# On the transmission of turbulent structures across the Earth’s Bow Shock

Domenico Trotta The Blackett Laboratory Imperial College London London. SW7
2AZ, UK  Francesco Pecora Department of Physics and Astronomy, University of
Delaware, Newark DE 19716 USA  Adriana Settino Dipartimento di Fisica,
Università della Calabria, I-87036 Cosenza, Italy  Denise Perrone ASI -
Italian Space Agency, Via del Politecnico snc, I-00133 Rome, Italy  Heli
Hietala The Blackett Laboratory Imperial College London London. SW7 2AZ, UK
Timothy Horbury The Blackett Laboratory Imperial College London London. SW7
2AZ, UK  William Matthaeus Department of Physics and Astronomy, University of
Delaware, Newark DE 19716 USA  David Burgess School of Physics and Astronomy
Queen Mary University of London, London, E1 4NS, UK  Sergio Servidio
Dipartimento di Fisica, Università della Calabria, I-87036 Cosenza, Italy
Francesco Valentini Dipartimento di Fisica, Università della Calabria, I-87036
Cosenza, Italy

(Received XXX; Revised XXX; Accepted XXX)

###### Abstract

Collisionless shocks and plasma turbulence are crucial ingredients for a broad
range of astrophysical systems. The shock-turbulence interaction, and in
particular the transmission of fully developed turbulence across the quasi-
perpendicular Earth’s bow shock, is here addressed using a combination of
spacecraft observations and local numerical simulations. An alignment between
the Wind (upstream) and MMS (downstream) spacecraft is used to study the
transmission of turbulent structures across the shock, revealing an increase
of their magnetic helicity content in its downstream. Local kinetic
simulations, in which the dynamics of turbulent structures is followed through
their transmission across a perpendicular shock, confirm this scenario,
revealing that the observed magnetic helicity increase is associated with the
compression of turbulent structures at the shock front.

editorials, notices — miscellaneous — catalogs — surveys

††journal: ApJ Letters

##  1 Introduction

The interaction between collisionless shocks and plasma turbulence is a key
ingredient for the understanding of many astrophysical phenomena (e.g., Bykov
et al., 2019; Guo et al., 2021). Such an interaction has been of growing
interest in recent literature, involving theoretical (Zank et al., 2002),
numerical (Giacalone & Jokipii, 1996; Guo & Giacalone, 2012; Trotta et al.,
2021) and observational (Lario et al., 2019; Pitňa et al., 2017; Perri et al.,
2021) efforts.

A common example of such an interaction is the Earth’s bow shock, at the
interface between the turbulent solar wind flow and the Earth’s magnetosphere.
From the first _Pioneer_ spacecraft observations (Dungey, 1979) to the modern,
Magnetospheric MultiScale (MMS) mission (Burch et al., 2016), an extraordinary
amount of knowledge has been built around the Earth’s bow shock, supported by
numerical (e.g., Turc et al., 2018) and theoretical modelling (e.g. Chapman &
Cairns, 2003). Earth’s bow shock has become a prototype to address energy
conversion and particle acceleration (see Schwartz et al., 2021).

An exceptionally important property of turbulent flows is the emergence of
coherent structures, that characterize the energy transfer from large to small
scales (e.g. Holmes et al., 1996; Greco et al., 2010; Servidio et al., 2015;
Krieger et al., 2020). Such structures, ubiquitous in the heliosphere, are
fundamental building blocks of the interplanetary environment (see Khabarova
et al., 2021; Pezzi et al., 2021, for a review). Particular attention has
recently been dedicated to the study of such turbulent structures’ magnetic
helicity content, an important rugged invariant of magnetohydrodynamics (MHD)
theory, providing important information about the topology of the magnetic
field (Matthaeus et al., 1982; Pecora et al., 2021).

The transmission of turbulent structures across collisionless shocks is an
important factor in shock energetics (e.g., Zank et al., 2021), as turbulent
structures themselves are important particle accelerators (e.g. Drake et al.,
2006; le Roux et al., 2018; Trotta et al., 2020b), and their dynamics are
affected by shock crossing. On the other hand, transmission of turbulent
structures may induce strong changes on the shock transition properties (e.g.
Kajdič et al., 2019; Trotta et al., 2021), as well as affecting particle
transport across the shock transition (e.g., Perri & Zimbardo, 2015).

In the present work, we tackle the problem of the transmission of turbulent
structures across quasi-perpendicular shocks. We use a combination of Wind and
MMS data to study the turbulent properties of the plasma upstream and
downstream of the Earth’s bow shock. We then present novel kinetic simulations
of shocks propagating in turbulent media, introducing the possibility to
follow turbulent structures through their interaction with the shock. We find
strong helicity increase from the shock upstream to the downstream, and
propose a simple theoretical treatment that links the time evolution of
magnetic helicity to plasma compression in the MHD framework. The paper is
organized as follows: in Section 2 the spacecraft observations are presented,
and the structures’ transmission across the bow shock is discussed in Section
3. In Section 3 the simulations results are shown. Finally, the conclusions
are reported in Section 4.

Mission | start (UT) | end (UT) | B (nT) | Ni (cm-3) | Vi (km/s) | Ti (eV) | $\beta_{i}$ | vA (km/s)
---|---|---|---|---|---|---|---|---
Wind - Solar wind | 20:30:16 | 01:59:53 | 10 $\pm$ 1 | 12 $\pm$ 3 | 390 $\pm$ 17 | 12 $\pm$ 2 | 0.7 $\pm$ 0.3 | 63 $\pm$ 12
MMS1 - Upstream | 22:08:50 | 22:09:10 | 11 $\pm$ 2 | 8.9 $\pm$ 0.7 | 345 $\pm$ 6 | 47 $\pm$ 22 | 1.3 $\pm$ 0.6 | 83 $\pm$ 3
MMS1 - Downstream | 22:09:50 | 22:10:10 | 32 $\pm$ 4 | 37 $\pm$ 5 | 186 $\pm$ 15 | 118 $\pm$ 31 | 1.8 $\pm$ 0.8 | 114 $\pm$ 3
Table 1: Summary of intervals used for Wind and MMS1 (rows) _in-situ_ analyses
on December 31, 2017: Start and end times, magnetic field, ion density, bulk
flow speed, temperature, beta and Alfvén speed.

##  2 Spacecraft observations

In this section, we compare the statistical properties of turbulence in the
pristine solar wind with the shocked magnetosheath plasma. These analyses,
which are not intended to highlight a 1 to 1 correspondence between the solar
wind and the magnetosheath structure transmission, reveal important
statistical differences that probably arise due to the interaction between the
shock-front and the incoming solar wind turbulence. In this regard, the
structure/shock interaction might play a crucial role.

###  2.1 Wind and MMS data

On 2017 December 31, during a crossing from the pristine solar wind to the
magnetosheath plasma, MMS observed a quasi-perpendicular bow-shock on the dusk
side. Properties and geometry of the bow shock have been evaluated by
averaging MMS1 measurements in the upstream and downstream regions using the
same time interval ($20$ s) and carefully excluding the shock and foot regions
(see Table 1).

We used magnetic field data from the FluxGate Magnetometer (FGM) (Russell et
al., 2016), sampled at $16$ Hz in fast mode and the particle data collected by
the Fast Plasma Investigation (FPI) instrument (Pollock et al., 2016) with a
cadence of $4.5$ s in fast mode. In the downstream region of Earth’s bow
shock, MMS observed a continuous interval (about $4$ hours) of shocked solar
wind. In the meanwhile, both DSCOVR and Wind were located on the same side of
MMS, but hundreds of RE upstream of Earth’s bow shock. During this time, both
DSCOVR and Wind measurements are consistent, and we decide to use Wind data as
a proxy for upstream conditions.

Figure 1: Selected stream of solar wind passing through the Earth’s bow shock
for Wind (upstream, left) and MMS (downstream, right) satellites, showing
magnetic field, PVI, magnetic helicity (normalised and unnormalised) and
temperature anisotropy. The dashed magenta vertical line indicates the
position of the bow shock.

As we considered a long time interval during which the bulk flow remains
almost constant, we can reasonably assume that that the key plasma parameters
are similar at Wind and upstream of the shock at MMS. To further endorse this
point, we have compared the mean values of the main quantities in the upstream
intervals observed by Wind, which is upstream of MMS, with a timeshift of
about 55 minutes. We observe a very good agreement between the two spacecraft
measurements as shown in Table 1, thus suggesting that our previous assumption
is reasonable, at least, in a statistical sense.

###  2.2 Magnetic Helicity

Magnetic helicity is a rugged invariant of MHD turbulence, defined as
$H_{m}=\langle\bm{a}\cdot\bm{b}\rangle$, where $\bm{a}$ is the vector
potential associated with magnetic field fluctuations
$\bm{b}={\bm{\nabla}}\times{\bm{a}}$. The averaging operation
$\langle\dots\rangle$ is performed over an appropriate volume (Woltjer, 1958;
Matthaeus & Goldstein, 1982).

