] Layter, J.G.: The CMS Muon Project: Technical Design Report. Technical design report. CMS. CERN, Geneva (1997). https://cds.cern.ch/record/343814
  * Ferguson et al. [2002] Ferguson, T., Gavrilov, G., Korytov, A., Krivchitch, A., Kuznetsova, E., Lobachev, E., Mitselmakher, G., Schipunov, L.: Aging studies of cms muon chamber prototypes. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 488(1), 240–257 (2002) https://doi.org/10.1016/S0168-9002(02)00400-X
  * Kisselev and Soulimov [1997] Kisselev, O., Soulimov, V.: Gas Mixture Optimization for CMS Muon Cathode Strip Chambers. Technical report, CERN, Geneva (1997). https://cds.cern.ch/record/687536
  * Malter [1936] Malter, L.: Thin film field emission. Phys. Rev. 50, 48–58 (1936) https://doi.org/10.1103/PhysRev.50.48
  * Albicocco et al. [2019] Albicocco, F.P., et al.: Long-term operation of the multi-wire-proportional- chambers of the LHCb muon system. JINST 14, 11031 (2019) https://doi.org/10.1088/1748-0221/14/11/P11031 arXiv:1908.02178 [physics.ins-det]
  * Kadyk [1991] Kadyk, J.A.: Wire chamber aging. Nucl. Instrum. Meth. A300, 436–479 (1991) https://doi.org/10.1016/0168-9002(91)90381-Y
  * Va’vra [2002] Va’vra, J.: Physics and chemistry of aging: Early developments. ICFA Instrum. Bull. 24, 1–21 (2002) https://doi.org/10.1016/j.nima.2003.08.124
  * Myhre et al. [2013] Myhre, G., Shindell, D., Breon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., Zhang, H.: 8\. Anthropogenic and Natural Radiative Forcing, pp. 659–740. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA (2013). https://doi.org/10.1017/CBO9781107415324.018 . www.climatechange2013.org
  * The European Parliament and the Council of the European Union [2014] The European Parliament and the Council of the European Union: Regulation (EU) No 517/2014 of 16 April 2014 on fluorinated greenhouse gases. Technical report, EU (2014). http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32014R0517&from=EN
  * Prokofev et al. [2003] Prokofev, O., et al.: Aging tests of full scale CMS muon cathode strip chambers. Nucl. Instrum. Meth. A 515, 226–233 (2003) https://doi.org/10.1016/j.nima.2003.09.002
  * Wang [2020] Wang, B.: Longevity studies and phase 2 electronics upgrade for cms cathode strip chambers in preparation for hl-lhc. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 958, 162279 (2020) https://doi.org/10.1016/j.nima.2019.06.020 . Proceedings of the Vienna Conference on Instrumentation 2019
  * Jäkel et al. [2014] Jäkel, M.R., Capeans, M., Efthymiopoulos, I., Fabich, A., Guida, R., Maire, G., Moll, M., Pfeiffer, D., Ravotti, F., Reithler, H.: CERN-GIF++: a new irradiation facility to test large-area particle detectors for the high-luminosity LHC program. PoS TIPP2014, 102 (2014) https://doi.org/10.22323/1.213.0102
  * Buzoveria et al. [2020] Buzoveria, M.E., Zavyalov, N.V., Karpov, I.A., Tkachenko, M.I., Dziuba, A.A., Maisuzenko, D.A., Gavrilov, G.E., Nasybulin, S.A., Grechkina, M.V.: Investigation of the Cathode Plane Radiation Damage in the Prototypes of Multiwire Proportional Chamber from the CMS Experiment. Phys. Atom. Nucl. 82(9), 1252–1262 (2020) https://doi.org/10.1134/S1063778819090035


# Fairness-aware Summarization for Justified Decision-Making

Moniba Keymanesh
&Tanya Berger-Wolf
&Micha Elsner
&Srinivasan Parthasarathy
&The Ohio State University
{keymanesh.1, berger-wolf.1, elsner.14<EMAIL_ADDRESS>

###### Abstract

In many applications such as recidivism prediction, facility inspection, and
benefit assignment, it’s important for individuals to know the decision-
relevant information for the model’s prediction. In addition, the model’s
predictions should be fairly justified. Essentially, decision-relevant
features should provide sufficient information for the predicted outcome and
should be independent of the membership of individuals in protected groups
such as race and gender. In this work, we focus on the problem of (un)fairness
in the justification of the text-based neural models. We tie the explanatory
power of the model to fairness in the outcome and propose a fairness-aware
summarization mechanism to detect and counteract the bias in such models.
Given a potentially biased natural language explanation for a decision, we use
a multi-task neural model and an attribution mechanism based on integrated
gradients to extract the high-utility and discrimination-free justifications
in the form of a summary. The extracted summary is then used for training a
model to make decisions for individuals. Results on several real world
datasets suggests that our method: (i) assists users to understand what
information is used for the model’s decision and (ii) enhances the fairness in
outcomes while significantly reducing the demographic leakage.

##  1 Introduction and Related Work

AI systems are increasingly adopted to assist or replace humans in several
highly consequential domains including recidivism assessment [5], policing
[64, 48], credit card offering [66], lending [52], and prioritizing resources
for inspection 111https://chicago.github.io/food-inspections-evaluation/. To
maximize the utility, such models are trained to minimize the error on
historical data which may be biased toward certain individuals or groups. Bias
in the data can be due to historical bias, representation bias, or measurement
bias [55, 68, 57]. Training models on biased data without fairness
considerations have resulted in several cases of discrimination against
certain groups of individuals [51, 58, 4, 10]. Discrimination in this context
is defined as the unjustified distinction between individuals based on their
membership in a protected group 222Protected groups that are currently
recognized by law include race, gender, and color [1]. Find a full list of
protected classes in https://en.wikipedia.org/wiki/Protected_group[70]. The
concerns and observations regarding the unfairness of AI algorithms have led
to a growing interest in defining, measuring, and mitigating algorithmic
unfairness [60, 8, 18, 30, 38].

In recent years, several techniques have been proposed to enhance fairness in
machine learning algorithms. Such methods can be broadly categorized into pre-
processing methods, in-processing methods, and post-processing methods [60].
Pre-processing mechanisms use re-weighting, relabeling or other
transformations of the input data to remove dependencies between the class
label and the sensitive attributes before feeding it to the machine learning
algorithm [31, 13, 77, 28, 23, 27, 73]. This approach is closely related to
the field of privacy [26]. Since both fairness and privacy can be enhanced by
obfuscating sensitive information from the input data with the adversary goal
of minimal data perturbation [46, 41]. Our proposed approach of using
fairness-aware text summarization to remove bias from the input explanations
also belongs to this category. In-processing methods modify the optimization
procedure of the classifier to integrate fairness criteria in the objective
function [45, 3, 12]. This is often done by using a regularization term [24,
74, 75, 76, 32, 6, 7, 61, 43], meta-learning algorithms [15], reduction-based
methods [2, 22], or adversarial training [54, 78, 16, 72]. Post-processing
methods adjust the output of the AI algorithm to enhance fairness in decisions
[29]. For example, by flipping some of the decisions of the classifier [33] or
learning a different classifier [25] or a separate threshold for each group
[56].

