††thanks: These authors contributed equally††thanks: These authors contributed
equally

# Finite resource performance of small satellite-based quantum key
distribution missions

Tanvirul Islam<EMAIL_ADDRESS>Centre for Quantum Technologies, National
University of Singapore, 3 Science Drive 2, 117543 Singapore  Jasminder S.
Sidhu<EMAIL_ADDRESS>SUPA Department of Physics, University of
Strathclyde, Glasgow, G4 0NG, UK  Brendon L. Higgins
<EMAIL_ADDRESS>Institute for Quantum Computing and Department of
Physics and Astronomy, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Thomas Brougham  SUPA Department of Physics, University of Strathclyde,
Glasgow, G4 0NG, UK  Tom Vergoossen  SpeQtral Pte. Ltd., 73 Science Park Drive
Science Park 1, 118254 Singapore  Daniel K. L. Oi  SUPA Department of Physics,
University of Strathclyde, Glasgow, G4 0NG, UK  Thomas Jennewein  Institute
for Quantum Computing and Department of Physics and Astronomy, University of
Waterloo, Waterloo, ON N2L 3G1, Canada  Alexander Ling  Centre for Quantum
Technologies, National University of Singapore, 3 Science Drive 2, 117543
Singapore  Department of Physics, National University of Singapore, Blk S12, 2
Science Drive 3, 117551 Singapore

###### Abstract

In satellite-based quantum key distribution (QKD), the number of secret bits
that can be generated in a single satellite pass over the ground station is
severely restricted by the pass duration and the free-space optical channel
loss. High channel loss may decrease the signal-to-noise ratio due to
background noise, reduce the number of generated raw key bits, and increase
the quantum bit error rate (QBER), all of which have detrimental effects on
the output secret key length. Under finite-size security analysis, higher QBER
increases the minimum raw key length necessary for non-zero secret key length
extraction due to less efficient reconciliation and post-processing overheads.
We show that recent developments in finite key analysis allow three different
small-satellite-based QKD projects CQT-Sat, UK-QUARC-ROKS, and QEYSSat to
produce secret keys even under very high loss conditions, improving on
estimates based on previous finite key bounds. This suggests that satellites
in low Earth orbit can satisfy finite-size security requirements, but remains
challenging for satellites further from Earth.

##  I Introduction

The emergence of terrestrial quantum networks in large metropolitan areas
demonstrates an increasing maturity of quantum technologies. A networked
infrastructure enables increased capabilities for distributed applications in
delegated quantum computing [1, 2], quantum communications [3, 4], and quantum
sensing [5]. However, extending these applications over global scales is
currently not possible owing to exponential losses in optical fibres. Space-
based segments provide a practical route to overcome this and realise global
quantum networking [6, 7]. Satellite-based Quantum Key Distribution (SatQKD)
has become a precursor to long-range applications of general quantum
communication [8, 9]. Although a general-purpose quantum network [10] will
require substantial advancements in quantum memories and routing techniques, a
satellite-based QKD system adds to the progress of global-scale quantum
networks by driving the maturation of space-based long-distance quantum links.

There has been growing interest in satellite-based quantum key distribution.
The recent milestone achievements by the Micius satellite [11] which
demonstrated space-to-ground QKD and entanglement distribution have energized
this interest. Micius, being a large satellite, leaves open the possibility of
using smaller satellites to perform satellite-based QKD—there have been
feasibility studies for small-satellite-based QKD and CubeSat-based pathfinder
missions [12] for QKD applications.

This surge in effort emphasizes the importance of understanding specific
limitations to the performance of different SatQKD systems. For low-Earth
orbit (LEO) satellites, a particular challenge is the limited time window to
establish and maintain a quantum channel with an optical ground station (OGS).
This limitation disproportionately constrains the volume of secure keys that
can be generated due to a pronounced impact of statistical fluctuations in
estimated parameters.

In this work, we analyze three different mission configurations: the Singapore
Centre for Quantum Technologies’ CQT-Sat, the UK quantum research
CubeSat/Responsive Operations for key services (QUARC/ROKS) satellite, and the
Canadian quantum encryption and science satellite (QEYSSat). The quantum
channel configuration for each mission is illustrated in Figure 1. Depending
on the ground station location and the specific LEO orbit, a satellite may
have a limited number of passes over the OGS for which QKD key generation is
possible—for example, current technology requires that passes are conducted
during nighttime. Therefore, it is important to understand the conditions that
allow a SatQKD system to produce secret keys successfully from a single pass
over the OGS. More specifically, for any given satellite pass, how many secret
key bits can be generated? We answer this for the three mission configurations
by revisiting the supporting theory and modelling of key generation [13, 14,
15, 16, 17]. It is shown that all three missions demonstrate enhanced key
generation with the latest advancements in finite key analysis. We conclude by
looking at the prospects for satellites at higher altitude where the longer
access time for a ground receiver does not overcome the increased diffraction
loss.

Figure 1: Quantum channel configuration for three different SatQKD missions.
Each mission implements a different combination of QKD protocols and quantum
channel configurations between an OGS and an orbiting satellite. The
Singaporean CQT-Sat mission (left) implements the entanglement-based BBM92
protocol (blue arrow) in a downlink configuration. For this mission, one of
the photon pairs is measured on board and the other is transmitted to the OGS.
The UK QUARC/ROKS mission (middle) implements the WCP decoy-state BB84
protocol (red arrow) in a downlink configuration. The Canadian QEYSSat mission
(right), implements both the decoy BB84 and BBM92 protocols (purple arrow) in
an uplink configuration and intends to also incorporate a decoy BB84 downlink.

##  II Satellite-to-ground entanglement-based QKD using BBM92

CQT-Sat is a 12U nano-satellite capable of performing space-to-ground
entanglement-based QKD. Its precursor SpooQy-1 demonstrated [12] successful
launch and operation of a miniaturized polarization-entangled photon pair
source in LEO. The subsequent payload will build upon this to perform space-
to-ground entanglement distribution and demonstrate entanglement-based BBM92
QKD.

During a satellite’s overpass over the ground station, the link loss for the
downlink quantum channel will depend on the relative distance between the
satellite and the OGS. Using a variable attenuator, a tabletop setup can
emulate a time-varying satellite-to-ground link loss (a similar experiment was
conducted previously in the context of QEYSSat [18]). This enables us an
estimation of the achievable raw key length and overall QBER for various
satellite passes. Using these parameters we perform finite key analysis and
show that CQT-Sat can successfully generate shared secret keys between the
satellite and OGS when the maximum elevation is as low as $33^{\circ}$.