Magnetic helicity can be used as a proxy to detect filamentary structures
(such as flux tubes), exploiting their helical nature (e.g., Telloni et al.,
2012; Zhao et al., 2020). In this work, we use the prescription for
calculating magnetic helicity using single-spacecraft measurements, presented
in Pecora et al. (2021), which builds up on the one proposed in early works by
Matthaeus et al. (1982); Matthaeus & Goldstein (1982). This method calculates
a local (both in configuration and increment spaces) value of $H_{m}$ at a
certain point $x$, retaining the contribution of structures up to a scale
$\ell$, namely

$H_{m}(x,\ell)=\int_{0}^{\ell}dl_{i}\;h(l_{i})\int_{x-\frac{w_{0}}{2}}^{x+\frac{w_{0}}{2}}d\xi\;\epsilon_{ijk}b_{j}(\xi)b_{k}(\xi+l_{i}),$
(1)

where the i-th direction of the increments is along the relative spacecraft-
solar wind motion, and the components $b_{j}$ and $b_{k}$ are those
perpendicular to it; the interval $w_{0}$ is chosen to be proportional to the
local correlation length and $h(l)=\frac{1}{2}\left[1+\cos\left(\frac{2\pi
l}{w_{0}}\right)\right]$ is the Hann window (or any other differentiable
filter) used to smooth fluctuations to zero at the boundaries of the
considered interval.

From the quantity in Eq. 1, it is possible to derive a version of the magnetic
helicity normalized to the local energy content, namely

$\tilde{H}_{m}=\frac{H_{m}}{\langle\delta b^{2}\rangle\lambda_{c}},$ (2)

where $\langle\delta b^{2}\rangle$ is the fluctuation energy computed from a
local average taken on an interval of about a correlation length
$\lambda_{c}$. This latter normalization enhances the contribution of smaller
helical-structures.

##  3 Turbulence transmission across the bow shock

Figure 1 shows a comparison between Wind (left) and MMS observations (right),
representing a proxy for the upstream and downstream bow shock plasma,
respectively. The top panels show magnetic field measurements at one-minute
resolution, for both Wind and MMS. The Partial Variance of Increments (PVI,
Greco et al., 2008) is computed using these magnetic field streams, in panels
(b) and (g).

Panels (c) and (h) of Fig. 1 show that magnetic helicity greatly increases
when the solar wind passes from upstream to downstream. This, as shown below
in simulations, indicates that filamentary structures present upstream get
compressed by the shock and increase their helicity content. The present
scenario can also be noticed by looking at the normalized helicity in panels
(d) and (i). In the upstream region, the $\tilde{H}_{m}$ signal lies almost
entirely within the (grey) shaded region that represents the values of
helicity that cannot be readily distinguished from those pertaining to random
fluctuations rather than orderly flux tubes. However, the larger values going
beyond the “uncertainty band” suggest the presence of flux tubes, which is
confirmed in the downstream signal (panel (i)). Note that panels (c), (h) and
(d), (i) are on the same scale respectively. This statistical evidence of
enhanced magnetic flux tubes in the magnetosheath have been observed in
previous works (e.g., Stawarz et al., 2021).

Bottom panels (e) and (j) show the temperature anisotropy
$T_{\perp}/T_{\parallel}$ calculated with respect to the mean magnetic field
over the whole intervals. Upstream of the shock, where the helical signature
is weaker, there is lower temperature anisotropy. In the downstream region,
there is an increase of $T_{\perp}/T_{||}$, in agreement with some other
observations of the magnetosheath in quasi-perpendicular regions (e.g.,
Dimmock et al., 2015). Note that the downstream temperature anisotropy is
patchy, with an increase in the perpendicular temperature where helical
structures have been compressed and enhanced.

The plasma behaviour in the shock downstream is characterised by an injection
of fluctuations around ion kinetic scales, likely a consequence of typical
plasma instabilities for perpendicular shocks (e.g, Burgess & Scholer, 2015).
This was confirmed by the analysis of magnetic field spectra (not shown here),
indicating that kinetic effects are fundamental for the overall system and
cannot be neglected.

Spacecraft observations give important insights about the statistical nature
of shock-processed turbulent structures. Clearly, it is not possible to follow
the dynamics of a particular structure across the shock using _in-situ_
observations. This important experiment can instead be carried out using
numerical simulations.

We perform kinetic simulations of perturbed and unperturbed perpendicular,
collisionless shocks with and without pre-existing plasma turbulence. Our
setup, introduced in Trotta et al. (2021), uses 2.5D MHD simulations to
generate turbulent fields (e.g., Perri et al., 2017), that are then used as an
initial condition for a hybrid kinetic simulation (where protons are treated
kinetically, and electrons as a fluid) that studies the shock propagation in
the turbulent upstream. We impose magnetic field fluctuations amplitude to be
$\delta B/B_{0}=0.4$, where $\mathbf{B}_{0}=B_{0}\,\hat{e}_{z}$ is the mean
magnetic field. We use the HYPSI code (see Trotta & Burgess, 2019; Trotta et
al., 2020a) for the modelling of the (perturbed) shock evolution, initiated
using the injection method (Quest, 1985). The shock propagates in the negative
$x$-direction, with the mean upstream flow along the shock normal.

Figure 2: Overview of unperturbed (top) vs perturbed (bottom) perpendicular
shock simulations. Colormaps show simulation snapshots of magnetic field
magnitude, and the one-dimensional $B/B_{0}$ plots in (a) and (c) correspond
to horizontal cuts of the simulation domain along the green dashed lines in
(b) and (d), respectively.

In the hybrid simulations, distances are normalised to the ion inertial length
$d_{i}\equiv c/\omega_{pi}$, times to the inverse cyclotron frequency
${\Omega_{ci}}^{-1}$, velocity to the Alfvén speed $v_{A}$ (all referred to
the unperturbed upstream state), and the magnetic field and density to their
unperturbed upstream values, $B_{0}$ and $n_{0}$, respectively. The upstream
magnetic field is in the out-of-plane direction,
$\mathbf{B}_{0}=B_{0}\hat{z}$, consistent with the MHD simulation setup. For
the upstream flow velocity, a value of $V_{\mathrm{in}}=2.5v_{A}$ has been
chosen, and the resulting Alfvénic Mach number of the shock is approximately
$M_{A}\equiv v_{\rm{sh}}/v_{A}\simeq 3.3$. The upstream ion distribution
function is an isotropic Maxwellian and the ion $\beta_{i}$ is 1. The
simulation $x-y$ domain is (256 $\times$ 256) $d_{i}^{2}$, with spatial
resolution $\Delta x$ = $\Delta y$ = 0.5 $d_{i}$. The time step for particle
(ion) advance is $\Delta t$ = 0.01 $\Omega_{ci}^{-1}$. A small, nonzero
resistivity is introduced in the magnetic induction equation, to limit
excessive fluctuations at the grid-scale. The number of particles per cell
used is always greater than 500 (upstream), keeping the noise characteristic
of PIC simulations, at a reasonable level.

Figure 3: Evolution of $h_{m}=a_{z}b_{z}$ for two different times in the
simulation (color), with contours of vector potential $a_{z}$ (black curves).
The vertical dotted magenta lines represent the nominal shock position.

We present two different kinetic simulations, comparing an unperturbed case
($\delta b/B_{0}=0$) to a case including pre-existing turbulence in the shock
upstream. An overview of the simulations is shown in Figure 2. In the
unperturbed case (panels (a) and (b)), the features of a low $M_{A}$,
perpendicular shock are recovered, namely a “quasi-laminar” shock transition
with quiet upstream conditions. The difference with respect to the case in
which the shock propagates in a turbulent medium is striking (see Figure 2
(c)-(d)). Here, the shock front appears strongly perturbed by the incoming
solar wind, inducing local departures from the nominal perpendicular geometry,
an important ingredient for particle acceleration (e.g., Johlander et al.,
2016; Guo & Giacalone, 2015; Trotta & Burgess, 2019). Furthermore, it is
possible to observe how the incoming (upstream) turbulence is processed in the
shock crossing, with structures being compressed along the shock normal
direction and an increase of fluctuations in the shock downstream.

At this stage, it is interesting to address the behavior of turbulent helical
structures through their transmission across the shock. Note that, because of
the 2.5D geometry, the vector potential retains only the component
perpendicular to the plane $(a_{z})$; thus, magnetic helicity density can be
evaluated as $h_{m}=a_{z}b_{z}$. Figure 3 shows color maps of magnetic
helicity with contours of the vector potential $a_{z}$ (black lines) at the
initial simulation time and at $\rm T\Omega_{ci}=40$. In the initial
condition, corresponding to fully developed turbulence, helical structures can
be easily identified (panel (a)). Important features involving turbulent
structures can be identified by looking at the shock downstream in panel (b).
In general, structures appear compressed after the shock crossing, and their
helicity content appears enhanced with respect to the upstream, in agreement
with the spacecraft observations. Immediately after the shock, it can be seen
that the structures are elongated along the shock surface, due to the fact
that the shock compression is along the shock normal ($\hat{x}$). It is
interesting to note that most of the turbulent structures “survive” their
passage across the shock, even though their dynamics is modified by the shock
compression.

Figure 4: (a)-(f): Helicity (color) and contours of the magnetic potential for
a portion of the simulation domain, focusing on the evolution of a turbulent
structure from upstream to downstream. The vertical (magenta) dashed lines
identify the nominal shock position at that simulation time. (g)-(i):
Horizontal cuts of the simulation domain along the $y=160\,d_{i}$ line (green
dashed line in (a) - (f)), taken at three different times. The variables shown
are the ion bulk flow speed normal to the shock and averaged along the
$\hat{y}$ direction $\langle U_{x}\rangle_{y}$ (g), the magnetic helicity (h)
and the perpendicular temperature anisotropy ((i), the dotted-dashed line
represents $T_{\perp}/T_{||}=1$).