A majority of these methods mitigate bias in decision-making by minimizing the
difference between treatment and outcome among different protected groups.
However, in some cases, the differences can be justified and explained using
some features and therefore is not considered illegal [55]. For example,
Kamiral et al [44] state that the difference in income level in females and
males in the UCI adult income dataset
333https://archive.ics.uci.edu/ml/datasets/adult\- a well-studied dataset in
algorithmic fairness research- can be attributed to the difference in working
hours. They argue that methods that do not take into account the
explainability aspect of discrimination will result in reverse discrimination
which is equally harmful. This brings us to the focus of our work - fairly-
justified decision-making. A fairly-justified decision should not be based on
information about membership in protected groups. In addition, the
justification should include enough information to explain the outcome. In
other words, given the justifications, human subjects should be able to
understand why a decision has been made for them and interpret what were the
main factors that led to the received outcome [14].

In this work, we propose a fairness-aware summarization mechanism as a pre-
processing step to reduce potential biases from textual justifications.
Automatic summarization [65] has been previously used to extract text based on
human-interpretable syntactic or semantic properties. We propose methods to
first identify and measure bias in textual explanations and then mitigate this
bias using a filtering-based approach. We measure bias 1) by using metrics
such as demographic parity [11], equalized odds [33], and calibration [51] and
2) by measuring the adversary’s ability to identify membership in protected
groups given the textual explanations. To counteract the bias, our proposed
summarization model extracts explanations that maximize utility for the final
decision while removing unfair justifications that correlate with membership
in protected groups. Next, the extracted fairly-justified summaries will be
used to train a final model. This framework facilitates learning fairly
justified models by removing biases from input explanations. Additionally, it
assists users with understanding why a decision has been made for them by
presenting the most predictive justifications. To summarize, in this study, we
make the following contributions: C1. We propose the use of a multi-task model
and an attribution mechanism to attribute the decision of the model as well as
potential biases in the justification for the decision to certain parts of the
inputs. C2. We propose a fairness-aware summarization model to condense the
input explanations by extracting the most predictive justification while
removing the unfair ones. C3. We show that this pre-processing step does not
hurt the utility of the model but significantly limits the leakage of
information about protected attributes of individuals in the input
justifications. C4. We show that pre-processing the input justifications to
make them fair using our proposed approach also moderately enhances the
fairness in the outcome. Next, we will formally define our problem and explain
our proposed solution.

##  2 Problem Formulation

Given a dataset consisting of $n$ samples
$\\{(X_{i},Y_{i},P_{i})\\}^{n}_{i=1}$ where $X$ denotes a textual explanation
written by the decision-maker to provide evidence or justify an outcome $Y$
and $P$ indicates one or more protected variables such as gender or race, we
aim to extract a fairly-justified summary $\\{{X_{i}}^{\prime}\\}^{n}_{i=0}$
such that i) $X^{\prime}$ provides sufficient information to predict and
justify $Y$ and ii) $X^{\prime}$ is independent of protected variable $P$. We
explain how we measure and attribute these qualities to sentences in the
justification $X$ in Section 3. For instance, $Y_{i}$ could represent a court
decision for individual $i$, which is a member of the demographic group
$P_{i}$ and has received a textual argument $X_{i}$ regarding this decision.
Potentially, $X_{i}$ can be biased toward certain demographic groups. Our goal
is to transform a given dataset $\\{(X_{i},Y_{i},P_{i})\\}^{n}_{i=1}$ into a
new dataset $\\{({X_{i}}^{\prime},Y_{i},P_{i})\\}^{n}_{i=1}$ that is
decontaminated from unfair arguments. To achieve this goal, we use a fairness-
aware extractive summarization model as a data pre-processing step.

Figure 1: A graphical model of the proposed approach. $P$ represents the
protected attribute. $X$ indicates the input explanations while $X^{\prime}$
indicates the farily-justified summary of $X$ which is used to train the final
model to predict outcome $\hat{Y}$.

####

##  3 Proposed Methodology

In this section, we explain our proposed methodology to extract a fairly-
justified summary $\\{{X_{i}}^{\prime}\\}^{n}_{i=0}$ such that i) summary
$X^{\prime}$ provides sufficient information to predict and justify $\hat{Y}$
and ii) the extracted summary $X^{\prime}$ is independent of protected
variable $P$. A graphical model of the proposed approach is shown in Figure 1.
Given an input explanation $X_{i}$ consisting of sentences
$\\{s_{1},s_{2},...,s_{m}\\}$, the goal of our proposed fairly-justified
extractive summarization model is to select a subset of these sentences
subject to a utility and a fairness constraint. Next, we explain how we
measure and attribute utility and discrimination of the input sentences.

Utility Control:  To ensure that the extracted summary $X^{\prime}$ includes
sufficient decision-relevant information in $X$, we measure the salience of
each sentence in $X$ in predicting outcome $Y$. We train a neural
classification model on $X$ using ground truth decision $Y$ as supervision.
Next, we use this model to derive the contribution of each sentence in $X$ for
predicting outcome $\hat{Y}$. This process is explained in Section 3.2. We
hypothesize that the dataset is sufficiently large and the model can learn
which factors are associated with which outcomes. This assumption especially
holds for scenarios in which a decision-maker (e.g. an inspector or judge) is
required to go through a standard set of criteria (e.g. a standard form or set
of guidelines) and thus, the same arguments may repeatedly be articulated in
different ways to justify a certain outcome. Sentences with the highest
attribution score can be incorporated as candidates to be included in the
summary. However, some of these high-utility arguments may be unfairly used to
explain decisions for certain protected groups. We explain how we mitigate
this problem in Section 3.

Discrimination Control:  To ensure that sentences in input explanation $X$
that are biased toward certain protected groups are excluded from summary
$X^{\prime}$, we attribute a discrimination score to each sentence in $X$.
Discrimination is defined as the utility of an argument in identifying the
membership of an individual $i$ in the protected group $P_{i}$. We use
justification $X$ to predict protected attribute $P$. Next, we use the trained
model to derive the contribution of each sentence in the membership
identification task. Sentences with the highest discrimination score should
not be selected for inclusion in summary $X^{\prime}$. We train a multi-task
model for decision classification and membership identification tasks. Next,
we explain our model architecture.

###  3.1 Model Architecture

Prior research has exploited word embeddings and Convolutional Neural Networks
(CNN) for sentence classification task [20, 42, 37, 40, 79, 36]. Kim [49]
achieved strong empirical performance using static vectors and little hyper-
parameter tuning over a range of benchmarks. Variations of this architecture
have achieved good performance for extractive summarization of privacy
policies [47] and court cases [80]. CNNs are fast to train and can easily be
combined with methods such as Integrated Gradients [67] for attributing
predictions to sentences in the explanations. These considerations led to our
decision to use a slight variant of the sentence-ngram CNN model in [80] for
decision outcome prediction and membership identification tasks. Next, we will
explain the architecture of this model.