###  II.1 System configuration

The satellite quantum source generates polarization-entangled photon pairs by
superposing orthogonally polarized photons created from spontaneous parametric
down-conversion using two pump decay paths [19]. Detailed design of a
functional model of the source and associated design trade-offs can be found
in [20]. The source generates pairs of polarization-entangled photons where
each pair consists of a 785 nm wavelength signal photon and an 837 nm
wavelength idler photon. For the purpose of QKD, each of the idler photons is
measured aboard the satellite in either the computational or the diagonal
basis with probability $1/2$. The signal photon is sent to the satellite’s
optical terminal using an optical interface. A subsystem inside the optical
source also generates a synchronization beacon. Both the beacon and the signal
photons are transmitted to the OGS through the satellite’s optical terminal.

Optical terminals both on the satellite and in the ground station facilitate
establishing a space-to-ground free-space optical link. They consist of
optical telescopes and fine pointing mechanisms for transmitting and
collecting the signal photons, and synchronization and tracking beacons. Table
1 presents the parameters of the quantum source and the optical link.

Parameter |  Value |  Description
---|---|---
| Transmitter
---
aperture
0.09 m |  | Realistic aperture size
---
for nanosatellite
| Receiver
---
aperture
0.6 m |  | Optimum aperture
---
| Beam
---
quality
1.6 M2 |  | Fundamental limit is 1.4
---
due to diffraction
| Atomspheric
---
attenuation
3 dB |  | 3 dB at zenith,
---
scaled with path length
for lower elevation
| Pointing
---
jitter
5 microrad |  | 1.2 microrad demonstrated
---
on Micius satellite
Efficiency |  50 % |  | Estimated based on
---
reflectivity and number
of optical surfaces
| Background
---
counts
1300 cps |  | Measured with respective
---
setup in Singapore
|  |
Table 1: Space-to-ground optical link parameters for CQT-Sat.

###  II.2 Emulating space-to-ground QKD using a tabletop setup

To emulate a space-to-ground QKD link we built the entanglement source and the
detection apparatus representative of both the satellite and ground systems.
The system parameters for this setup is listed in Table 2.

System Parameter | Value
---|---
Entangled pair production rate | 20 Mcps
Source intrinsic QBER | 0.91 %
Signal wavelength | 785 nm
Idler wavelength | 837 nm
Bandwidth | 5 nm
Detection efficiency | 25 %
Dark count rate per detector | 500 cps
Detector dead time | 50 ns
Detection jitter | 320 ps
Detector after-pulsing probability | 5 %
Table 2:  Source and detector parameters for CQT-Sat.

We consider a Sun synchronous low Earth orbit with 500 km altitude above sea
level. This orbit choice gives us daily passes over our ground station at a
pre-specified time of the day [21]. We compute a time series of the
satellite’s angular elevation with respect to the OGS and the loss at that
elevation for a pass. For example, in Figure 2 we show a pass with
$88^{\circ}$ maximal elevation and associated loss that the optical link
experiences.

Figure 2:  Example simulated satellite pass reaching $88^{\circ}$ elevation
angle (green line). Experimental data is assembled such that its loss profile
(orange line) closely matches the theoretically predicted optical loss (blue
line).

Using a variable attenuator we introduce different losses, and record
detection timestamps for both signal and idler photons. Due to physical
limitations, we only use a finite number of attenuator settings, and stitch
the experimental data together to emulate the predicted loss of the optical
downlink. The blue and orange lines in Figure 2 illustrate the predicted and
experimentally-emulated loss respectively at various segments of the satellite
pass.

This technique allows the investigation of satellite passes with different
maximum elevations and to generate the associated detection timestamps both
on-board the satellite and in the OGS. These timestamp sets are processed
through the rest of the QKD protocol stack including finite key analysis to
compute the secure key length achievable from each pass.

###  II.3 Key length of CQT-Sat for various LEO satellite passes

Depending on geographical location and satellite orbit, a ground receiver
might observe 2 to 6 satellite passes each day. An ideal satellite pass would
transit directly over the ground station with maximal elevation of
$90^{\circ}$ (zenith). At zenith, the satellite is closest to the OGS, and in
clear weather this pass would exhibit the lowest transmission loss and longest
link time. However, such a pass is less likely than more “glancing” passes.
For a given detector dark count rate, higher losses would result in a poorer
signal-to-noise ratio and increase the QBER. Moreover, the pass duration and
the number of photons successfully received from the satellite also decrease.
Figure 3 shows how the secret key length changes with different satellite
passes. Here we use the finite key analysis from [17] taking security
parameter $10^{-10}$ where error correction efficiency is 1.18.

The analysis shows that below an elevation of $30^{\circ}$ no secret key is
generated. This is acceptable for CQT-Sat which was designed to avoid
operation at low elevation. The ground receiver in this case is sited at sea
level in a tropical, urban environment and the optical channel below
$30^{\circ}$ is extremely lossy due to the thicker atmospheric column.
Additional light pollution at low elevation is much more severe, contributing
to increased noise.

Figure 3:  Secret key length achievable for passes of given maximal elevation.
The blue curve gives results for the default simulation where the minimum loss
at zenith is 30.5 dB. Other curves show behaviour for optical links with
additional 1 to 4 dB losses. Solid lines are based on experimental data
constructed to emulate pass loss profiles. Dashed curves were simulated
numerically.

##  III Satellite-to-ground QKD using decoy-state BB84

The UK Quantum Research CubeSat (QUARC) project provides a design and
architectural foundation for the Responsive Operations for Key Services (ROKS)
mission in the National Space Innovation Programme (NSIP) [22, 23]. ROKS uses
a continuation of the same 6U CubeSat platform as QUARC and will first
implement decoy-state BB84 protocol in a downlink configuration for QKD
service provision using a weak coherent pulse (WCP) source (Figure 1).

The satellite quantum modelling and analysis (SatQuMA) open-source software
has been developed to estimate expected key generation performances for such
satellite QKD missions [24]. SatQuMA models the efficient BB84 weak coherent
pulse (WCP) two-decoy (three intensity) protocol and can optimise over the
entire protocol parameter space and transmission segment time. It also
incorporates recent results in finite-block composable secure key length
estimation [25, 26, 17]. SatQuMA can be applied to model the expected key
generation performance for ROKS for a general satellite pass geometry in a
Sun-synchronous orbit (SSO) at altitude $h$.