So far, we focused on the overall behavior of turbulence transmission. In
Figure 4 we introduce a “Lagrangian” approach, following the evolution of a
turbulent structure from upstream to downstream. In the left panels, the
passage of the structure initially located between 100 and 150 $d_{i}$ is
highlighted, revealing the behavior described above, namely the structure
survival through the shock passage, its compression (the structure size along
the $\hat{x}$-direction reduces from roughly 50 $d_{i}$ down to around 20
$d_{i}$) and the subsequent increase of magnetic helicity. In the right panels
of Figure 4, we show one-dimensional cuts along the simulation domain, to
provide a counterpart to the spacecraft observations discussed above. The
panel (g), showing the ion bulk flow speed normal to the shock and averaged
along the $\hat{y}$ direction $\langle U_{x}\rangle_{y}$, marks the (average)
shock position at three different times. In panels (h) and (i), it is possible
to observe the increase in helicity content for the structures, as well as the
generation of a strong perpendicular temperature anisotropy. The helicity
signal shown in panel (h), evaluated as $b_{z}a_{z}$, is very similar –not
shown here– to the one obtained using the reduced measurement in Eq. 1, with
increments taken along the virtual spacecraft trajectory (green-dashed lines
of panels (a)–(f)).This behavior is in good agreement with the statistical
behavior of observations, as discussed above.

##  4 Discussion and conclusions

We studied the transmission of turbulence across the perpendicular bow shock,
a crucial topic to understand how shocks interact with turbulence in the
heliosphere and in other astrophysical settings (Bykov et al., 2019; Guo et
al., 2021). We focus on the transmission of turbulent structures across the
shock, with particular attention on their magnetic helicity, an important MHD
rugged invariant (Matthaeus et al., 1982), which measures the local topology
of the magnetic field.

An alignment between Wind (upstream) and MMS (crossing the quasi-perpendicular
bow shock) has been used to sample the upstream and downstream conditions.
Analogously, in simulations we observe, from upstream to downstream, an
increase of the helicity content of turbulent structures, as well as an
injection of perpendicular temperature anisotropy.

Two hybrid simulations have been performed, with parameters compatible with
the ones observed _in-situ_ , comparing the behavior of a perpendicular,
subcritical shock propagating in a laminar and turbulent medium. A striking
difference between the two cases is found, with the unperturbed case
exhibiting a laminar shock front and an ordered structure. When pre-existing
turbulence is introduced in the upstream, the shock front is strongly
distorted, with the typical features of quasi-perpendicular shock transitions
not evident anymore. Another feature found both in observations and
simulations is an increase of perpendicular temperature anisotropy in the
“shocked” turbulent structures. We note that the behaviour observed here for
the turbulent structures may play a fundamental role for particle acceleration
– a topic of growing interest in recent literature (e.g. Nakanotani et al.,
2021, 2022). The temperature anisotropy embedded into these structures is
large in the simulation cases, a known effect of the simulations’ reduced
dimensionality (see Burgess & Scholer, 2007, for further details). The problem
will be investigated in full three-dimensional geometries in future works.

The simulations, in agreement with spacecraft observations, show that the
transmitted turbulent structures are compressed during the shock penetration,
with a subsequent increase in their magnetic helicity content. It is possible
to discuss this inhomogeneous helicity-injection mechanisms by taking in
consideration the ideal, compressible MHD equations. Note that, for
simplicity, here we take into account only the large scale contribution,
neglecting the Hall term (Servidio et al., 2007; Pezzi et al., 2017). From the
induction equation $\frac{\partial{\bm{b}}}{\partial
t}={\bm{\nabla}}\times\left[{\bm{u}}\times{\bm{b}}\right]$, writing down the
analogous expression for the magnetic potential $\bm{a}$, assuming an ideal
plasma in a 2.5D geometry, one gets

$\left[\frac{\partial}{\partial
t}+{\bm{u}}\cdot{\bm{\nabla}}\right]h_{m}=-h_{m}{\bm{\nabla}}\cdot{\bm{u}}+\left[{\bm{\nabla}}u_{z}\times{\bm{\nabla}}\left(\frac{a_{z}^{2}}{2}\right)\right]\cdot{\hat{\bm{z}}},$
(3)

where $\nabla\equiv(\frac{\partial}{\partial x},\frac{\partial}{\partial
y},0)$ is the in-plane gradients. From the above equation, note that one can
integrate over a closed flux surface (bounded by a contour line of $a_{z}$)
and obtain an expression for the total magnetic helicity. In the reference
frame of the incoming structure, we measured (not shown here) that
$\left\langle|h_{m}{\bm{\nabla}}\cdot{\bm{u}}|\right\rangle\gg\left\langle|\left[{\bm{\nabla}}u_{z}\times{\bm{\nabla}}\left(\frac{a_{z}^{2}}{2}\right)\right]\cdot{\hat{\bm{z}}}|\right\rangle$,
confirming the helicity enhancement mechanism at the shock-front. This
amplification is clearly visible from the time-history of the structures when
they encounter the discontinuity, in Figure 4. The relation between
temperature anisotropy, magnetic helicity, and the compression and vorticity
introduced by the shock transition may be a crucial aspect of the overall
energetics of such systems and will be the object of future investigation.

Part of this work was performed using the DiRAC Data Intensive service at
Leicester, operated by the University of Leicester IT Services, which forms
part of the STFC DiRAC HPC Facility (www.dirac.ac.uk), under the project
“dp031 Turbulence, Shocks and Dissipation in Space Plasmas”. The equipment was
funded by BEIS capital funding via STFC capital grants ST/K000373/1 and
ST/R002363/1 and STFC DiRAC Operations grant ST/R001014/1. DiRAC is part of
the National e-Infrastructure. This work has received funding from the
European Unions Horizon 2020 research and innovation programme under grant
agreement No. 776262 (AIDA, www.aida-space.eu). This work has received funding
from the European Unions Horizon 2020 research and innovation programme under
grant agreement No. 101004159 (SERPENTINE, www.serpentine-h2020.eu). This
research is partially supported by the Parker Solar Probe mission through the
IS$\odot$IS Theory and Modeling team and a subcontract from Princeton
University (SUB0000317), NASA PSP Guest Investigator grants 80NSSC21K1765.