Given explanation $X_{i}$ consisting of $m$ sentences/arguments
$\\{s_{1},....s_{m}\\}$ to justify decision $Y_{i}$ for individual $i$, we use
Universal Sentence Encoder [17] to encode each sentence $s_{j}$ to a
512-dimensional embedding vector $v_{j}$. We build the justification matrix
$A\in R^{m\times 512}$ by concatenating the sentence vectors $v_{1}$ to
$v_{m}$:

$A_{1:m}=v_{1}\oplus v_{2}\oplus...v_{m}$

The Sentence Encoder is pre-trained using a variety of data sources and tasks
[17] using the Transformer [71] architecture and is obtained from Tensorflow
Hub 444https://tfhub.dev/google/universal-sentence-encoder/4. Following [20]
we apply convolution filters to windows of sentences in explanation $X_{i}$ to
capture compounded and higher-order features. We use multiple filter sizes to
capture various features from sentence n-grams. We use filter sizes of
$h\times d$ where $h$ is the height or region size of the filter and indicates
the number of sentences that are considered jointly when applying the
convolution filter. $d$ is the dimensionality of the sentence vectors and is
equal to 512. The feature map $c\in R^{m-h+1}$ of the convolution operation is
then obtained by repeatedly applying the convolution filter $w$ to a window of
sentences $s_{j:j+h-1}$. Each element $c_{j}$ in feature map
$c=[c_{1},c_{2},...c_{m-h+1}]$ is then obtained from:

$c_{i}=f(w~{}.~{}A[j:j+h-1]+b)$

where $A[j:k]$ is the sub-matrix of $A$ from row $j$ to $k$ corresponding to a
window of sentence $s_{j}$ to $s_{k}$ and "." represents the dot product
between the filter $w$ and the sub-matrices. $b\in R$ represents the bias term
and $f$ is an activation function such as a rectified linear unit. We use
window sizes 2, 3, and 4 and train 100 filters for each window size. The
dimensionality of the feature map $c$ generated by each convolution filter is
different for explanations with various lengths and filters with different
heights. We apply an average-max pooling operation over the feature maps of
each window size to downsample them by taking the average value over the
window defined by a pool size. Next, we concatenate the output vectors.
Eventually, the concatenated vector runs through a dense layer with 64 units
followed by an activation function 555For classification tasks we used softmax
(multi-class) or Sigmoid (binary classes) functions. For scalar outputs, we
used Rectified Linear Unit..

This is a multi-task model with a decision learner and membership identifier
modules. The decision learner is trained using decision outcome $Y$ as
supervision and the membership identifier is trained using the protected
attribute $P$. For instance, $Y_{i}$ could represent an inspection outcome
(e.g. fail, pass, or conditional pass) for establishment $i$ which is owned by
an individual who is a member of a demographic group $P_{i}$ (e.g. a racial
group). In our setup, the loss at each epoch is computed based on a weighted
sum of the decision prediction and membership identification losses. Training
details are explained in Section 4.2. Next, we explain the method we use for
attributing the predictions $\hat{Y}$ and $\hat{P}$ of the model to arguments
proposed in justification $X$.

###  3.2 Attribution

Sundararajan et al [67] proposed a method called Integrated Gradients to
attribute predictions of a deep neural network to its input features. This
method is independent of the specific neural architecture and can provide a
measure of relevance for each feature by quantifying its impact on the outcome
of the prediction. Zhong et al [80] adopted this method for identifying most
decision-relevant aspects of legal cases. We also utilize this method to
measure the impact of each input sentence in decision prediction and the
membership identification tasks. Essentially we take a straight line path from
an input $x$ to its baseline $b$ 666Conceptually, baselines represent data
points that do not contain any useful information for the model. They are used
as a benchmark by the integrated gradients method. Sundararajan et al [67]
suggest using an all-zero input embedding vector for text-based networks. and
notice how model prediction changes along this path by integrating the
gradients along the path. To approximate the integral of the integrated
gradients, we simply sum up the gradients at points occurring at small
intervals along the straight-line path from the baseline to the input. The
resulting single scalar represents the gradients and attributes the prediction
to input features. The integrated gradient along the $i$-th dimension for an
input $x$ and baseline $x^{\prime}$ is defined as follows:

$IG_{i}(x)::=(x_{i}-b_{i})\times\sum_{k=1}^{m}\frac{\partial{F(b+\frac{k}{m}\times(x-b))}}{\partial
x_{i}}\times\frac{1}{m}$

Here, $F:X\rightarrow Y$ represents the neural model, $\frac{\partial
F(x)}{\partial x_{i}}$ is the gradient of F(X) along the $i$-th dimension, $x$
represents the input at hand, $b$ represents the baseline input( an all-zero
vector), and $m$ is the number of steps in the approximation of the integral.
To obtain utility attributions $U=\\{u_{1},u_{2},...u_{m}\\}$ for sentences
$\\{s_{1},s_{2},...,s_{m}\\}$ in input justification $X_{i}$ we calculate the
integrated gradients attributions for the model using the predicted decision
outcome $\hat{Y}$. Note that each input feature is one dimension of sentence
embedding. To obtain salience scores for each sentence, we sum up the
attribution scores for each dimension. Next, we run $U$ through a softmax
function to get a utility distribution over the sentences. Similarly, we
obtain discrimination attributions $D=\\{d_{1},d_{2},...d_{m}\\}$ for
sentences $\\{s_{1},s_{2},...,s_{m}\\}$ by calculating the integrated
gradients attributions for the model using the predicted protected attribute
$\hat{P}$. We run $D$ through a softmax function to get a discrimination
distribution over the sentences.

Figure 2: An overview of the architecture: Decision learner and membership
identifier are trained using decision $Y$ and protected attribute $P$ as
supervision respectively.The attributions of each module is normalized and
subtracted to obtain the inclusion scores.

We include high-utility and discrimination-free sentences in the fairly-
justified summary of the explanations. To do so, we subtract the
discrimination attribution $d_{i}$ from the utility attributions $u_{i}$ to
get the final inclusion score $a_{i}$ for each sentence $s_{i}$. Essentially,
the inclusion score $a_{i}$ is computed using the following equation:

$a_{i}=\sigma(u)_{i}-\alpha\times\sigma(d)_{i}$

In the equation above, $\alpha$ is a hyper-parameter that controls the
utility-discrimination trade-off. Higher values of $\alpha$ correspond to
removing more information about protected attributes from the input
justifications. Figure 2 shows the attribution process. If an argument $s_{j}$
is used unfairly to justify an outcome for individuals in a certain protected
group, it will get a high utility attribution $u_{j}$ and high discrimination
attribution $d_{j}$ . The subtraction operation ensures that it gets a small
inclusion score $a_{j}$.

Extracting Fairly-Justified Summarizes:  Given sentences
$\\{s_{1},s_{2},...,s_{m}\\}$ and the corresponding inclusion scores
$\\{a_{1},....a_{m}\\}$, we select sentences with positive scores to be
included in the fairly-justified summary. As explained in Section 3.2
sentences with a positive score $a_{i}$ have high utility for decision
prediction but do not reveal the protected attribute of the individuals. In
our experiments, we test whether training a decision classifier on the fairly-
justified summaries will enhance fairness in the outcome on real-world and
synthetic datasets.