###  III.1 System configuration

We use published empirical Micius mission measurements of the total optical
loss of the SatQKD channel [27] to construct a representative total system
link efficiency as a function of elevation angle during a satellite pass. To
account for local horizon constraints around the OGS, we restrict quantum
transmissions to elevations above $10^{\circ}$. The simplest type of orbit is
the zenith pass, where the satellite flies directly over the OGS, reaching a
maximum elevation of $90^{\circ}$.

The link efficiency (loss) is highly dependent on the system parameters, OGS
conditions, and orbits. The nominal system parameters are summarized Table 3,
where the minimum total system loss at zenith is computed to be $34$ dB. One
can scale the minimum system loss at zenith to allow the comparison of
differently performing SatQKD systems. Changes to the minimum system loss at
zenith would then account for differences in the transmit and receive aperture
sizes, pointing accuracy, atmospheric absorption, turbulence, receiver
internal losses, and detector efficiencies. For the current simulations, we
consider a nominal baseline value of 34 dB. SatQKD missions with differing
performance can be modelled by linearly scaling the link efficiency vs
elevation curve to account for different constant efficiency factors, such as
a change in OGS receiver area.

To evaluate the sensitivity of the achievable secret key length to different
errors, we categorise different contributions associated with sources and
detectors in two key parameters. First, errors from dark counts and background
light are combined together into a single extraneous count probability
$p_{\text{ec}}$, here assumed to be constant and independent of elevation. In
practice, it will depend strongly on the environment of the OGS and light from
celestial bodies. Second, all other error terms, such as misalignment, source
quality, imperfect detection, are combined into an intrinsic quantum bit error
rate $\text{QBER}_{\text{I}}$ independent of channel loss/elevation. This
allows an efficient method to determine the sensitivity of the secret key
length to different categories of errors, which helps identify targeted
improvements for future SatQKD missions. All of the nominal system parameters
are summarised in Table 3.

Parameter |  Value |  Description
---|---|---
| Intrinsic error
---
0.5% |  Source errors
After-pulsing |  0.1% |  Probability of $p_{\text{ap}}$
Extraneous count rate |  $5\times 10^{-7}$ |  Probability of counts from background light
Source rate |  100 MHz |  Signal frequency
Error correction |  $10^{-15}$ |  Error-correction efficiency
| Security
---
$10^{-9}$ |  Security parameter
Altitude |  $500$ km |  Satellite orbit altitude
System loss |  $34$ dB |  Loss at zenith
|  |
Table 3: Reference system parameters. We take published information of the
Micius satellite and OGS system as representing an empirically derived set
point for our finite key analysis. The total loss at zenith can be linearly
scaled to model other systems with smaller OGSs or differing source rates.

###  III.2 Optimised finite key length

We model the efficient BB84 protocol, adopting the convention of key
generation using signals encoded in the $\mathsf{X}$ basis and parameter
estimation using signals in the $\mathsf{Z}$ basis, chosen with biased
probabilities. For a two-decoy-state WCP BB84 protocol, one of three
intensities $\mu_{j}$ for $j\in\\{1,2,3\\}$ are transmitted with probabilities
$p_{j}$. An expression for the final finite key length, $\ell$, for this
protocol is given in Ref. [13]. The key is extracted from data for the whole
pass as a single block without partitioning, the security proof of Ref. [13]
makes no assumptions about the underlying statistics. This avoids having to
combine small data blocks with similar statistics from different passes—thus,
it is both quicker and avoids the need to track and store a combinatorially
large number of link segments until each has attained a sufficiently large
block size for asymptotic key extraction.

The limited data sizes from restricted pass times results in key length
corrections to account for finite statistics of the link. To improve the
analysis, we use the tight multiplicative Chernoff bound [16] and improve the
estimate of error correction leakage
$\lambda_{\text{EC}}\leq\log|\mathcal{M}|$, where $\mathcal{M}$ characterises
the set of error syndromes for reconciliation [14] (see Ref. [25] for more
details).

Figure 4:  Estimated secret key length generated by QUARC mission for passes
with different maximum elevation angles. In the default simulation the minimum
total system loss is 34 dB. Other curves show behaviour for optical links with
additional 2, 4, and 6 dB losses.

For a defined SatQKD system, we optimise the finite key length $\ell$ by
optimising over the protocol parameter space that includes the source
intensities (with $\mu_{3}=0$) and their probabilities, and the basis encoding
probability $p_{\mathsf{X}}$. We also optimise over the portion of the pass
data used for key generation. To efficiently handle this optimisation, we
developed a numerical toolkit to numerically analyse different SatQKD systems.
The satellite quantum modelling and analysis (SatQuMA) software assists in
developing an intuition about the effects of different operational scenarios
on the key rate, to better inform the development of source and receiver
systems for future satellite missions [24].

The SatQuMA code is used to generate simulated measurement data. The QBER and
phase errors for the key bits are estimated using only the data for the
complementary basis. This is a classic sampling without replacement problem,
which is usually solved in QKD using an approximation for the hyper-geometric
distribution [28]. Recently, however, an improved sampling bound has been
proposed [17]. This can be used to estimate the QBER and phase error. The
formalism from Ref. [17] is used together with data generated from SatQuMA to
determine the secret key length for different satellite passes, which we
characterize through the maximum elevation angle. The secret key is plotted in
Figure 4 as a function of the maximum elevation angle of a pass.

##  IV SatQKD using BBM92 in uplink or downlink configurations

The Quantum Encryption and Science Satellite (QEYSSat) mission [29] is a
Canadian initiative to develop and launch a microsatellite-hosted quantum
receiver payload into low-Earth orbit. The primary objective of the mission is
to demonstrate QKD via quantum uplink from sources located at two or more
ground stations. To support this, the QEYSSat payload will possess a large
front-end telescope for light collection, polarization discriminating optics,
and single-photon avalanche diodes [30]. Support for a WCP downlink protocol
is also being developed. As of writing, QEYSSat is in the late design/early
construction phase and on schedule to launch in 2023/2024.