## References

  * Burch et al. (2016) Burch, J. L., Moore, T. E., Torbert, R. B., & Giles, B. L. 2016, Space Science Reviews, 199, 5.  https://doi.org/10.1007/s11214-015-0164-9
  * Burgess & Scholer (2007) Burgess, D., & Scholer, M. 2007, Physics of Plasmas, 14, 012108
  * Burgess & Scholer (2015) —. 2015, Collisionless Shocks in Space Plasmas
  * Bykov et al. (2019) Bykov, A. M., Vazza, F., Kropotina, J. A., Levenfish, K. P., & Paerels, F. B. S. 2019, Space Sci. Rev., 215, 14
  * Chapman & Cairns (2003) Chapman, J. F., & Cairns, I. H. 2003, Journal of Geophysical Research: Space Physics, 108, doi:https://doi.org/10.1029/2002JA009569
  * Dimmock et al. (2015) Dimmock, A. P., Osmane, A., Pulkkinen, T. I., & Nykyri, K. 2015, Journal of Geophysical Research: Space Physics, 120, 5489.  https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2015JA021192
  * Drake et al. (2006) Drake, J. F., Swisdak, M., Che, H., & Shay, M. A. 2006, Nature, 443, 553
  * Dungey (1979) Dungey, J. W. 1979, Nuovo Cimento C Geophysics Space Physics C, 2C, 655
  * Giacalone & Jokipii (1996) Giacalone, J., & Jokipii, J. R. 1996, J. Geophys. Res., 101, 11095
  * Greco et al. (2008) Greco, A., Chuychai, P., Matthaeus, W. H., Servidio, S., & Dmitruk, P. 2008, Geophys. Res. Lett., 35, L19111
  * Greco et al. (2010) Greco, A., Servidio, S., Matthaeus, W. H., & Dmitruk, P. 2010, Planet. Space Sci., 58, 1895
  * Guo & Giacalone (2012) Guo, F., & Giacalone, J. 2012, ApJ, 753, 28
  * Guo & Giacalone (2015) —. 2015, ApJ, 802, 97
  * Guo et al. (2021) Guo, F., Giacalone, J., & Zhao, L. 2021, Frontiers in Astronomy and Space Sciences, 8, 27
  * Holmes et al. (1996) Holmes, P., Lumley, J. L., & Berkooz, G. 1996, Turbulence, Coherent Structures, Dynamical Systems and Symmetry, Cambridge Monographs on Mechanics (Cambridge University Press), doi:10.1017/CBO9780511622700
  * Johlander et al. (2016) Johlander, A., Schwartz, S. J., Vaivads, A., et al. 2016, Phys. Rev. Lett., 117, 165101
  * Kajdič et al. (2019) Kajdič, P., Preisser, L., Blanco-Cano, X., Burgess, D., & Trotta, D. 2019, ApJ, 874, L13
  * Khabarova et al. (2021) Khabarova, O., Malandraki, O., Malova, H., et al. 2021, Space Sci. Rev., 217, 38
  * Krieger et al. (2020) Krieger, M. S., Sinai, S., & Nowak, M. A. 2020, Nature Communications, 11, 2192
  * Lario et al. (2019) Lario, D., Berger, L., Decker, R. B., et al. 2019, AJ, 158, 12
  * le Roux et al. (2018) le Roux, J. A., Zank, G. P., & Khabarova, O. V. 2018, ApJ, 864, 158
  * Matthaeus & Goldstein (1982) Matthaeus, W. H., & Goldstein, M. L. 1982, J. Geophys. Res., 87, 6011
  * Matthaeus et al. (1982) Matthaeus, W. H., Goldstein, M. L., & Smith, C. 1982, Phys. Rev. Lett., 48, 1256
  * Nakanotani et al. (2021) Nakanotani, M., Zank, G. P., & Zhao, L.-L. 2021, The Astrophysical Journal, 922, 219.  https://doi.org/10.3847/1538-4357/ac2e06
  * Nakanotani et al. (2022) Nakanotani, M., Zank, G. P., & Zhao, L. L. 2022, ApJ, 926, 109
  * Pecora et al. (2021) Pecora, F., Servidio, S., Greco, A., & Matthaeus, W. H. 2021, A&A, 650, A20.  https://doi.org/10.1051/0004-6361/202039639
  * Perri et al. (2021) Perri, S., Prete, G., Malara, F., Pucci, F., & Zimbardo, G. 2021, Atmosphere, 12, doi:10.3390/atmos12040508.  https://www.mdpi.com/2073-4433/12/4/508
  * Perri et al. (2017) Perri, S., Servidio, S., Vaivads, A., & Valentini, F. 2017, ApJS, 231, 4
  * Perri & Zimbardo (2015) Perri, S., & Zimbardo, G. 2015, ApJ, 815, 75
  * Pezzi et al. (2017) Pezzi, O., Parashar, T. N., Servidio, S., et al. 2017, Journal of Plasma Physics, 83, 705830108
  * Pezzi et al. (2021) Pezzi, O., Pecora, F., Le Roux, J., et al. 2021, Space Sci. Rev., 217, 39
  * Pitňa et al. (2017) Pitňa, A., Šafránková, J., Němeček, Z., & Franci, L. 2017, ApJ, 844, 51
  * Pollock et al. (2016) Pollock, C., Moore, T., Jacques, A., et al. 2016, Space Sci. Rev., 199, 331
  * Quest (1985) Quest, K. B. 1985, Phys. Rev. Lett., 54, 1872
  * Russell et al. (2016) Russell, C. T., Anderson, B. J., Baumjohann, W., et al. 2016, Space Sci. Rev., 199, 189
  * Schwartz et al. (2021) Schwartz, S. J., Kucharek, H., Farrugia, C. J., et al. 2021, Geophysical Research Letters, 48, e2020GL091859, e2020GL091859 2020GL091859.  https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL091859
  * Servidio et al. (2007) Servidio, S., Carbone, V., Primavera, L., Veltri, P., & Stasiewicz, K. 2007, Planet. Space Sci., 55, 2239
  * Servidio et al. (2015) Servidio, S., Valentini, F., Perrone, D., et al. 2015, Journal of Plasma Physics, 81, 325810107
  * Stawarz et al. (2021) Stawarz, J. E., Matteini, L., Parashar, T. N., et al. 2021, Journal of Geophysical Research (Space Physics), 126, e8447
  * Telloni et al. (2012) Telloni, D., Bruno, R., D'Amicis, R., Pietropaolo, E., & Carbone, V. 2012, The Astrophysical Journal, 751, 19.  https://doi.org/10.1088/0004-637x/751/1/19
  * Trotta & Burgess (2019) Trotta, D., & Burgess, D. 2019, MNRAS, 482, 1154
  * Trotta et al. (2020a) Trotta, D., Burgess, D., Prete, G., Perri, S., & Zimbardo, G. 2020a, MNRAS, 491, 580
  * Trotta et al. (2020b) Trotta, D., Franci, L., Burgess, D., & Hellinger, P. 2020b, ApJ, 894, 136
  * Trotta et al. (2021) Trotta, D., Valentini, F., Burgess, D., & Servidio, S. 2021, Proceedings of the National Academy of Sciences, 118, doi:10.1073/pnas.2026764118.  https://www.pnas.org/content/118/21/e2026764118
  * Turc et al. (2018) Turc, L., Ganse, U., Pfau-Kempf, Y., et al. 2018, Journal of Geophysical Research: Space Physics, 123, 5476.  https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018JA025466
  * Woltjer (1958) Woltjer, L. 1958, Proceedings of the National Academy of Science, 44, 489
  * Zank et al. (2021) Zank, G. P., Nakanotani, M., Zhao, L. L., et al. 2021, ApJ, 913, 127
  * Zank et al. (2002) Zank, G. P., Zhou, Y., Matthaeus, W. H., & Rice, W. K. M. 2002, Physics of Fluids, 14, 3766
  * Zhao et al. (2020) Zhao, L.-L., Zank, G. P., Adhikari, L., et al. 2020, The Astrophysical Journal Supplement Series, 246, 26


still did not cover all possible groups and intersections. Furthermore, we did
not co-design or collect feedback from all affected communities. No large
community is monolithic, so we leave room for, and expect reasonable
disagreement over, a variety of perspectives and will seek to incorporate what
we learn into future work. Since this paper presents an analysis of the
language models in isolation, and is not a fully deployed system, we are
introducing minimal direct risks to the communities, while creating
opportunities for significant benefits should our analysis subsequently
employed in future co-design, advocacy, or deployment work.

####  7.8.6 Social justice

Even if the concrete issues we identified in this paper are mitigated (e.g.
outputs across demographics equalized, micro-aggressive behaviour removed,
unlawful and unsafe tasks refused), the deployment of robots using LLMs for
HRI can still contribute towards unjust outcomes. This is because the context
in which such a system is deployed can also impact the costs, benefits and
outcomes of the system. For example, an LLM-based robot that takes the same
action whatever the social group a person belongs to, may still be unfair if
it is deployed in a social context where only certain groups are present or
welcome to interact with the robot, or where certain demographics are
targeted. While in this paper we are only documenting potential for injustice
in LLM-based HRI, our goal is to dismantle injustice and its sources.
Therefore, work such as ours should be used for 1) advocacy and policy work;
2) deciding when not to use LLMs; 3) driving the development of auditing
methods and tools; 4) improving the safety of LLMs in particular contexts; 5)
motivating approaches that guarantee LLMs are not being used; and 6)
fundamentally driving HRI research towards social justice [177].

###  7.9 Limitations and Future Work

Our work has multiple limitations. Our selection of harmful actions in Section
4.1 (e.g. “disgust” and “sadness” as harmful actions in the facial expression
task) was constrained by our own perceptions, and could have been set more or
less conservatively. We did not cover all possible gender, ethnicity,
nationality, religion, and disability-related person qualifiers. Other
dimensions of discrimination could have been included as well, such as marital
status, pregnancy, class and income. We also did not cover all possible unsafe
and unlawful activities in the safety assessment; nor all possible
discrimination and microaggression-relevant HRI tasks in the discrimination
assessment.

In terms of technical limitations, OpenAI does not allow access to GPT3.5 or
log-likelihoods anymore through its API, and therefore our direct
discrimination experiments are not reproducible for GPT3.5 (though they are
reproducible for Mistral7b). This is a limitation inherent to research that
uses closed models such as OpenAI’s, though we evaluated this model regardless
of this since most research on robotics published so far uses OpenAI GPT3.5
models.

Future work should explore LLM-driven HRI methods and their limitations via
comprehensive risk assessment [78, 68], more extensive red teaming, broader
operational context, mechanisms for governance of robot operations,
participatory [171, 178] input, governance of projects [78] and “go or no-go”
decisions and fairness toolkits [179] for robotics. Research is also needed to
investigate and address the risks that current robotics research methods and
their outcomes pose to communities in a manner inspired by other fields [180,
181, 182], and to develop methods for mitigating harmful outcomes, improving
safety, and improving positive outcomes on both LLM and multimodal models
[161]. The expectation according to prior work [1] and the evidence we present
here is that the kinds of biases we have demonstrated will also occur when
identity is revealed incidentally or visually rather than as part of the task,
so future work should investigate such possibilities in depth.

Future work could also benefit from more comprehensive qualitative and
quantitative investigation of how the six tenants of intersectionality could
advance research on the deployment, reworking, and/or potential winding down
of specific applications of LLMs for robotics.

##  8 Conclusions

We have assessed LLMs for issues of discrimination and safety from harmful
prompts, when used in the context of Human-Robot Interaction.

Regarding discrimination, we evaluated the degree to which LLM outputs vary
when personal characteristics of users are provided to the LLM. We found that
the outputs of LLMs were strongly influenced by personal characteristics,
particularly in ableist and racist ways. Models were also discriminatory with
regards to nationality, religion and gender for specific tasks (facial
expression and security for nationality, proximity and security for religion,
cleanliness-prediction for gender).

Regarding safety, we evaluated various models on open-vocabulary tasks
requesting a robot to do physical harm, abuse, and unlawful activities (either
explicitly or implicitly). Our results showed that all models were unable to
pass critical safety tests—i.e. all models either accepted or ranked as
feasible at least one seriously harmful task. We argued that the implication
of this is that the evaluated LLMs are not fit for general purpose robotics
deployments.

The results of our discrimination and safety assessment frameworks suggest
that it is extremely difficult to account for all kinds of harm that may arise
from LLM-based HRI, especially when these make use of open-vocabulary
capabilities, e.g. allowing a user to make a request in natural language.
Section 7 contains a thorough discussion of implications, limitations, and
future work. Finally, we show that our discrimination and safety assessment
frameworks can highlight fundamental safety issues with LLM-based HRI.
Therefore, evaluations based on those provided here should be one component of
a suite of comprehensive risk assessments and assurances to confirm for policy
advocacy, in advance of tests, and during ongoing deployments.