##  4 Experiments and Results

In this section, we introduce the datasets we use for training and testing our
model followed by training details, experimental setup, and metrics in
consideration.

###  4.1 Datasets

Inspection Reports of food establishments in Chicago (D1): The City of Chicago
has published reports of food inspections conducted since 2010. We extracted
the information on food inspections conducted from January 2010 till December
2014 from the City of Chicago’s GitHub repository
777https://github.com/Chicago/food-inspections-evaluation. This dataset
contains the outcome of inspection which can be pass, fail, or conditional
pass as well as notes that the sanitarian left in the inspection form about
the observed violations in order to justify the outcome and explain what needs
to be fixed before the next inspection 888There could be other outcomes e.g.
when the sanitarian could not access the establishment. These cases are
excluded from our study. We also excluded the inspection reports with comments
section shorter than 12 sentences.. This dataset does not include the
demographic information of the food establishment owners. As a proxy, we use
the ethnicity of the majority of the population in the census block group that
the food establishment is located at 999The demographic information of
neighborhoods were extracted from https://www.census.gov/. The resulting
dataset includes 17,212 inspection reports. The inspector comments are on
average 18.2 sentences long with a standard deviation of 7.2. The breakdown of
the inspection outcome for each demographic group is shown in Table 4 of the
supplementary material A.1. We train the decision classifier explained in
Section 3.1 on inspector notes using inspection outcome and ethnicity as
supervision for decision classifier and membership identifier respectively.

Rate My Professor (D2-D4): Students can leave an anonymous review and rating
on the scale of 1-5 in several categories for their previous instructors on
the Rate My Professor (RMP) website 101010https://www.ratemyprofessors.com/.
Several previous studies have identified several types of biases in students’
evaluations [53, 62, 19, 9, 63, 69]. In our study, we aim to detect and remove
potential biases in justifications provided by students to explain their
ratings using our proposed methodology. We rely on the dataset collected in
[34]. We combine all the reviews written for each instructor and use the
average rating as the supervision for the decision classifier. We use the
gender of the instructor as the supervision for the membership identifier
model. In our experiments, we exclude the instructors that have less than 5
reviews. We also remove the pronouns and instructors’ names from the
reviews111111This pre-processing step ensures that the membership identifier
does not rely on blatant signals from the text and instead extracts more
latent patterns in the justifications. . The resulting dataset includes
reviews written for 1344 instructors which are on average 45.6 sentences long.
We indicate this dataset with D2. We create two additional datasets D3 and D4
by splitting the RMP dataset based on the gender gap of the students in each
discipline. D3 includes student evaluations for professors in fields that are
female-dominant such as nursing, psychology, and education while D4 includes
student evaluations for male-dominant majors such as engineering, computer
science, and philosophy 121212Fields with less than 20% gender gap are
excluded. The statistics about the bachelor’s degrees earned by field and
gender is obtained from [59] . The breakdown of ratings for each gender group
for D2-D4 is shown in Appendix A.1.

###  4.2 Hyper-parameters and Training Details

Training Details:  To train the model introduced in 3.1 on D1, We employ
window sizes of 2, 3 and 4, and train 100 filters for each window size. For
smaller datasets D2-D4. we use window sizes 2 and 3 and train 50 filters for
each window size. We initialize each convolution layer using the
initialization method proposed in [35]. We use rectified linear unit as the
activation function of the convolution layer. After performing the convolution
operation, we apply batch normalization [39] followed by a global average-
pooling operation over the feature map of each window size. Next, we
concatenate the output vectors. Eventually, we run the concatenated vector
through a dense layer with 64 units followed by an activation function. For
decision classification and membership identification on D1, we used the
softmax operation to obtain class probabilities. For D2-D4 we used rectified
linear unit to obtain the output rating, and sigmoid to obtain gender class
probabilities. We implement the decision classifier and member identifier
networks using the Keras library 131313https://keras.io. We use weighted
cross-entropy loss function for classification tasks and mean squared loss for
regression tasks and learn the model parameters using Adam optimizer [50] with
a learning rate of 0.001.

For D1, we use 90% of inspections conducted from January 2010 till October
2013 (75% all records in our data-set) as our training set and the remaining
10% as our validation set. The inspections conducted from November 2013 till
December 2014 are used as our test set. We set the maximum length of the
arguments to the 70-th percentile of explanation lengths in our train set (18
sentences). Textual explanations that are longer than this are truncated while
shorter ones are padded. For D2-D4, we randomly split our dataset to a
70-15-15 split to build our train, validation, and test sets. We set the
maximum length of the arguments to the 70-th percentile of the review length
in our train set (64 sentences). Reviews that are longer than this are
truncated while shorter ones are padded. We set the loss weight for the
decision prediction task and the membership identification task to 1. We train
our multi-task network for a maximum of 25 epochs and stop the training if the
decision classification loss on the validation set does not improve for 3
consecutive epochs. In the end, we revert the network’s weights to those that
achieved the lowest validation loss. We repeat each experiment 5 times and
report the average result. We used Nvidia Tesla K80 GPUs for our experiments.

Parameters of the attribution Model: For computing the integrated gradients
for attribution, we set the number of steps in the path integral approximation
from the baseline to the input instance to 50 and use Gauss–Legendre
quadrature method for integral approximation. We compute the attributions of
the decision classifier and the membership identification networks for the
input layer.

###  4.3 Evaluation Metrics

In our experiments we seek to answer the following questions: (i) How does
fairness-aware summarization of the input justification affect the utility of
the model? (ii) Will this pre-processing step help in defending against
membership identification attacks? and (iii) Will enhancing the fairness in
the justification using our proposed approach also enhances the fairness in
the outcome? To answer the first question, we report the utility of the
decision learner. For categorical outcomes (e.g. in D1 and D1′) we report the
Micro-F1 and Macro-F1 and for scalar outcomes (D2-D4) we report the Mean
Absolute Error(MAE). To answer the second question, we report the demographic
leakage. Leakage is defined as the ability of the membership identifier
network to correctly predict the protected attribute of the individuals given
the justification. We report the Micro-F1 and Macro-F1 of our membership
identification model. Lower demographic leakage is desirable. Lastly, to
answer the question regarding the fairness of the model for categorical
outcomes we report the demographic parity, equality of odds, and calibration
141414For a discussion on fairness measures and their trade-offs see [51] and
[33].. We additionally report False Pass Rate Gap (FPRG) and False Fail Rate
Gap (FFRG) across demographic groups. FPRG and FFRG represent the equality in
distribution of the model errors across demographic groups. Similar metrics
were used in [72]. To measure fairness for scalar outcomes, we report Mean
Absolute Error GAP. Next, we define the fairness measures in the context of
food inspection.

Parity: a decision classifier satisfies demographic parity if the proportion
of food establishments predicted to fail the inspection is the same for each
demographic. We report the gap between the most and least favored groups. For
sake of consistency with previous work, we present the protected attribute
with S.