###  IV.1 System configuration

Figure 5: Expected performance of representative conditions modelled for
QEYSSat performing entanglement based QKD in (a) Entanglement based satellite-
to-ground downlink and (b) Entanglement based ground-to-satellite uplink
configurations. Newer finite key analysis yields improved secure key lengths.

With QEYSSat’s nominal configuration being an uplink, and the quantum sources
located on the ground, lower secure key rates are expected when compared to a
downlink with equivalent parameters, owing to the steering effect of turbulent
atmosphere on the beam at the beginning of its propagation (in contrast to the
end of propagation for a downlink). However, a satellite receiver affords
considerably greater source flexibility. For this reason, two source types are
baselined: WCP with decoy states in an unbiased BB84 protocol, and entangled
photons (with one photon of each pair kept at the ground) in a BBM92 protocol.
It is expected that other quantum source types—e.g., quantum dots (see, e.g.,
Ref. [31])—will also be employed during the experimental phase of the QEYSSat
mission.

Commencement of the QEYSSat mission in 2018 was preceded by several
theoretical and experimental investigations into the mission’s feasibility,
both as a whole [32] and with focus on critical subsystems including pointing
[33, 34] and photon measurement [35, 36]. Of these, one early work [37]
numerically modelled the quantum optical link to establish the loss and
fidelity of polarized-photon transmission under the assumptions of the
expected orbital configuration and (generally conservative) atmospheric
conditions. Multiple scenarios were considered, consisting of notional WCP or
entangled-photon sources in both uplink and downlink configurations. Although
some details of the in-development QEYSSat apparatus and conditions have been
refined since, the values remain generally very similar. In this work we
present secret key generation performance of QEYSSat while executing the
entanglement-based BBM92 protocol in both satellite-to-ground (downlink) and
ground-to-satellite (uplink) quantum communication configuration modes.

###  IV.2 Key length analysis

In the prior analysis [37], calculations were performed using secure key rate
equations which were well-established at that time—for comparison, we
reproduce those calculations, with minor corrections found since publication,
error correction efficiency improved to 1.18 (originally 1.22), and security
parameter $10^{-9}$.

More significantly, here we determine the expected secure key lengths with the
same quantum transmission and fidelity profiles (corresponding to the same
conditions being modeled) using updated secure key length analysis [17] which
has improved performance with smaller raw key block size. Performance with
smaller block size is important because it has implications on QKD feasibility
under high-loss conditions and during low maximum elevation passes.

Table 4 summarizes the parameters that describe the quantum source and the
optical link. The assumed satellite orbit (Sun-synchronous noon/midnight at
$600\text{\,}\mathrm{km}$ altitude) was simulated for one year’s duration of
nighttime passes over a notional ground station located
$20\text{\,}\mathrm{km}$ outside of Ottawa, Canada. Optical link conditions
for each pass were modeled at ten-second intervals. Background light was
determined from the Defence Meteorological Satellite Program’s Operational
Linescan System measurements and combined with an assumed half-moon at
$45\text{\,}\mathrm{\SIUnitSymbolDegree}$ (contributing via Earth reflection
using its mean albedo) along with Earth’s thermal (blackbody) radiation,
taking into account the geometry of the optical field of view and 1-nm-
bandwidth spectral filtering. Detector dark counts were also included.

Parameter | Value
---|---
Orbital altitude | $600\text{\,}\mathrm{km}$
Transmitter aperture | $0.5\text{\,}\mathrm{m}$
Receiver aperture | $0.3\text{\,}\mathrm{m}$
Pointing error | $2\text{\,}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{d}$
Optics losses | $3\text{\,}\mathrm{dB}$
Quantum transmission wavelength | $785\text{\,}\mathrm{nm}$
Detector loss | $2.3\text{\,}\mathrm{dB}$
Spectral filtering bandwidth | $1\text{\,}\mathrm{nm}$
Dark count rate per detector | $20\text{\,}\mathrm{c}\mathrm{p}\mathrm{s}$
EPS pair production rate | 100 Mcps
Source intrinsic QBER | 1%
Coincidence window | $0.5\text{\,}\mathrm{ns}$
Table 4: Ground-to-space optical link parameters for the model representing
QEYSSat.

The study considered various transmitter and receiver diameters, while
$50\text{\,}\mathrm{cm}$ and $30\text{\,}\mathrm{cm}$, respectively, were
baselined and will be assumed here. Optical losses were calculated from the
contributions of numerically modeled diffraction given a central obstruction
(secondary mirror), an assumed mean pointing error of
$2\text{\,}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{d}$,
atmospheric attenuation modeled by MODTRAN 5 for a “rural” profile with
$5\text{\,}\mathrm{km}$ visibility, and Hufnagel–Valley model of atmospheric
turbulence at sea-level. Photonic states were simulated in a 7-dimensional
Fock-space (0 to 6 photons). Intrinsic reduction in quantum visibility was
included via an operation equivalent to a small rotation.

Detector count rate statistics were calculated using assumed EPS pair
production rate of
$100\text{\,}\mathrm{M}\mathrm{p}\mathrm{a}\mathrm{i}\mathrm{r}\mathrm{s}\mathrm{/}\mathrm{s}$
and a $0.5\text{\,}\mathrm{ns}$ coincidence window. Intervals where the
simulated measurement visibility was below 85% were filtered out (see Ref.
[38]). For the present analysis, we aggregated the remaining statistics at
each pass, and sorted these by the maximum elevation achieved by the satellite
with respect to the ground station for that pass.

In Figure 5(a) and (b) we show the secret key generated for passes with
different maximum elevation in the downlink and uplink configurations
respectively for entanglement-based BBM92. For the new results we use the
finite key analysis form from Ref. [17], with stricter security parameter
$10^{-10}$ and error correction efficiency 1.18 to compute the secure key
lengths. Notably in the uplink case, the new analysis techniques achieve
almost 1/4 better key lengths for the same losses near zenith, while also
accessing positive key lengths for passes with lower maximum elevations than
the old analysis could tolerate (even with its more lenient $10^{-9}$ security
parameter). We may also compare these results with the CQT-Sat downlink
scenario where finite key analysis is performed with the same parameters.
Notably, the secret key size is considerably greater—we expect this is largely
a consequence of the faster source rate and assumed enhancements to intrinsic
QBER and pointing accuracy, coupled with the highly nonlinear effect of
finite-size statistical analysis.