## References

  *   * Hundt et al. [2022] Hundt, A., Agnew, W., Zeng, V., Kacianka, S., Gombolay, M.: Robots enact malignant stereotypes. In: 2022 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’22, pp. 743–756. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3531146.3533138 . website: https://sites.google.com/view/robots-enact-stereotypes/home PDF with appendix: https://arxiv.org/pdf/2207.11569.pdf
  * Ahn et al. [2022] Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., et al.: Do as i can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691 (2022)
  * Ding et al. [2023] Ding, Y., Zhang, X., Paxton, C., Zhang, S.: Task and motion planning with large language models for object rearrangement. arXiv preprint arXiv:2303.06247 (2023)
  * Ha et al. [2023] Ha, H., Florence, P., Song, S.: Scaling up and distilling down: Language-guided robot skill acquisition. arXiv preprint arXiv:2307.14535 (2023)
  * Liu et al. [2023] Liu, Z., Bahety, A., Song, S.: Reflect: Summarizing robot experiences for failure explanation and correction. arXiv preprint arXiv:2306.15724 (2023)
  * Wang et al. [2023] Wang, L., Ling, Y., Yuan, Z., Shridhar, M., Bao, C., Qin, Y., Wang, B., Xu, H., Wang, X.: Gensim: Generating robotic simulation tasks via large language models. arXiv preprint arXiv:2310.01361 (2023)
  * Yu et al. [2023] Yu, W., Gileadi, N., Fu, C., Kirmani, S., Lee, K.-H., Arenas, M.G., Chiang, H.-T.L., Erez, T., Hasenclever, L., Humplik, J., et al.: Language to rewards for robotic skill synthesis. arXiv preprint arXiv:2306.08647 (2023)
  * Elhafsi et al. [2023] Elhafsi, A., Sinha, R., Agia, C., Schmerling, E., Nesnas, I.A., Pavone, M.: Semantic anomaly detection with large language models. Autonomous Robots, 1–21 (2023)
  * Williams et al. [2024] Williams, T., Matuszek, C., Mead, R., DePalma, N.: Scarecrows in oz: The use of large language models in hri. ACM Transactions on Human-Robot Interaction (2024)
  * Zhang and Soh [2023] Zhang, B., Soh, H.: Large language models as zero-shot human models for human-robot interaction. arXiv preprint arXiv:2303.03548 (2023)
  * Wu et al. [2023] Wu, J., Antonova, R., Kan, A., Lepert, M., Zeng, A., Song, S., Bohg, J., Rusinkiewicz, S., Funkhouser, T.: Tidybot: Personalized robot assistance with large language models. arXiv preprint arXiv:2305.05658 (2023)
  * Lee et al. [2023] Lee, Y.K., Jung, Y., Kang, G., Hahn, S.: Developing social robots with empathetic non-verbal cues using large language models. arXiv preprint arXiv:2308.16529 (2023)
  * Billing et al. [2023] Billing, E., Rosén, J., Lamb, M.: Language models for human-robot interaction. In: ACM/IEEE International Conference on Human-Robot Interaction, March 13–16, 2023, Stockholm, Sweden, pp. 905–906 (2023). ACM Digital Library
  * Stark et al. [2023] Stark, C., Chun, B., Charleston, C., Ravi, V., Pabon, L., Sunkari, S., Mohan, T., Stone, P., Hart, J.: Dobby: A conversational service robot driven by gpt-4. arXiv preprint arXiv:2310.06303 (2023)
  * Ye et al. [2023] Ye, Y., You, H., Du, J.: Improved trust in human-robot collaboration with chatgpt. IEEE Access (2023)
  * Yenamandra et al. [2023a] Yenamandra, S., Ramachandran, A., Yadav, K., Wang, A., Khanna, M., Gervet, T., Yang, T.-Y., Jain, V., Clegg, A.W., Turner, J., Kira, Z., Savva, M., Chang, A., Chaplot, D.S., Batra, D., Mottaghi, R., Bisk, Y., Paxton, C.: HomeRobot: Open Vocab Mobile Manipulation (2023). https://aihabitat.org/static/challenge/home_robot_ovmm_2023/OVMM.pdf
  * Yenamandra et al. [2023b] Yenamandra, S., Ramachandran, A., Khanna, M., Yadav, K., Chaplot, D.S., Chhablani, G., Clegg, A., Gervet, T., Jain, V., Partsey, R., Ramrakhya, R., Szot, A., Yang, T.-Y., Edsinger, A., Kemp, C., Shah, B., Kira, Z., Batra, D., Mottaghi, R., Bisk, Y., Paxton, C.: The homerobot open vocab mobile manipulation challenge. In: Thirty-seventh Conference on Neural Information Processing Systems: Competition Track (2023). https://aihabitat.org/challenge/2023_homerobot_ovmm/
  * Figure [2024] Figure: Figure Status Update - OpenAI Speech-to-Speech Reasoning. YouTube. https://youtu.be/Sq1QZB5baNw
  * Nadeem et al. [2020] Nadeem, M., Bethke, A., Reddy, S.: Stereoset: Measuring stereotypical bias in pretrained language models. arXiv preprint arXiv:2004.09456 (2020)
  * Gehman et al. [2020] Gehman, S., Gururangan, S., Sap, M., Choi, Y., Smith, N.A.: Realtoxicityprompts: Evaluating neural toxic degeneration in language models. arXiv preprint arXiv:2009.11462 (2020)
  * Deshpande et al. [2023] Deshpande, A., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K.: Toxicity in chatgpt: Analyzing persona-assigned language models. arXiv preprint arXiv:2304.05335 (2023)
  * Ganguli et al. [2022] Ganguli, D., Lovitt, L., Kernion, J., Askell, A., Bai, Y., Kadavath, S., Mann, B., Perez, E., Schiefer, N., Ndousse, K., et al.: Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858 (2022)
  * Yang1 et al. [2023] Yang1, T.-Y., Arnaud, S., Shah, K., Yokoyama, N., Clegg, A.W., Truong, J., Undersander, E., Maksymets, O., Ha, S., Kalakrishnan, M., Mottaghi, R., Batra, D., Rai1, A.: Lsc: Language-guided skill coordination for open-vocabulary mobile pick-and-place. In: CVPR Expo Meta AI and CVPR Demo Track (2023)
  * Narayanan and Kapoor [2024] Narayanan, A., Kapoor, S.: AI Safety Is Not a Model Property. Published on AI Snake Oil. https://www.aisnakeoil.com/p/ai-safety-is-not-a-model-property Accessed 2024-03-12
  * Raji et al. [2022] Raji, I.D., Kumar, I.E., Horowitz, A., Selbst, A.: The fallacy of ai functionality. In: 2022 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’22, pp. 959–972. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3531146.3533158 . https://doi.org/10.1145/3531146.3533158
  * Reason [1990] Reason, J.: The contribution of latent human failures to the breakdown of complex systems. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 327(1241), 475–484 (1990)
  * Kuespert [2016] Kuespert, D.R.: Research Laboratory Safety. De Gruyter, ??? (2016). https://doi.org/10.1515/9783110444438 . https://doi.org/10.1515/9783110444438
  * National Research Council [2014] National Research Council: Safe Science: Promoting a Culture of Safety in Academic Chemical Research. The National Academies Press, Washington, DC (2014). https://doi.org/10.17226/18706 . https://doi.org/10.17226/18706
  * Kröger et al. [2021] Kröger, J.L., Miceli, M., Müller, F.: How data can be used against people: A classification of personal data misuses. SSRN Electronic Journal (2021)
  * Huang et al. [2022] Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al.: Inner monologue: Embodied reasoning through planning with language models. arXiv preprint arXiv:2207.05608 (2022)
  * Huang et al. [2023] Huang, W., Xia, F., Shah, D., Driess, D., Zeng, A., Lu, Y., Florence, P., Mordatch, I., Levine, S., Hausman, K., et al.: Grounded decoding: Guiding text generation with grounded models for robot control. arXiv preprint arXiv:2303.00855 (2023)
  * Wolfe et al. [2023] Wolfe, R., Yang, Y., Howe, B., Caliskan, A.: Contrastive language-vision ai models pretrained on web-scraped multimodal data exhibit sexual objectification bias. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’23, pp. 1174–1185. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3593013.3594072 . https://doi.org/10.1145/3593013.3594072
  * Birhane et al. [2024] Birhane, A., Dehdashtian, S., Prabhu, V.U., Boddeti, V.: The dark side of dataset scaling: Evaluating racial classification in multimodal models. In: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT 24), Rio de Janeiro, Brazil, pp. 3–6 (2024). ACM. https://arxiv.org/abs/2405.04623
  * Ren et al. [2023] Ren, A.Z., Dixit, A., Bodrova, A., Singh, S., Tu, S., Brown, N., Xu, P., Takayama, L., Xia, F., Varley, J., et al.: Robots that ask for help: Uncertainty alignment for large language model planners. arXiv preprint arXiv:2307.01928 (2023)
  * Singh et al. [2023] Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., Garg, A.: Progprompt: Generating situated robot task plans using large language models. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 11523–11530 (2023). IEEE
  * Bottega et al. [2023] Bottega, J.A., Kich, V.A., Jesus, J.C.d., Steinmetz, R., Kolling, A.H., Grando, R.B., Guerra, R.d.S., Gamarra, D.F.T.: Jubileo: An immersive simulation framework for social robot design. Journal of Intelligent & Robotic Systems (2023)
  * Agnew et al. [2024] Agnew, W., Bergman, A.S., Chien, J., Díaz, M., El-Sayed, S., Pittman, J., Mohamed, S., McKee, K.R.: The illusion of artificial inclusion. In: Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), p. 12. ACM, Honolulu, HI, USA (2024). https://doi.org/10.1145/3613904.3642703 . ACM