$\max(P(\hat{Y}=fail|S=s_{i})-P(\hat{Y}=fail|S=s_{j}))=\epsilon,~{}~{}~{}~{}~{}~{}~{}s_{i},s_{j}\in
S$

Equality of odds: for those establishments who actually failed the inspection,
the proportion of failed predictions should be the same. We report the gap
between the most and least favored groups. Ideally, the gap should be very
close to zero.

$\max(P(\hat{Y}=fail|Y=fail,S=s_{i})-P(\hat{Y}=fail|Y=fail,S=s_{j}))=\epsilon,~{}~{}~{}~{}~{}~{}~{}s_{i},s_{j}\in
S$

Calibration:  for those establishments who received a fail prediction, the
probability of actually failing the inspection should be the same. We report
the gap between the most and least favored groups. Ideally, the gap should be
very close to zero.

$\max(P(Y=fail|\hat{Y}=fail,S=s_{i})-P(Y=fail|\hat{Y}=fail,S=s_{j}))=\epsilon,~{}~{}~{}~{}~{}~{}~{}s_{i},s_{j}\in
S$

False Pass Rate Gap(FPRG):  food establishments that did not pass the
inspection should have the same probability of falsely receiving a pass
prediction. We report the gap between the most and least favored groups which
ideally should be close to 0.

$\max(P(\hat{Y}=pass|Y\neq pass,S=s_{i})-P(\hat{Y}=pass|Y\neq
pass,S=s_{j}))=\epsilon,~{}~{}~{}~{}~{}~{}~{}s_{i},s_{j}\in S$

False Fail Rate Gap(FFRG): establishments of different demographic groups that
did not fail the inspection should have the same probability of falsely
receiving a fail prediction. We report the gap between the most and least
favored groups which ideally should be close to 0.

$\max(P(\hat{Y}=fail|Y\neq fail,S=s_{i})-P(\hat{Y}=fail|Y\neq
fail,S=s_{j}))=\epsilon,~{}~{}~{}~{}~{}~{}~{}s_{i},s_{j}\in S$

###  4.4 Results and Discussion

Dataset |  | Utility
---
(Micro-F1)
| Utility
---
(Macro-F1)
| Demographic
---
Leakage
(Micro-F1)
| Demographic
---
Leakage
(Macro-F1)
| Empty | Full | FairSum | Empty | Full | FairSum | Empty | Full | FairSum | Empty | Full | FairSum
D1 | 0.48 | 0.83 | 0.83 | 0.22 | 0.83 | 0.82 | 0.56 | 0.58 | 0.52 | 0.18 | 0.38 | 0.33
D1′ | 0.48 | 0.8 | 0.8 | 0.26 | 0.79 | 0.79 | 0.78 | 0.66 | 0.56 | 0.66 | 0.48 | 0.36

Table 1: Results on datasets D1 and D1′ (which is built by adding unfairness
attacks to D1). In the "Empty" setting, justifications are removed (in D1′ \-
the "Empty" setting includes the address in the justification). "Full" refers
to the model trained on all justifications while FairSum refers to the model
trained on fair summaries obtained by our proposed pre-processing approach.

Dataset | Parity |  | Equality
---
of Odds
Calibration | FPRG | FFRG
| Full | FairSum | Full | FairSum | Full | FairSum | Full | FairSum | Full | FairSum
D1 | 0.15 | 0.14 | 0.08 | 0.1 | 0.05 | 0.06 | 0.05 | 0.05 | 0.11 | 0.11
D1′ | 0.12 | 0.12 | 0.06 | 0.07 | 0.07 | 0.05 | 0.03 | 0.04 | 0.09 | 0.07

Table 2: Fairness metrics for datasets D1 and D1′.

Dataset | MAE |  | Demographic
---
Leakage
(Micro-F1)
| Demographic
---
Leakage
(Macro-F1)
MAE Gap
| Empty | Full | FairSum | Empty | Full | FairSum | Empty | Full | FairSum | Empty | Full | FairSum
D2 | 0.72 | 0.47 | 0.49 | 0.59 | 0.71 | 0.61 | 0.37 | 0.69 | 0.58 | 0.07 | 0.06 | 0
D3 | 0.76 | 0.52 | 0.53 | 0.5 | 0.66 | 0.61 | 0.33 | 0.66 | 0.59 | 0.19 | 0.03 | 0.06
D4 | 0.66 | 0.54 | 0.53 | 0.45 | 0.82 | 0.74 | 0.3 | 0.71 | 0.49 | 0.04 | 0.02 | 0

Table 3: Results on RMP Datasets (D2-D4)

In our experiments, we compare the utility, demographic leakage, and fairness
of models that are identical in terms of architecture but are trained on
different versions of the training data. The model architecture is discussed
in Section 3.1. In the "Empty" setting, justifications are empty. In the
"Full" setting, the model is trained and tested on the original data while in
the "FairSum" setting it is trained and tested on the fairly-justified
summaries. We use to empty setting to indicate the lower bound of the
demographic leakage. We use the full setting, to measure the bias in the
justifications in the input dataset. This setting also acts as our baseline.
The FairSum setting represents the effectiveness of our proposed approach in
mitigating the bias in the justification. To extract the fairly-justified
summaries, we train the multi-task CNN model on justifications using decision
outcome as supervision for one task and the demographic information as
supervision for the other. We measure the importance of each sentence in the
justifications using the attribution mechanism explained in Section 3.2. Next,
we include sentences that have a positive inclusion score in the fairly
justified summaries. These sentences have a high utility for decision outcome
prediction and a low utility for membership identification. We apply this
preprocessing step on both the train and test sets. The results are presented
in Tables 1, 2, and 3. For these experiments, the parameter $\alpha$ which
controls the trade-off between the utility and the demographic leakage is set
to 1.

As it can be seen in Table 1, FairSum reduces the demographic leakage on
dataset D1 (by 0.06 in Micro-F1 and 0.05 in Macro-F1) while achieving the same
level of accuracy on the decision classification task in comparison to the
full setting. As it is indicated in Table 2, our approach decreases parity by
0.01 while achieving similar results in terms of FFRG and FPRG. We also test
the impact of unfairness attacks on this dataset which will be discussed in
the next section. As indicated in Table 3, in dataset D2, FairSum decreases
the demographic leakage from 0.71 to 0.61 Micro-F1 and 0.69 to 0.58 Macro-F1
while increasing the MAE by 0.02 in a 5-point scale. FairSum outcomes also are
more fair on D2. In the full setting, predictions have 0.06 higher average MAE
for females than males. While FairSum achieves similar error rates for both
gender groups (0 MAE gap).