##  V Comparisons

As the three SatQKD missions discussed here have different design
specifications for the ground stations, satellites, and the protocols
implemented, a direct quantitative performance comparison of the missions is
difficult. Despite this, we provide a qualitative discussion on the respective
strengths and weaknesses of each missions. First, the uplink configuration
employed by QEYSSat has the advantage that it relieves the source design from
the strict SWaP constraint imposed by a satellite. Moreover, it potentially
allows QEYSSat to swap the entanglement source to a prepare and measure source
at any point of the mission to perform a different SatQKD protocol. However,
it has a disadvantage that the optical link suffers larger environmental
turbulence during the initial part of the optical path, generating higher beam
wandering compared to a downlink configuration.

The QUARC/ROKS mission uses a prepare and measure decoy state BB84 protocol
where the source emits a periodic signal. This allows higher repetition rates
to achieve larger raw key rates to counter for the loss experienced in the
satellite-to-ground optical link. In addition to a higher performance
requirement for the detection and time-stamping circuits, the repetition rate
is also constrained by the speed of quantum random number generation for
choice of quantum signal transmission. A similar attempt to counter the link
loss by increasing brightness of an entanglement source quickly hits a
bottleneck because the SPDC based source does not produce periodic signals,
therefore due to finite resolution of time-stamping devices and the limits
imposed by detector jitters the source ends up producing too many multi-photon
events per time-slot increasing the QBER to an unacceptable value. However,
entanglement based QKD implementations act as precursors to entanglement-
sharing links, which are essential for future development towards a general-
purpose quantum internet.

##  VI Challenges and outlook

The preceding sections show that an individual small satellite can satisfy
finite-key requirements for SatQKD. While an individual satellite in an
appropriately chosen orbit can cover the Earth’s surface each day, increased
performance in the network will probably require constellations of these
satellites [39]. Aside from putting more satellites into space, it is
important to consider how the performance of each individual satellite could
be enhanced. From the preceding discussion, it is clear that LEO satellites
are working at the edge of performance in terms of SatQKD that satisfy finite-
key security. It would be desirable to have continued development to the
efficiency of finite-key security in the future. This would enable existing
technology to deliver enhanced key rates. Aside from improvements in finite-
key analysis, in which direction should development efforts proceed in order
to improve SatQKD performance?

A conventional research programme would revolve around improved transmitters
and detectors. We propose that building a system that can operate effectively
in daylight would be a major step. Practically, every SatQKD mission for the
foreseeable future will operate during nighttime to avoid excess background
light from the Sun. In order to operate during daylight, the spectral window
of the transmitted light has to be sufficiently narrow for effective
filtering, while the transmission system on the satellite has to be built to
avoid reflecting sunlight directly to the ground receiver. We note that
adaptive optics (AO) for the optical ground receiver [40, 41, 42], to
effectively couple light into a single mode fiber for direct transmission of
the QKD signal to end-users located away from the ground receiver, will also
become increasingly important. This has the added advantage that an adaptive
optics system will act as a spatial filter, reducing the amount of stray light
entering the quantum channel. To be useful, the AO system will need to be able
to operate with high coupling efficiency, so that the overall system
throughput is not compromised. Research into transmission and detection
systems that can penetrate cloud and fog would also be highly desirable.

Aside from the transmitter and detector aspects we note that a major loss
contribution is the diffraction of the beam from the satellite. This loss
could be mitigated using several different approaches. First is to operate the
spacecraft at Very Low Earth Orbit (VLEO). This orbit has a nominal altitude
below the International Space Station (approximately 400 km), and is often not
considered due to satellites experiencing significant drag and re-entry into
the Earth’s atmosphere within a year. However, with space propulsion systems
being developed for station keeping [43, 44], this approach may become
feasible and would afford significantly lower losses owing to shorter optical
links. In designing a VLEO system, factors such as micro-buffeting from the
residual atmosphere, degradation from atomic oxygen, and the shorter overhead
time of the satellite remain open challenges.

Second is the use of larger transmit/receive apertures. The use of larger
apertures has been the primary route to minimise link loss, with a doubling in
aperture sizes providing 6 dB improvements [25]. However, apertures are
restricted for small satellites. Recent efforts rely on deployable and active
optics [45, 46, 47].

Finally, diffraction losses can also be mitigated by developing larger and
more capable satellites at very high altitudes. The advantage is that such
satellites can be equipped with large transmit apertures, while increasing the
ground coverage area as well as improving access time for a ground receiver.
The drawback is a dramatic increase in diffraction loss that must be
compensated by enlarging the transmit/receive aperture and improving pointing
accuracy.

We can model the performance of SatQKD systems for varying orbital altitudes,
by imposing similar capability on the satellites. The result for LEO, Medium
Earth Orbit (MEO) [48] and Geostationary Orbit (GEO) [49] demonstrates the
range of link losses that will be encountered. Using the receiver (ground-
based) and sender (onboard-satellite) telescope, and beam parameters as shown
in Table 5 we study the space-to-ground optical link loss (Figure 6) for
higher altitude orbits. We see that the optical link loss increases rapidly
with the increase in altitude as expected from the beam expansion. Therefore,
to successfully perform SatQKD at higher orbits we would need to compensate by
increasing parameters such as the sending or receiving telescope’s aperture
and pointing accuracy. In Figure 7 we see a trade-off heat-map where the
satellite’s transmitter telescopes aperture and pointing accuracy are varied
to show how it affects the transmission for a satellite in low Earth orbit
(LEO), medium Earth orbit (MEO) and geostationary orbit (GEO). From these
trade-off calculations, we see that for orbits higher than LEO it is not
sufficient to only change the satellite’s parameter to achieve the
transmission gain necessary for successful SatQKD. For higher altitudes, one
would also need to improve other parameters, such as the ground telescope’s
aperture and pointing accuracy, and detector efficiencies.