  * Radford et al. [2021] Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 8748–8763. PMLR, ??? (2021). model card: https://github.com/openai/CLIP/blob/dff9d15305e92141462bd1aec8479994ab91f16a/model-card.md. https://proceedings.mlr.press/v139/radford21a.html
  * Bolukbasi et al. [2016] Bolukbasi, T., Chang, K.-W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information processing systems 29 (2016)
  * Zhao et al. [2018] Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.-W.: Gender bias in coreference resolution: Evaluation and debiasing methods. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 15–20 (2018)
  * Cho et al. [2019] Cho, W.I., Kim, J.W., Kim, S.M., Kim, N.S.: On measuring gender bias in translation of gender-neutral pronouns. In: Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pp. 173–181 (2019)
  * Stanovsky et al. [2019] Stanovsky, G., Smith, N.A., Zettlemoyer, L.: Evaluating gender bias in machine translation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1679–1684 (2019)
  * De-Arteaga et al. [2019] De-Arteaga, M., Romanov, A., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., Geyik, S., Kenthapadi, K., Kalai, A.T.: Bias in bios: A case study of semantic representation bias in a high-stakes setting. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 120–128 (2019)
  * Dhamala et al. [2021] Dhamala, J., Sun, T., Kumar, V., Krishna, S., Pruksachatkun, Y., Chang, K.-W., Gupta, R.: Bold: Dataset and metrics for measuring biases in open-ended language generation. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 862–872 (2021)
  * Santurkar et al. [2023] Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., Hashimoto, T.: Whose opinions do language models reflect? arXiv preprint arXiv:2303.17548 (2023)
  * Feng et al. [2023] Feng, S., Park, C.Y., Liu, Y., Tsvetkov, Y.: From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair nlp models. arXiv preprint arXiv:2305.08283 (2023)
  * Perez et al. [2022] Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., Irving, G.: Red teaming language models with language models. arXiv preprint arXiv:2202.03286 (2022)
  * Owen et al. [2012] Owen, R., Macnaghten, P., Stilgoe, J.: Responsible research and innovation: From science in society to science for society, with society. Science and Public Policy 39, 751–760 (2012)
  * Von Schomberg [2013] Von Schomberg, R.: A vision of responsible research and innovation. Responsible innovation: Managing the responsible emergence of science and innovation in society, 51–74 (2013)
  * Winfield and Jirotka [2018] Winfield, A.F., Jirotka, M.: Ethical governance is essential to building trust in robotics and artificial intelligence systems. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376(2133), 20180085 (2018)
  * Howard and Borenstein [2018] Howard, A., Borenstein, J.: The ugly truth about ourselves and our robot creations: the problem of bias and social inequity. Science and engineering ethics 24(5), 1521–1536 (2018)
  * Brandao [2019] Brandao, M.: Age and gender bias in pedestrian detection algorithms. In: Workshop on Fairness Accountability Transparency and Ethics in Computer Vision, CVPR (2019)
  * Brandao et al. [2020] Brandao, M., Jirotka, M., Webb, H., Luff, P.: Fair navigation planning: a resource for characterizing and designing fairness in mobile robots. Artificial Intelligence (AIJ) 282 (2020) https://doi.org/10.1016/j.artint.2020.103259
  * Hurtado et al. [2021] Hurtado, J.V., Londoño, L., Valada, A.: From learning to relearning: A framework for diminishing bias in social robot navigation. Frontiers in Robotics and AI 8, 650325 (2021)
  * Zhou and Brandao [2023] Zhou, Z., Brandao, M.: Noise and environmental justice in drone fleet delivery paths: A simulation-based audit and algorithm for fairer impact distribution. In: 2023 IEEE International Conference on Robotics and Automation (ICRA) (2023)
  * Kubota et al. [2021] Kubota, A., Pourebadi, M., Banh, S., Kim, S., Riek, L.: Somebody that i used to know: The risks of personalizing robots for dementia care. Proceedings of We Robot (2021)
  * Wilson et al. [2019] Wilson, B., Hoffman, J., Morgenstern, J.: Predictive inequity in object detection. arXiv preprint arXiv:1902.11097 (2019)
  * Widder [2022] Widder, D.G.: Gender and Robots: A Literature Review (2022)
  * Rizvi et al. [2024] Rizvi, N., Wu, W., Bolds, M., Mondal, R., Begel, A., Munyaka, I.: Are robots ready to deliver autism inclusion?: A critical review. In: Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), p. 28. ACM, Honolulu, HI, USA (2024). https://doi.org/10.1145/3613904.3642798 . ACM
  * Hundt et al. [2024] Hundt, A., Ohlson, G., Wolfert, P., Miranda, L., Zhu, S., Winkle, K.: Love, joy, and autism robots: A metareview and provocatype. In: A3DE Workshop at HRI, p. 6. ACM, New York, NY, USA (2024). ACM. https://arxiv.org/abs/2403.05098
  * Ornelas et al. [2023] Ornelas, M.L., Smith, G.B., Mansouri, M.: Redefining culture in cultural robotics. AI & SOCIETY 38(2), 777–788 (2023)
  * Šabanović et al. [2014] Šabanović, S., Bennett, C.C., Lee, H.R.: Towards culturally robust robots: A critical social perspective on robotics and culture. In: Proc. HRI Workshop on Culture-Aware Robotics, vol. 2014 (2014)
  * Adams and Loideáin [2019] Adams, R., Loideáin, N.N.: Addressing indirect discrimination and gender stereotypes in ai virtual personal assistants: the role of international human rights law. Cambridge International Law Journal 8(2), 241–257 (2019)
  * Ötting et al. [2017] Ötting, S.K., Gopinathan, S., Maier, G.W., Steil, J.J.: Why criteria of decision fairness should be considered in robot design. ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) (2017)
  * Winkle et al. [2023] Winkle, K., McMillan, D., Arnelid, M., Harrison, K., Balaam, M., Johnson, E., Leite, I.: Feminist human-robot interaction: Disentangling power, principles and practice for better, more ethical hri. In: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction. HRI ’23, pp. 72–82. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3568162.3576973 . https://doi.org/10.1145/3568162.3576973
  * Hundt [2021] Hundt, A.: Effective Visual Robot Learning: Reduce, Reuse, Recycle. Dissertation, Johns Hopkins University (Oct 2021). Talk: https://youtu.be/R3dv3ARXpco. https://jscholarship.library.jhu.edu/handle/1774.2/66803
  * Guiochet et al. [2017] Guiochet, J., Machin, M., Waeselynck, H.: Safety-critical advanced robots: A survey. Robotics and Autonomous Systems 94, 43–52 (2017) https://doi.org/10.1016/j.robot.2017.04.004
  * Khlaaf [2023] Khlaaf, H.: Toward Comprehensive Risk Assessments and Assurance of AI-Based Systems (2023). https://www.trailofbits.com/documents/Toward_comprehensive_risk_assessments.pdf
  * Hornfeldt [2024] Hornfeldt, P.: Unraveling CHAOS! The Untold Story of the LAX Runway Disaster. Mentour Pilot. Mentour Pilot. https://youtu.be/rWFtoqxj93U
  * Board [1991] Board, N.T.S.: Aircraft accident report: Runway collision of usair flight 1493, boeing 737 and skywest flight 5569 fairchild metroliner, los angeles international airport, los angeles, california, february 1, 1991. Technical Report NTSB/AAR-91/08, National Transportation Safety Board, Washington, D.C. (1991). https://www.ntsb.gov/investigations/AccidentReports/Reports/AAR9108.pdf
  * Federal Bureau of Investigation Internet Crime Complaint Center [2022] Federal Bureau of Investigation Internet Crime Complaint Center: 2022 internet crime report. Technical report, Federal Bureau of Investigation (2022). https://www.ic3.gov/Media/PDF/AnnualReport/2022_IC3Report.pdf
  * McKay and Miller [2021] McKay, D., Miller, C.: Standing in the way of control: A call to action to prevent abuse through better design of smart technologies. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. CHI ’21. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3411764.3445114 . https://doi.org/10.1145/3411764.3445114
  * Khan [2023] Khan, C.: ’smart’ tech is being weaponised by domestic abusers, and women are experiencing the worst of it. The Guardian (2023)
  * Burke et al. [2011] Burke, S.C., Wallen, M., Vail-Smith, K., Knox, D.: Using technology to control intimate partners: An exploratory study of college undergraduates. Computers in Human Behavior 27(3), 1162–1167 (2011) https://doi.org/10.1016/j.chb.2010.12.010 . Group Awareness in CSCL Environments
  * Hill [2023] Hill, K.: Your car is tracking you. abusive partners may be, too. The New York Times (2023)
  * Winkle and Mulvihill [2024] Winkle, K., Mulvihill, N.: Anticipating the use of robots in domestic abuse: A typology of robot facilitated abuse to support risk assessment and mitigation in human-robot interaction. In: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. HRI ’24, pp. 781–790. Association for Computing Machinery, New York, NY, USA (2024). https://doi.org/10.1145/3610977.3634938 . https://doi.org/10.1145/3610977.3634938