For D3 and D4, fairSum reduces the demographic leakage (from 0.66 to 0.59 and
0.71 to 0.49 Macro-F1 respectively). We conclude that our proposed approach is
very effective in reducing the demographic-leakage in the input justifications
(-11.3% and -19.1% decrease on Micro-F1 and Macro-F1 on average over datasets
D1-D4 and D1′) while also not reducing the utility of the model. Removing
gender-coded language from D3 justifications comes with the cost of having
0.06 higher MAE for females than males (this was 0.03 for the full setting).
On D2 and D4 however, FairSum completely closes the MAE gap between the gender
groups. An example of applying FairSum on a teaching evaluation for a female
professor is shown in Figure 3. As it can be seen in the figure, arguments
about the looks of the instructor (more frequent for female instructors) are
assigned a low inclusion score (indicated with red shade) and therefore
excluded from the summary. The preserved sentences are indicated with a green
shade and have a high inclusion score. Our model increases the fairness in the
outcome moderately for D1, D2, and D4 but when the ground-truth rating is
correlated with the protected attribute (e.g. in more biased dataset D3 with
0.07 correlation) it’s effectiveness is limited. Therefore, in addition to
perturbing the justifications using a filtering-based approach more
interventions is required to enhance the fairness in the outcome. This can be
in form of adding justifications rather than removing or imposing the fairness
constraints on the model itself rather than data.

Figure 3: An example of using FairSum on teaching evaluations for a female
professor. The colors indicate the inclusion score for each sentences.
Sentences with a positive score (represented with green shade) are preserved
in the summary while the rest are excluded.

Utility-Fairness Trade-Off:  Figure 4 shows the utility, demographic leakage,
and fairness metrics as a function of $\alpha$ on D1 and D2. Too low values of
$\alpha$ prioritize utility, selecting even relatively biased sentences and
have scores close to the full setting (see Figure 4a and 4c). On D1,
increasing $\alpha$ generally decreases the demographic parity while
increasing the FPRG (see Figure 4b). It does not have a consistent or
noticeable impact on other fairness metrics. On D2 and with $\alpha$ near 1,
the gap shrinks to 0 (See Figure 4c). Too high values of $\alpha$ remove too
many sentences, leading to high error rate. This is because many summaries are
empty with high value for $\alpha$ and thus, the resulting decision are
unjustified (justifications are not informative about the outcomes) and unfair
(the lack of justification is not uniformly distributed over genders) so the
gap emerge once again. For error bars and more details on impact of $\alpha$
on summary length see Section A.3 and A.2 in the Appendix.

(a)

(b)

(c)

Figure 4: Impact of $\alpha$ on utility and fairness on datasets D1 (a and b)
and D2 (c).

Unfairness Attacks:  Next, we test the effectiveness of our proposed method in
a scenario where unfair arguments are injected into the justifications.
Chicago is one of the most segregated cities in the US [21]. Thus, the address
of the food establishment includes "proxy" information about the demography of
the neighborhood. In this experiment, we add the address of the food
establishment to the input justification of dataset D1 151515positions are
randomized.. This dataset is represented with D1′ in Table 1. In the empty
setting for D1′ the input to the model only includes the address and no other
justifications for the decision. The full setting includes all the
justifications as well as the address. It can be seen that the address indeed
includes some proxy information about the race as the empty setting has a high
demographic leakage (0.78 Micro-F1 and 0.66 Macro-F1). We also observe that
fairSum effectively decreases the demographic leakage on D1′ (-0.10 in
Micro-F1 and -0.12 in Macro-F1 in comparison to the full setting) while
achieving the same level of accuracy. FairSum has moderate improvements in
terms of fairness over the full setting; decreasing the calibration and FFRG
by 0.02 points.

##  5 Conclusion and Future Work

In this work, we propose using a train-attribute-mask pipeline for detecting
and mitigating the bias in the justification of the text-based neural models.
Our objective for extracting fairly-justified summaries is to maximize the
utility of the output summary for the decision prediction task while
minimizing the inclusion of proxy information in the summary that can reveal
sensitive attributes of individuals. Our approach is not intended to enhance
the fairness in the outcome but rather to enhance the fairness in the model
justification. We achieve this by training a multi-task model for decision
classification and membership identification. We attribute predictions of
these models back to textual input attributes using an attribution mechanism
called integrated gradients. Next, we incorporate the high-utility and bias-
free sentences in form of a summary. Eventually, we retrain the decision
classifier on the fairly-justified summaries. Our experiments on real and
synthetic data sets indicate that our pipeline effectively limits the
demographic leakage from the input data. In addition, it moderately enhances
the fairness in the outcome. We plan to test the impact of our proposed
pipeline in removing bias from the data using more data sets and on several
synthetic scenarios in which a subgroup of the population is treated
differently.

## Acknowledgments and Disclosure of Funding

This material is based upon work supported by the National Science Foundation
(NSF) under award number 1939743. Any opinions, findings, and conclusions in
this material are those of the authors and may not reflect the views of the
respective funding agency.