Parameter | Value
---|---
Beam waist to aperture ratio | 0.89
Beam | $10.8\text{\,}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{d}$
Satellite transmitter aperture | $0.1\text{\,}\mathrm{m}$
Pointing error | $2.5\text{\,}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{r}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{d}$
Ground receiver aperture | $0.6\text{\,}\mathrm{m}$
Wavelength | $785\text{\,}\mathrm{nm}$
Atmospheric attenuation at zenith | $3\text{\,}\mathrm{dB}$
Table 5:  Space-to-ground optical link parameters used for modeling higher
altitude orbits. Figure 6: Link loss as a function of orbital altitude using
parameters from Table 5. Green shaded region indicates altitude range for VLEO
orbits, gray for LEO orbits, red for MEO orbits, orange for HEO, and the solid
black line for a GEO orbit. The red dashed line corresponds to a
representative MEO altitude of $7\times 10^{3}$ km that we consider later.
Link loss at zenith rapidly increases with increasing orbital altitudes.
Figure 7: Trade-off between telescope aperture onboard the satellite and
pointing error with respect to the the link gain at zenith. (a) The trade-off
for a representative low Earth orbit (LEO) at altitude 500 km. (b) The trade-
off for a representative medium Earth orbit (MEO) at altitude 7000 km. (c) The
trade-off for a geostationary orbit (GEO).

Due to increased loss at higher altitude orbits, obtaining a secure key from a
single pass over a ground station may not be possible in these cases. However,
it is possible to accumulate the raw key bits over multiple passes, increasing
the block size to reduce finite block size effects sufficiently to achieve a
secure key. In Figure 8 we see the block sizes and associated QBERs for the
raw key bits accumulated over an year for entanglement based SatQKD operated
at various orbital altitudes. The drawback of key aggregation is that large
amounts of data will have to be stored for a long time onboard the satellite
which might introduce vulnerabilities due to storage security. In Figure 8, we
discard passes that yield QBER higher than $11\%$, which generates a key with
minimal information known to an eavesdropper after post-processing [50].
However, given the limited number of bits acquired at each pass it might not
be feasible to determine the QBER reliably by exchanging subset of these bits
between the satellite and the OGS.

Figure 8: Raw key length (solid blue line) accumulated over a year and average
QBER (red line) as a function of orbital altitude. The dashed blue line gives
the secure key extracted from the accumulated raw key block. The gray dashed
vertical line indicates a GEO altitude, where the satellite remains stationary
at the zenith leading to a higher raw key and lower QBER compared to other
nearby orbits where the satellite has varying angular elevation with respect
to the OGS during an overpass. To model this, we assume satellites telescope
with aperture $0.5\text{\,}\mathrm{m}$, OGS telescope with aperture
$1.8\text{\,}\mathrm{m}$ and the entangled pair production rate 100 Mcps.

##  VII Conclusion

There is growing interest in deploying satellites to enable a global QKD
network. To ensure this goal remains feasible and to guide experimental and
engineering efforts, it is crucial to understand how SatQKD can yield
efficient secret key generation under finite transmission times and high loss
regimes. Previous works have shown that secret key generation with SatQKD is
possible using finite-key analyses. Recent advancements in the treatment of
finite key effects have improved the efficiency of key extraction, which
greatly diminishes the requirements on the minimum raw key length required for
key extraction.

We use these latest finite key bounds in the most current performance analyses
of three different mission concepts; the 12U CQT-Sat mission implementing an
entanglement-based BBM92 downlink, the 6U QUARC project implementing a WCP
decoy-state BB84 in downlink configuration, and the QEYSSat mission
implementing both the decoy BB84 and BBM92 protocols in an uplink
configuration, in addition to a decoy BB84 state downlink. All three SatQKD
missions achieve good secure key yields on the order of kilobits from a single
pass over a ground receiver, even for the missions based on resource-
constrained and aperture-limited CubeSats. This provides reassurance that
planned SatQKD missions are on course to achieve key milestones that can lead
to an effective global QKD network.

The long-term vision of a satellite-based global quantum network remains a
principal motivation behind SatQKD. However, developing the infrastructure for
a global QKD network sets the stage for future theoretical, experimental, and
engineering milestones. Multiple OGSs should cooperate with a constellation of
satellites to improve the reliability of general applications beyond QKD
services. The height of technological difficulty is to realise quantum
computing networks that implement full error correction [10, 8]. This will
require a constellation of satellites, each synchronised and equipped with
entanglement sources and quantum memories to dynamically create multi-link
connections between any two points on Earth. While our calculations
demonstrate that all three LEO SatQKD missions considered here have the
ability to yield secure finite keys, it is clear that overcoming numerous
hardware challenges and further improving security analyses simultaneously are
required to establish global quantum communications.

###### Acknowledgements.

J.S.S, T.B. and D.K.L.O. acknowledge support of the UKNQTP and the Quantum
Technology Hub in Quantum Communications (EPSRC Grant EP/T001011/1). B.L.H.
and T.J. acknowledge support from the Canadian Space Agency, and thank J.-P.
Bourgoin for discussions. T.I. and A.L. acknowledge support from the Research
Centres of Excellence programme supported by the National Research Foundation
(NRF) Singapore and the Ministry of Education, Singapore.

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# Preventing Repeated Real World AI Failures by Cataloging Incidents:
The AI Incident Database

Sean McGregor

###### Abstract

Mature industrial sectors (e.g., aviation) collect their real world failures
in incident databases to inform safety improvements. Intelligent systems
currently cause real world harms without a collective memory of their
failings. As a result, companies repeatedly make the same mistakes in the
design, development, and deployment of intelligent systems. A collection of
intelligent system failures experienced in the real world (i.e., incidents) is
needed to ensure intelligent systems benefit people and society. The AI
Incident Database is an incident collection initiated by an industrial/non-
profit cooperative to enable AI incident avoidance and mitigation. The
database supports a variety of research and development use cases with faceted
and full text search on more than 1,000 incident reports archived to date.

##  1 Introduction

Governments, corporations, and individuals are increasingly deploying
intelligent systems to safety-critical problem areas, including transportation
(NTSB 2017) and law enforcement (Dressel and Farid 2018), as well as
challenging social system domains such as recruiting (Dastin 2018). Failures
of these systems pose serious risks to life and wellbeing, but even good-
intentioned intelligent system developers fail to imagine what can go wrong
when their systems are deployed in the real world. Worse, the artificial
intelligence system community has no formal systems whereby practitioners can
discover and learn from the mistakes of the past. Individuals in technology
(Olsson 2019), legal practice (Hall 2020), and reputation management (Pownall
2020) now collect artificial intelligence failure history on Google Docs and
GitHub. While these are admirable efforts, a person checking for problems
matching their technology or problem domain will need to page through lists of
links to find ones of potential relevance. Existing lists are difficult to use
in development, are not comprehensive archives, and are representative of
individual viewpoints of artificial intelligence (AI) failures in the real
world.