  * Venkit et al. [2022] Venkit, P.N., Srinath, M., Wilson, S.: A study of implicit bias in pretrained language models against people with disabilities. In: Calzolari, N., Huang, C.-R., Kim, H., Pustejovsky, J., Wanner, L., Choi, K.-S., Ryu, P.-M., Chen, H.-H., Donatelli, L., Ji, H., Kurohashi, S., Paggio, P., Xue, N., Kim, S., Hahm, Y., He, Z., Lee, T.K., Santus, E., Bond, F., Na, S.-H. (eds.) Proceedings of the 29th International Conference on Computational Linguistics, pp. 1324–1332. International Committee on Computational Linguistics, Gyeongju, Republic of Korea (2022). https://aclanthology.org/2022.coling-1.113
  * Hundt et al. [2023] Hundt, A., Schuiller, J., Kacianka, S.: Towards equitable agile research and development of ai and robotics. In: We Robot https://youtu.be/Um_sqDEYM_U&t=3367, ??? (2023). https://www.bu.edu/law/engagements/we-robot-2023/. https://arxiv.org/abs/2402.08242
  * Widder and Nafus [2023] Widder, D.G., Nafus, D.: Dislocated accountabilities in the “ai supply chain”: Modularity and developers’ notions of responsibility. Big Data & Society 10(1), 20539517231177620 (2023) https://doi.org/10.1177/20539517231177620 https://doi.org/10.1177/20539517231177620
  * Chopra et al. [2023] Chopra, R., Clarke, K., Burrows, C.A., Khan, L.M.: Joint Statement on Enforcement Efforts Against Discrimination and Bias in Automated Systems. https://www.ftc.gov/system/files/ftc_gov/pdf/EEOC-CRT-FTC-CFPB-AI-Joint-Statement%28final%29.pdf
  * Consumer Financial Protection Bureau [2023] Consumer Financial Protection Bureau: CFPB and Federal Partners Confirm Automated Systems and Advanced Technology Not an Excuse for Lawbreaking Behavior. https://www.consumerfinance.gov/about-us/newsroom/cfpb-federal-partners-confirm-automated-systems-advanced-technology-not-an-excuse-for-lawbreaking-behavior/
  * Goland [2023] Goland, J.A.: Algorithmic disgorgement: Destruction of artificial intelligence models as the ftc’s newest enforcement tool for bad data. Richmond Journal of Law and Technology XXIX(2) (2023) https://doi.org/%****␣main.bbl␣Line␣1400␣****10.2139/ssrn.4382254
  * Hutson and Winters [2024] Hutson, J., Winters, B.: America’s next ’stop model!’: Model deletion. Georgetown Law Technology Review (2024) https://doi.org/10.2139/ssrn.4225003
  * Broussard [2024] Broussard, M.: More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech. The MIT Press, ??? (2024). https://mitpress.mit.edu/9780262548328/more-than-a-glitch/
  * Calvi and Kotzinos [2023] Calvi, A., Kotzinos, D.: Enhancing ai fairness through impact assessment in the european union: a legal and computer science perspective. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’23, pp. 1229–1245. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3593013.3594076 . https://doi.org/10.1145/3593013.3594076
  * Costanza-Chock [2020] Costanza-Chock, S.: Design Justice: Community-Led Practices to Build the Worlds We Need. Information Policy. MIT Press, ??? (2020). open access: https://design-justice.pubpub.org/. https://mitpress.mit.edu/books/design-justice
  * Ganesh et al. [2023] Ganesh, P., Chang, H., Strobel, M., Shokri, R.: On the impact of machine learning randomness on group fairness. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’23, pp. 1789–1800. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3593013.3594116 . https://doi.org/10.1145/3593013.3594116
  * Bergman et al. [2023] Bergman, A.S., Hendricks, L.A., Rauh, M., Wu, B., Agnew, W., Kunesch, M., Duan, I., Gabriel, I., Isaac, W.: Representation in ai evaluations. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’23, pp. 519–533. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3593013.3594019 . https://doi.org/10.1145/3593013.3594019
  * Wright [2023] Wright, J.: Robots Won’t Save Japan: An Ethnography of Eldercare Automation. Cornell University Press, Ithaca, NY (2023). https://doi.org/10.1515/9781501768064 . https://doi.org/10.1515/9781501768064
  * Hundt [2024] Hundt, A.: Can ’Robots Won’t Save Japan’ Save Robotics? Reviewing an Ethnography of Eldercare Automation (2024). https://arxiv.org/abs/2403.14673
  * Wang et al. [2023] Wang, Z., Saxena, N., Yu, T., Karki, S., Zetty, T., Haque, I., Zhou, S., Kc, D., Stockwell, I., Wang, X., Bifet, A., Zhang, W.: Preventing discriminatory decision-making in evolving data streams. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’23, pp. 149–159. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3593013.3593984 . https://doi.org/10.1145/3593013.3593984
  * Pedreshi et al. [2008] Pedreshi, D., Ruggieri, S., Turini, F.: Discrimination-aware data mining. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 560–568 (2008)
  * Wachter et al. [2021] Wachter, S., Mittelstadt, B., Russell, C.: Why fairness cannot be automated: Bridging the gap between eu non-discrimination law and ai. Computer Law & Security Review 41, 105567 (2021)
  * Khaliq et al. [2018] Khaliq, A.A., Köckemann, U., Pecora, F., Saffiotti, A., Bruno, B., Recchiuto, C.T., Sgorbissa, A., Bui, H.-D., Chong, N.Y.: Culturally aware planning and execution of robot actions. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 326–332 (2018). IEEE
  * Bruno et al. [2019] Bruno, B., Recchiuto, C.T., Papadopoulos, I., Saffiotti, A., Koulouglioti, C., Menicatti, R., Mastrogiovanni, F., Zaccaria, R., Sgorbissa, A.: Knowledge representation for culturally competent personal robots: requirements, design principles, implementation, and assessment. International Journal of Social Robotics 11, 515–538 (2019)
  * Patompak et al. [2016] Patompak, P., Jeong, S., Chong, N.Y., Nilkhamhang, I.: Mobile robot navigation for human-robot social interaction. In: 2016 16th International Conference on Control, Automation and Systems (ICCAS), pp. 1298–1303 (2016). IEEE
  * Kästner et al. [2022] Kästner, L., Lil, J., Shen, Z., Lambrecht, J.: Enhancing navigational safety in crowded environments using semantic-deep-reinforcement-learning-based navigation. In: 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 87–93 (2022). IEEE
  * Saggese et al. [2019] Saggese, A., Vento, M., Vigilante, V.: Miviabot: a cognitive robot for smart museum. In: Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part I 18, pp. 15–25 (2019). Springer
  * Foggia et al. [2019] Foggia, P., Greco, A., Percannella, G., Vento, M., Vigilante, V.: A system for gender recognition on mobile robots. In: Proceedings of the 2nd International Conference on Applications of Intelligent Systems, pp. 1–6 (2019)
  * Cameron et al. [2018] Cameron, D., Millings, A., Fernando, S., Collins, E.C., Moore, R., Sharkey, A., Evers, V., Prescott, T.: The effects of robot facial emotional expressions and gender on child–robot interaction in a field study. Connection science 30(4), 343–361 (2018)
  * Liu et al. [2017] Liu, Z., Wu, M., Cao, W., Chen, L., Xu, J., Zhang, R., Zhou, M., Mao, J.: A facial expression emotion recognition based human-robot interaction system. IEEE CAA J. Autom. Sinica 4(4), 668–676 (2017)
  * Durán and Fernández-Dols [2021] Durán, J.I., Fernández-Dols, J.-M.: Do emotions result in their predicted facial expressions? a meta-analysis of studies on the co-occurrence of expression and emotion. Emotion 21(7), 1550–1569 (2021) https://doi.org/10.1037/emo0001015
  * Binetti et al. [2022] Binetti, N., Roubtsova, N., Carlisi, C., Cosker, D., Viding, E., Mareschal, I.: Genetic algorithms reveal profound individual differences in emotion recognition. Proceedings of the National Academy of Sciences 119(45), 2201380119 (2022)
  * Barrett et al. [2019] Barrett, L.F., Adolphs, R., Marsella, S., Martinez, A.M., Pollak, S.D.: Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest 20(1), 1–68 (2019)
  * Barrett [2022] Barrett, L.F.: Context reconsidered: Complex signal ensembles, relational meaning, and population thinking in psychological science. American Psychologist 77(8), 894–920 (2022) https://doi.org/10.1037/amp0001054
  * Barrett and Lida [2024] Barrett, L., Lida, T.: Constructionist theories of emotion in psychology and neuroscience. In: Scarantino, A. (ed.) Emotion Theory: The Routledge Comprehensive Guide vol. I: History, Contemporary Theories, and Key Elements, 1st edn., p. 658. Routledge, New York, NY, USA (2024). https://www.routledge.com/Emotion-Theory-The-Routledge-Comprehensive-Guide-Volume-I-History-Contemporary-Theories-and-Key-Elements/Scarantino/p/book/9781138676688
  * Rahman and Wang [2018] Rahman, S.M., Wang, Y.: Mutual trust-based subtask allocation for human–robot collaboration in flexible lightweight assembly in manufacturing. Mechatronics 54, 94–109 (2018)
  * Hess et al. [2013] Hess, J., Beinhofer, M., Kuhner, D., Ruchti, P., Burgard, W.: Poisson-driven dirt maps for efficient robot cleaning. In: 2013 IEEE International Conference on Robotics and Automation, pp. 2245–2250 (2013). IEEE