## References

  * [1] Civil Rights Act.  Civil rights act of 1964.  Title VII, Equal Employment Opportunities, 1964.
  * [2] Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach.  A reductions approach to fair classification.  In ICML. PMLR, 2018.
  * [3] Sina Aghaei, Mohammad Javad Azizi, and Phebe Vayanos.  Learning optimal and fair decision trees for non-discriminative decision-making.  In AAAI, 2019.
  * [4] Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner.  Machine bias.  ProPublica, May, 2016.
  * [5] Anna Maria Barry-jester, Ben Casselman, and Dana Goldstein.  The new science of sentencing, Aug 2015.
  * [6] Yahav Bechavod and Katrina Ligett.  Penalizing unfairness in binary classification.  arXiv preprint arXiv:1707.00044, 2017.
  * [7] Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth.  A convex framework for fair regression.  arXiv preprint arXiv:1706.02409, 2017.
  * [8] Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth.  Fairness in criminal justice risk assessments: The state of the art.  Sociological Methods & Research, 2018.
  * [9] April Bleske-Rechek and Kelsey Michels.  Ratemyprofessors com: Testing assumptions about student use and misuse.  Practical Assessment, Research, and Evaluation, 15(1):5, 2010.
  * [10] Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai.  Man is to computer programmer as woman is to homemaker? debiasing word embeddings.  arXiv preprint arXiv:1607.06520, 2016.
  * [11] Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy.  Building classifiers with independency constraints.  In 2009 IEEE ICDM Workshops. IEEE, 2009.
  * [12] Toon Calders and Sicco Verwer.  Three naive bayes approaches for discrimination-free classification.  Data Mining and Knowledge Discovery, 2010.
  * [13] Flavio P Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney.  Optimized pre-processing for discrimination prevention.  In Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017.
  * [14] Diogo V Carvalho, Eduardo M Pereira, and Jaime S Cardoso.  Machine learning interpretability: A survey on methods and metrics.  Electronics, page 832, 2019.
  * [15] L Elisa Celis, Lingxiao Huang, Vijay Keswani, and Nisheeth K Vishnoi.  Classification with fairness constraints: A meta-algorithm with provable guarantees.  In Proceedings of the conference on fairness, accountability, and transparency, 2019.
  * [16] L Elisa Celis and Vijay Keswani.  Improved adversarial learning for fair classification.  arXiv preprint arXiv:1901.10443, 2019.
  * [17] Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Céspedes, Steve Yuan, Chris Tar, et al.  Universal sentence encoder.  arXiv preprint arXiv:1803.11175, 2018.
  * [18] Alexandra Chouldechova and Aaron Roth.  The frontiers of fairness in machine learning.  arXiv preprint arXiv:1810.08810, 2018.
  * [19] Dennis E Clayson.  What does ratemyprofessors. com actually rate?  Assessment & Evaluation in Higher Education, 39(6):678–698, 2014\.
  * [20] Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa.  Natural language processing (almost) from scratch.  JMLR, 2011.
  * [21] Evan Comen.  Detroit, chicago, memphis the 25 most segregated cities in america, Jul 2019.
  * [22] Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, and Seungil You.  Training well-generalizing classifiers for fairness metrics and other data-dependent constraints.  In ICML. PMLR, 2019.
  * [23] Eustasio Del Barrio, Fabrice Gamboa, Paula Gordaliza, and Jean-Michel Loubes.  Obtaining fairness using optimal transport theory.  arXiv preprint arXiv:1806.03195, 2018.
  * [24] Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, and Massimiliano Pontil.  Empirical risk minimization under fairness constraints.  arXiv preprint arXiv:1802.08626, 2018.
  * [25] Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson.  Decoupled classifiers for group-fair and efficient machine learning.  In Conference on Fairness, Accountability and Transparency. PMLR, 2018.
  * [26] Fahimeh Ebrahimi, Miroslav Tushev, and Anas Mahmoud.  Mobile app privacy in software engineering research: A systematic mapping study.  Information and Software Technology, 2020.
  * [27] Harrison Edwards and Amos Storkey.  Censoring representations with an adversary.  arXiv preprint arXiv:1511.05897, 2015.
  * [28] Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian.  Certifying and removing disparate impact.  In ACM SIGKDD, 2015.
  * [29] Benjamin Fish, Jeremy Kun, and Ádám D Lelkes.  A confidence-based approach for balancing fairness and accuracy.  In Proceedings of the 2016 SIAM International Conference on Data Mining. SIAM, 2016.
  * [30] Sorelle A Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P Hamilton, and Derek Roth.  A comparative study of fairness-enhancing interventions in machine learning.  In Proceedings of the conference on fairness, accountability, and transparency, 2019.
  * [31] AmirEmad Ghassami, Sajad Khodadadian, and Negar Kiyavash.  Fairness in supervised learning: An information theoretic approach.  In 2018 IEEE International Symposium on Information Theory (ISIT), pages 176–180. IEEE, 2018.
  * [32] Naman Goel, Mohammad Yaghini, and Boi Faltings.  Non-discriminatory machine learning through convex fairness criteria.  In AAAI, 2018.
  * [33] Moritz Hardt, Eric Price, and Nathan Srebro.  Equality of opportunity in supervised learning.  arXiv preprint arXiv:1610.02413, 2016.
  * [34] Jibo He.  Big data set from ratemyprofessor.com for professors’ teaching evaluation, 2020.
  * [35] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.  Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.  In Proceedings of the IEEE international conference on computer vision, 2015.
  * [36] Maryam Heidari and Setareh Rafatirad.  Semantic convolutional neural network model for safe business investment by using bert.  In International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2020.
  * [37] Maryam Heidari and Setareh Rafatirad.  Using transfer learning approach to implement convolutional neural network model to recommend airline tickets by using online reviews.  In International Workshop on Semantic and Social Media Adaptation and Personalization (SMA. IEEE, 2020.
  * [38] Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudik, and Hanna Wallach.  Improving fairness in machine learning systems: What do industry practitioners need?  In Proceedings of the 2019 CHI conference on human factors in computing systems, 2019.
  * [39] Sergey Ioffe and Christian Szegedy.  Batch normalization: Accelerating deep network training by reducing internal covariate shift.  In International conference on machine learning. PMLR, 2015.
  * [40] Fereshteh Jafariakinabad and Kien A Hua.  Style-aware neural model with application in authorship attribution.  In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019.
  * [41] Mimansa Jaiswal and Emily Mower Provost.  Privacy enhanced multimodal neural representations for emotion recognition.  In AAAI, 2020.
  * [42] Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom.  A convolutional neural network for modelling sentences.  arXiv preprint arXiv:1404.2188, 2014.
  * [43] Faisal Kamiran, Toon Calders, and Mykola Pechenizkiy.  Discrimination aware decision tree learning.  In 2010 IEEE ICDM. IEEE, 2010.
  * [44] Faisal Kamiran and Indrė Žliobaitė.  Explainable and non-explainable discrimination in classification.  In Discrimination and Privacy in the Information Society. Springer, 2013.
  * [45] Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma.  Fairness-aware classifier with prejudice remover regularizer.  In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2012.
  * [46] Ehsan Kazemi, Morteza Zadimoghaddam, and Amin Karbasi.  Scalable deletion-robust submodular maximization: Data summarization with privacy and fairness constraints.  In ICML. PMLR, 2018.
  * [47] Moniba Keymanesh, Micha Elsner, and Srinivasan Parthasarathy.  Toward domain-guided controllable summarization of privacy policies.  Natural Legal Language Processing Workshop at KDD, 2020.
  * [48] Moniba Keymanesh, Saket Gurukar, Bethany Boettner, Christopher Browning, Catherine Calder, and Srinivasan Parthasarathy.  Twitter watch: Leveraging social media to monitor and predict collective-efficacy of neighborhoods.  Complex Networks XI. Springer Proceedings in Complexity, 2020.
  * [49] Yoon Kim.  Convolutional neural networks for sentence classification.  arXiv:1408.5882, 2014.
  * [50] Diederik P Kingma and Jimmy Ba.  Adam: A method for stochastic optimization.  arXiv preprint arXiv:1412.6980, 2014.
  * [51] Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan.  Inherent trade-offs in the fair determination of risk scores.  arXiv preprint arXiv:1609.05807, 2016.
  * [52] James Koren.  What does that web search say about your credit?, Sep 2016.
  * [53] Angela M Legg and Janie H Wilson.  Ratemyprofessors. com offers biased evaluations.  Assessment & Evaluation in Higher Education, 37(1):89–97, 2012\.
  * [54] David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel.  Learning adversarially fair and transferable representations.  In International Conference on Machine Learning, pages 3384–3393. PMLR, 2018.
  * [55] Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan.  A survey on bias and fairness in machine learning.  arXiv preprint arXiv:1908.09635, 2019.
  * [56] Aditya Krishna Menon and Robert C Williamson.  The cost of fairness in binary classification.  In Conference on Fairness, Accountability and Transparency, pages 107–118. PMLR, 2018.
  * [57] Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kıcıman.  Social data: Biases, methodological pitfalls, and ethical boundaries.  Frontiers in Big Data, 2:13, 2019.
  * [58] Osonde A Osoba and William Welser IV.  An intelligence in our image: The risks of bias and errors in artificial intelligence.  Rand Corporation, 2017.
  * [59] Mark J. Perry.  Bachelors degrees by field and gender for the class of 2015, Aug 2017\.
  * [60] Dana Pessach and Erez Shmueli.  Algorithmic fairness.  arXiv preprint arXiv:2001.09784, 2020.
  * [61] Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Eric Rice, and Milind Tambe.  Fair influence maximization: A welfare optimization approach.  arXiv preprint arXiv:2006.07906, 2020.
  * [62] Landon D Reid.  The role of perceived race and gender in the evaluation of college teaching on ratemyprofessors. com.  Journal of Diversity in higher Education, 3(3):137, 2010.
  * [63] Andrew S Rosen.  Correlations, trends and potential biases among publicly accessible web-based student evaluations of teaching: a large-scale study of ratemyprofessors. com data.  Assessment & Evaluation in Higher Education, 43(1):31–44, 2018\.
  * [64] Cynthia Rudin.  Predictive policing using machine learning to detect patterns of crime.  Wired Magazine, August, 2013.
  * [65] Ritesh Sarkhel, Moniba Keymanesh, Arnab Nandi, and Srinivasan Parthasarathy.  Interpretable multi-headed attention for abstractive summarization at controllable lengths.  In Proceedings of the 28th International Conference on Computational Linguistics, 2020.
  * [66] Emily Steel and Julia Angwin.  On the web’s cutting edge, anonymity in name only, Aug 2010.
  * [67] Mukund Sundararajan, Ankur Taly, and Qiqi Yan.  Axiomatic attribution for deep networks.  In International Conference on Machine Learning, pages 3319–3328. PMLR, 2017.
  * [68] Harini Suresh and John V Guttag.  A framework for understanding unintended consequences of machine learning.  arXiv preprint arXiv:1901.10002, 2019.
  * [69] Katherine C Theyson.  Hot or not: The role of instructor quality and gender on the formation of positive illusions among students using ratemyprofessors. com.  Practical Assessment, Research, and Evaluation, 20(1):4, 2015.
  * [70] Miroslav Tushev, Fahimeh Ebrahimi, and Anas Mahmoud.  Digital discrimination in sharing economy a requirements engineering perspective.  In 2020 IEEE 28th International Requirements Engineering Conference (RE). IEEE, 2020.
  * [71] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin.  Attention is all you need.  In NIPS, 2017.
  * [72] Christina Wadsworth, Francesca Vera, and Chris Piech.  Achieving fairness through adversarial learning: an application to recidivism prediction.  arXiv preprint arXiv:1807.00199, 2018.
  * [73] Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu.  Fairgan: Fairness-aware generative adversarial networks.  In 2018 IEEE Big Data. IEEE, 2018.
  * [74] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi.  Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment.  In International conference on world wide web, 2017.
  * [75] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P Gummadi, and Adrian Weller.  From parity to preference-based notions of fairness in classification.  arXiv preprint arXiv:1707.00010, 2017.
  * [76] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi.  Fairness constraints: Mechanisms for fair classification.  In Artificial Intelligence and Statistics, pages 962–970. PMLR, 2017.
  * [77] Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork.  Learning fair representations.  In International conference on machine learning, pages 325–333. PMLR, 2013.
  * [78] Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell.  Mitigating unwanted biases with adversarial learning.  In AAAI Conference on AI, Ethics, and Society, 2018.
  * [79] Ye Zhang and Byron Wallace.  A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification.  arXiv:1510.03820, 2015.
  * [80] Linwu Zhong, Ziyi Zhong, Zinian Zhao, Siyuan Wang, Kevin D Ashley, and Matthias Grabmair.  Automatic summarization of legal decisions using iterative masking of predictive sentences.  In International Conference on Artificial Intelligence and Law, 2019\.