Avoiding repeated AI failures requires making past failures known to AI
practitioners. Therefore, we introduce a systematized collection of incidents
where intelligent systems have caused safety, fairness, or other real world
problems. The AI Incident Database (AIID) answers the question, “what can go
wrong when someone deploys this system”?

The contributions of this work are 3 fold. We provide infrastructure
supporting best practices within the artificial intelligence industry, a
dataset of more than 1 thousand incident reports, and an architecture for
building research products on the growing collection of incidents. We begin by
exploring incident databases in other fields of practice before introducing
the system architecture of the AIID. We then wrap up with a few concluding
remarks.

Figure 1:  The US National Transportation Safety Board’s (NTSB’s) database
shown above indexes incident and accident timelines, location, meteorology,
severity, aircraft, operators, and phase of flight as facets. The reports also
have a full text narrative that is searchable. Upon completion of an
investigation, the report is indexed alongside the case record within the
database (Federal Aviation Administration 2020).

##  2 Other Incident Databases

The commercial air travel industry owes much of its increasing safety to
systematically analyzing and archiving past accidents and incidents within a
shared database. In aviation, an accident is a case where substantial damage
or loss of life occurs. Incidents are cases where the risk of an accident
substantially increases. For example, when a small fire is quickly
extinguished in a cockpit it is an “incident” but if the fire burns crew
members in the course of being extinguished it is an “accident.” The aviation
database (see Figure 1) indexes flight log data and subsequent expert
investigations into comprehensive examinations of both technological and human
factors. In part due to this continual self-examination, air travel is one of
the safest forms of travel. Decades of iterative improvements to safety
systems and training have decreased fatalities 81 fold since 1970 when
normalized for passenger miles (Mediavilla 2020).

Aviation accidents share a well-defined operational context, but intelligent
systems can be applied to all contexts. The comprehensive nature of
“intelligence” means AI incident databases ingest unforeseen and novel
contexts, technologies, and failures. The AIID design outlined in the next
section introduces a system architecture inspired by the aviation incident and
accident database, but with a greater emphasis on extensibility.

The second incident database inspiring the AIID is the Common Vulnerabilities
and Exposures (CVE) system, which contains $141,076$ publicly disclosed
cybersecurity vulnerabilities and exposures (The MITRE Corporation 2020). In
contrast to the aviation database, which serves users associated with a single
industry, the CVE site serves as critical security infrastructure across all
industries by enabling vulnerabilities to be circulated and referenced with a
consistent identifier. Other systems consume the identifiers to apply
taxonomies (e.g., the Common Vulnerability Scoring System), produce research,
and develop more secure software. The creation of numbered identifications
forms community infrastructure that the field of artificial intelligence
currently lacks. The lists of Olsson (2019), Hall (2020), and Pownall (2020)
lack the comprehensive coverage, identification, and extensibility properties
of the CVE, and the full text search capability of the NTSB database.

##  3 The AI Incident Database

Figure 2: Incident numbers enumerate AI incidents within the database and join
together incident reports. Incidents are citeable and are credited to the
person who first submitted an incident report for a new incident. The
incidents are intentionally untitled because incident descriptions tend to
change through time as more is learned and achieving consensus titles by both
corporate and non-profit database participants would be difficult.

The AIID indexes more than 1,000 publicly available “incident reports,” which
are a mixture of documents from the popular, trade, and academic press.
Multiple reports often pertain to a single incident collectively joined
together by a single identifier. For example, incident number 3 is composed of
18 reports on the Boeing 737 MAX 8 crashes (Olsson 2018a). The variety of
reports serves several purposes. First, it provides multiple viewpoints on
incidents for which there is often disagreement about fair characterizations.
In the Boeing case, people disagree on the extent to which technological or
human factors played a part in the tragedies. Second, the number of
publications and publication types serves as a proxy for interest in the
incident. More reported incidents are typically more damaging, more
sensational, or both. After opening the AIID to public submissions, we expect
incident 3 will have thousands of incident reports due to intense public
interest in the safety of flight. Lastly, sampling multiple reports per
incident gives a more complete coverage of the words applicable to an incident
and increases the likelihood of users discovering incidents relevant to their
use cases. The use cases are detailed in the following user stories.

User: Product Managers. Corporate product managers are responsible for
defining product requirements before and during product development. If a
product manager discovers incidents where intelligent systems have caused
harms in the past, they can introduce product requirements to mitigate risk of
recurrence. For example, when a product manager is specifying a recommender
system for children, the AIID should facilitate the discovery of incident 1
(Yampolskiy 2020), wherein YouTube Kids recommended inappropriate content.
Knowledge of incident 1 would produce a range of technological, marketing, and
content moderation requirements for the product.

User: Risk Officers. Organizationally, risk officers are tasked with reducing
the strategic, reputational, operational, financial, and compliance risks
associated with an enterprise’s operation. Consider the case of a social
network preparing to launch a new automatic translation feature. A search of
“translate” within the AIID returns 40 separate reports, included among them
an incident wherein a social media status update of “good morning” translated
to “attack them” and resulted in the user’s arrest (Anonymous 2017). After
discovering the incident, the risk officer can read reports and analyses to
learn that it is currently impossible to technologically prevent this sort of
mistake from happening, but there is a variety of best practices in mitigating
risk, such as clearly indicating the text is a machine translation.

User: Engineers. Engineers can also benefit from checking the AIID to learn
more about the real world their systems are deployed within. Consider the case
of an engineer who is making a self driving car with a image recognition
system. The experience of incident 36 (Olsson 2018b), where a woman in China
was shamed for jaywalking because her picture was on the side of a bus, shows
hows images can confuse image recognition systems. Such cases must therefore
be represented within safety tests.

User: Researchers. Safety and fairness researchers already employ case study
methodologies in their scholarship, but they presently lack the capacity to
track AI incidents at the population level. For example, it is difficult to
show the rate at which incidents involving policing are changing through time.
An AIID search for “policing” in the full text of reports currently returns 14
distinct incidents. Each of these incidents are additionally citeable (see
Figure 2) within research papers. The resulting research papers can then be
added to the database as further reporting on the incident. Additionally,
researchers can show the importance of their publications by citing incidents
that could potentially be mitigated through their advances.

Finally, we note that making a database entry shareable (i.e., linkable)
empowers these users rhetorically to convince others that mitigation is
necessary. Technology companies are famous for their penchant to move quickly
without evaluating all potential bad outcomes. When bad outcomes are
enumerated and shared, it becomes impossible to proceed in ignorance of harms.

### System Architecture

Figure 3: A user has entered “facial recognition” as a search term into the
search box of the “Discover” AIID application. 89 reports returned to the
search instantaneously (every keystroke filters the results and the page
renders) and the matching text from the reports is snippeted. The publications
represented within the results are faceted in the left column along with the
authors, submitters, and incident numbers to support filtering the reports
based on their metadata.

The AIID is a project of the Partnership on AI (PAI), which is a multi-
stakeholder organization funded by technology companies and governed by a
board of directors split between corporate partners and non-profit civil
society organizations. The dual nature of the organization means prescriptive
norms are difficult to promulgate. For example, in 2018, Google unveiled a
dataset and competition meant to combat bias in computer vision classifiers
(Doshi 2018). Discussion within the partnership’s Fairness, Transparency, and
Accountability working group briefly entertained providing a PAI endorsement
for the program, but such an endorsement proved impossible due to vigorous
debate on whether computer vision should be improved in human contexts. Such
debates are both a great weakness of PAI and also its greatest asset as a
multi-stakeholder organization. Through forced exposure to diametrically
opposed views, it becomes possible for both sides to incrementally iterate and
find common ground. The AIID provides infrastructure joining together these
disparate viewpoints so that they can speak for themselves and avoids
providing top-down analysis or narrative. Instead, it is the responsibility of
the open source community to layer taxonomies and summaries onto the database.
This mirrors practices by the CVE system, which provides the identifier to a
surrounding security community ecosystem.

Figure 4: The database provides a leaderboard of submitters and authors
totalling the number of reports associated with their submissions.
Gamification in other contexts has shown that people are more eager to
volunteer their time for a community resource if the sum total of their
contribution is constantly recognized and reinforced. Figure 5: Every time the
applications rebuild, the complete text of all reports are queried, stemmed,
and stop words are removed. The top words in the database are then rank
ordered and rendered in the page. This application generates no requests to
the database when a user requests it since the page is pre-built in the
application rendering pipeline. This means computationally expensive natural
language models could be applied in the application lifecycle (e.g., training
topic models) without negatively impacting user experience.

The AIID is a collection of web applications that interfaces with a MongoDB
document database storing incident report text and metadata. The first
application developed for the database is the “Discover” application, which is
built to help users discover past incidents relevant to their work. Figure 3
shows one search in the Discover application. All searches in the Discover
application are “instant searches,” meaning they return results in less than a
second. Another application is the “Submit” application, which is a form for
submitting links to publicly available incident reports. As the database
grows, the Submit application will grow in sophistication to resolve new
report submissions to existing incidents. Incident reports are often written
and submitted long after the initial creation of an incident record and
resolving new submissions to already indexed incidents will require additional
tooling.

The system architecture is built to express the full range of viewpoints
represented by the PAI partner community by allowing partners to build their
own taxonomies, taxonomy documentation, and data summaries (see Figure 7). The
taxonomy system is built to be interoperable, meaning tags are namespaced and
can be faceted (i.e. filtered). For example, Figure 6 shows how taxonomies
defined in Industry and Fairness namespaces can be faceted within the Discover
application. Namespaces are managed by individual or groups of partners and
thus are not necessarily representative of the whole PAI ecosystem. This
avoids the challenge of developing a single shared universal ontology for AI
incidents and instead allows for multiple viewpoints on the data to develop
and compete for mindshare. All incident reports have metadata captured on
entry into the database, including title, source, author, submitter,
publication date, incident date, and incident number. These are all objective
facts that can be filtered as shown in Figure 3. These facets are also where
we introduce subjective taxonomic classification of reports and incidents.

Figure 6: A mockup of two taxonomies presented as facets within the Discover
application. Here the Fairness tags could be defined and applied by a non-
profit active in fairness advocacy, while the Industry tags could be defined
and managed by a business consultancy. While the tags are all controlled by
their own application and managing entity, they can be applied as filters
across all applications within the AIID. Figure 7: Fairness domain partners
create a new application for the AIID named “FairnessExplorer,” which includes
both a taxonomy and a summary page. Upon pushing the code to GitHub, a build
on the static website hosting service Netlify is triggered. The build process
queries the database to generate static summaries of the database contents,
including the Fairness Summary. The website then deploys to a global content
distribution network. Users can then apply classifications within the scope of
the FairnessExplorer taxonomy. Industry partners can then filter incidents
based on the classifications of the FairnessExplorer taxonomy, as well as
taxonomies developed by other domain partners.

The applications are hosted within the context of a web application combining
documentation, data products (see Figure 5), and social credit (see Figure 4).
The problem with these database views is that they often require iterating
over the complete database. If these pages render for the user every time the
user visits the page, the database would be slow and expensive to host.
Instead, the AIID periodically pre-renders database views as static web
applications, which means they only require a single database request at the
time the website builds. As such, it is possible to develop a gallery of views
into the data similar to the D3JS gallery, which has 168 different
visualization examples (Bostock 2020). One potential AIID example is
supporting trend analysis for unsupervised topic models or hand tailored topic
models monitoring technology, affected populations, or applications through
time. These analyses can be incorporated into the static build (see Figure 7)
and update automatically when the website updates.

##  4 Conclusion

We expect the extensible architecture will provide for the most pragmatic
coverage of AI incidents through time while reducing negative consequences
from AI in the real world. Early indications of adoption are strong. Even
prior to publishing the database, we have received collaboration requests from
“Big 4” accounting firms, international consultancies, law firms, research
institutes, and individual academics. Through time we hope the database will
develop from the work product of a small team of individuals into community
owned infrastructure aligned with producing the most beneficial intelligent
systems for people and society. To quote Santayana, “Progress, far from
consisting in change, depends on retentiveness… Those who cannot remember the
past are condemned to repeat it.” (Santayana and Cory 1924)

##  5 Acknowledgments

The author is grateful to Jingying Yang and the ABOUT ML project at the
Partnership on AI for sponsoring this work and to the incident submitters,
especially to Roman Yampolskiy, Catherine Olsson, Sam Yoon, Patrick Hall, and
Charlie Pownall for their efforts in collecting many incidents to date.
Iftekhar Ahmed also provided useful feedback on the paper.

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