##  Appendix A Appendix

###  A.1 Dataset Statistics

Inspection reports of the city of Chicago(D1):  The breakdown of the
inspection results for each demographic group is shown in Table 4. Note that
for the food establishments that have more violation, the inspection reports
tend to be longer. In our summarization experiments, we focused on longer
inspection reports which often includes establishments with higher number of
violations.

Race | Pass | Conditional pass | Fail | Total inspection count
---|---|---|---|---
White | 27.5 | 25.6 | 46.8 | 8339
Black | 28.9 | 15.6 | 55.4 | 4444
Hispanic | 33.8 | 19.2 | 46.8 | 4010
Asian | 29.3 | 17.4 | 53.2 | 419
Table 4: The percentage of inspections for each ethnic group that received a
pass, conditional pass outcome, or fail outcome.

Rate my professor (D2-D4):  The rate my professor dataset only includes
professor names and reviews. To infer the gender of the professors, we search
for pronouns and titles commonly used for each gender161616For sake of
simplicity we assume binary gender classes. If no pronouns or titles are found
in the reviews, the professor’s name is used to detect their gender 171717We
use https://pypi.org/project/gender-detector/ for mapping professors’ names to
their gender. The breakdown of reviews written for each gender category is
shown in Tables 7, 7, and 7.

| [1,2] | (2,3] | (3,4] | (4,5] | Total count
---|---|---|---|---|---
Female | 5.6 | 21.0 | 35.3 | 37.9 | 551
Male | 3.7 | 21.0 | 35.6 | 39.5 | 783

Table 5: The percentage of instructors of each gender group in each rating
class for dataset D2.

| [1,2] | (2,3] | (3,4] | (4,5] | Total count
---|---|---|---|---|---
Female | 4.3 | 22.2 | 31.5 | 41.9 | 279
Male | 1.7 | 18.0 | 32.6 | 47.5 | 288

Table 6: The percentage of instructors of each gender group in each rating
class for dataset D3.

| [1,2] | (2,3] | (3,4] | (4,5] | Total count
---|---|---|---|---|---
Female | 5.5 | 24.4 | 39.3 | 30.7 | 127
Male | 6.3 | 24.6 | 37.9 | 31.03 | 345

Table 7: The percentage of instructors of each gender group in each rating
class for dataset D4.

###  A.2 Impact of $\alpha$ on summary length

Figure 5: Impact of $\alpha$ on summary length on datasets D1-D4.

Figure 5 shows the average summary length (sentence count) for datasets D1-D4
as a function of $\alpha$. The food inspection reports in D1 are on average
much shorter than the teaching evaluations in dataset D2 (18.2 vs 45.6
sentences). Too low values of $\alpha$ prioritize utility by preserving even
relatively biased sentences. For all datasets, the summaries start shrinking
around $\alpha$ equal to 0.85. However, for D2-D4 the compression rate is
higher. Around $\alpha$ equal to 1.25, 39.9% input justifications for dataset
D1 are empty. This number is 77.8%, 95.7%, 100% for D2, D3, and D4
respectively. We conjecture that the existence of more implicit bias for D2-D4
causes the summaries to shrink faster by increasing $\alpha$. At this point
(1.25 and higher) the resulting decisions are unjustified (justifications are
not informative about the outcomes). Therefore in Figure 4 we only show impact
of changing $\alpha$ from 0.8 to 1.2.

###  A.3 Results (Error Bars):

Figure 6 and 7 indicate the errors in utility and membership prediction over 5
runs for datasets D1 and D2. For the FairSum setting the parameter $\alpha$
that controls the utility-discrimination trade-off is set to 1.

(a)

(b)

Figure 6: Error bars for utility (a) and demographic leakage(b) for dataset
D1. $\alpha$ for the FairSum setting is set to 1.

(a)

(b)

Figure 7: Error bars for MAE (a) and demographic leakage(b) for dataset D2.
$\alpha$ for the FairSum setting is set to 1.