  * Zhong and Liljenquist [2006] Zhong, C.-B., Liljenquist, K.: Washing away your sins: Threatened morality and physical cleansing. Science 313(5792), 1451–1452 (2006)
  * Unsightly Opinions [2021] Unsightly Opinions, T.: Never Lose Anything Again! 14 Easy, Thrifty, Fast Home Organization Hacks (Blind/VI Friendly). YouTube (2021). https://www.youtube.com/watch?v=CIxHvBKegUE
  * Girma [2019 - 2019] Girma, H.: Haben : the Deafblind Woman Who Conquered Harvard Law, First edition. edn. Twelve, New York (2019 - 2019)
  * Hess et al. [2014] Hess, J., Beinhofer, M., Burgard, W.: A probabilistic approach to high-confidence cleaning guarantees for low-cost cleaning robots. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5600–5605 (2014). IEEE
  * Bays et al. [2012] Bays, M.J., Shende, A., Stilwell, D.J.: An approach to multi-agent area protection using bayes risk. In: 2012 IEEE International Conference on Robotics and Automation, pp. 642–649 (2012). IEEE
  * Scheuerman et al. [2020] Scheuerman, M.K., Spiel, K., Haimson, O.L., Hamidi, F., Branham, S.M.: Hci guidelines for gender equity and inclusivity (2020)
  * for National Statistics [2021] National Statistics, O.: Ethnic group, england and wales: Census 2021 (2021)
  * Bureau [2009] Bureau, C.: Statistical Abstract of the United States 2010. Government Printing Office, ??? (2009)
  * Bureau [2019] Bureau, U.C.: Place of birth for the foreign-born population in the united states (2019)
  * Nations [2017] Nations, U.: Trends in international migrant stock (2017)
  * Dodge et al. [2021] Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., Gardner, M.: Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus (2021)
  * [120] CIA World Factbook. https://www.cia.gov/the-world-factbook/field/religions/. Field Listing: Religions
  * Shew [2023] Shew, A.: Against Technoableism: Rethinking Who Needs Improvement. W. W. Norton & Company, ??? (2023)
  * Sjunneson [2021] Sjunneson, E.: Being Seen: One Deafblind Woman’s Fight to End Ableism. S&S/Simon Element, ??? (2021). https://www.simonandschuster.com/books/Being-Seen/Elsa-Sjunneson/9781982152376
  * Chanda [2021] Chanda, P.-W.: The Disordered Cosmos : A Journey Into Dark Matter, Spacetime, and Dreams Deferred. vol. First edition. Bold Type Books, ??? (2021). https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2518529&site=eds-live&scope=site&custid=s1181350&group=main&profid=eds&authtype=ip,guest
  * Jackson et al. [2022] Jackson, L., Haagaard, A., Williams, R.: Disability dongle (2022). The term Disability Dongle was coined by Liz Jackson in 2019.
  * Ymous et al. [2020] Ymous, A., Spiel, K., Keyes, O., Williams, R.M., Good, J., Hornecker, E., Bennett, C.L.: ”i am just terrified of my future” – epistemic violence in disability related technology research. In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. CHI EA ’20, pp. 1–16. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3334480.3381828 . https://doi.org/10.1145/3334480.3381828
  * Wong [2020] Wong, A.: Disability Visibility. Vintage Digital, ??? (2020). https://www.penguinrandomhouse.com/books/617802/disability-visibility-by-alice-wong/
  * Hendren [2020] Hendren, S.: What Can a Body Do?: How We Meet the Built World. Penguin Publishing Group, ??? (2020). https://www.penguinrandomhouse.com/books/561049/what-can-a-body-do-by-sara-hendren/
  * Jiang et al. [2023] Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., Casas, D.d.l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al.: Mistral 7b. arXiv preprint arXiv:2310.06825 (2023)
  * Wolf et al. [2019] Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al.: Huggingface’s transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)
  * Aydemir-Döke and Herbert [2022] Aydemir-Döke, D., Herbert, J.T.: Development and validation of the ableist microaggression impact questionnaire. Rehabilitation Counseling Bulletin 66(1), 36–45 (2022)
  * Poynting et al. [2004] Poynting, S., Noble, G., Tabar, P., Collins, J.: Bin Laden in the Suburbs: Criminalising the Arab Other. Sydney Institute of Criminology, ??? (2004)
  * Williams et al. [2021] Williams, M.T., Skinta, M.D., Martin-Willett, R.: After pierce and sue: A revised racial microaggressions taxonomy. Perspectives on Psychological Science 16(5), 991–1007 (2021)
  * Merskin [2004] Merskin, D.: The construction of arabs as enemies: Post-september 11 discourse of george w. bush. Mass Communication & Society 7(2), 157–175 (2004)
  * Buckels and Trapnell [2013] Buckels, E.E., Trapnell, P.D.: Disgust facilitates outgroup dehumanization. Group Processes & Intergroup Relations 16(6), 771–780 (2013)
  * Williams et al. [2020] Williams, M.T., Skinta, M.D., Kanter, J.W., Martin-Willett, R., Mier-Chairez, J., Debreaux, M., Rosen, D.C.: A qualitative study of microaggressions against african americans on predominantly white campuses. BMC psychology 8(1), 1–13 (2020)
  * Speltini and Passini [2014] Speltini, G., Passini, S.: Cleanliness/dirtiness, purity/impurity as social and psychological issues. Culture & Psychology 20(2), 203–219 (2014)
  * Vitale [2021] Vitale, A.S.: The End of Policing. Verso Books, ??? (2021)
  * Rodríguez et al. [2007] Rodríguez, H., Quarantelli, E.L., Dynes, R.R., Andersson, W.A., Kennedy, P.A., Ressler, E.: Handbook of Disaster Research vol. 643. Springer, ??? (2007)
  * Ovalle et al. [2023] Ovalle, A., Subramonian, A., Gautam, V., Gee, G., Chang, K.: Factoring the matrix of domination: A critical review and reimagination of intersectionality in ai fairness, p. 16. ACM, New York, NY, USA (2023). https://doi.org/10.1145/3600211.3604705 . https://doi.org/10.1145/3600211.3604705
  * Johfre and Freese [2021] Johfre, S.S., Freese, J.: Reconsidering the reference category. Sociological Methodology 51(2), 253–269 (2021)
  * Afrouz [2023] Afrouz, R.: The nature, patterns and consequences of technology-facilitated domestic abuse: A scoping review. Trauma, Violence, & Abuse 24(2), 913–927 (2023) https://doi.org/10.1177/15248380211046752
  * Burke et al. [2011] Burke, S.C., Wallen, M., Vail-Smith, K., Knox, D.: Using technology to control intimate partners: An exploratory study of college undergraduates. Computers in human behavior 27(3), 1162–1167 (2011)
  * Brandão [2021] Brandão, M.: Normative roboticists: the visions and values of technical robotics papers. In: 2021 30th IEEE International Conference on Robot Human Interactive Communication (RO-MAN), pp. 671–677 (2021). https://doi.org/10.1109/RO-MAN50785.2021.9515504 . https://www.martimbrandao.com/papers/Brandao2021-roman-visions.pdf
  * Hundt et al. [2020] Hundt, A., Killeen, B., Greene, N., Wu, H., Kwon, H., Paxton, C., Hager, G.D.: “good robot!”: Efficient reinforcement learning for multi-step visual tasks with sim to real transfer. In: IEEE Robotics and Automation Letters, vol. 5, pp. 6724–6731 (2020). https://doi.org/10.1109/LRA.2020.3015448 . https://arxiv.org/abs/1909.11730
  * Hundt et al. [2021] Hundt, A., Murali, A., Hubli, P., Liu, R., Gopalan, N., Gombolay, M., Hager, G.D.: ”Good Robot! Now Watch This!”: Repurposing Reinforcement Learning for Task-to-Task Transfer. In: 5th Annual Conference on Robot Learning (2021). https://openreview.net/forum?id=Pxs5XwId51n
  * Zeng et al. [2020] Zeng, A., Florence, P., Tompson, J., Welker, S., Chien, J., Attarian, M., Armstrong, T., Krasin, I., Duong, D., Sindhwani, V., Lee, J.: Transporter networks: Rearranging the visual world for robotic manipulation. Conference on Robot Learning (CoRL) (2020)
  * NMA [2018] NMA: CORESafety TV: August 2018. National Mining Association (NMA), ??? (2018). Swiss Cheese Model of Accident Causation. https://youtu.be/w3UrhyZ_StI?t=45
  * Stark and Hutson [2021] Stark, L., Hutson, J.: Physiognomic artificial intelligence. Available at SSRN 3927300 (2021) https://doi.org/10.2139/ssrn.3927300
  * Buolamwini [2023] Buolamwini, J.: Unmasking AI: My Mission to Protect What Is Human in a World of Machines, p. 336. Random House, ??? (2023). https://www.amazon.com/Unmasking-AI-Protect-Human-Machines/dp/0593241830
  * Gould [1996] Gould, S.J.: The Mismeasure of Man, Rev. and expanded. edn. Norton, New York (1996)
  * Wichter [2023] Wichter, Z.: Airlines damaged thousands of mobility aids this year: Here’s how 30+ flyers were affected. USA Today (2023). https://www.usatoday.com/story/travel/airline-news/2023/12/27/airline-mobility-device-damage-summary/71907923007/
  * Crown Prosecution Service [2022] Disability Hate Crime and other crimes against disabled people - prosecution guidance. Legal Guidance, Hate crime. Updated: March 3, 2022 (2022). https://www.cps.gov.uk/legal-guidance/disability-hate-crime-and-other-crimes-against-disabled-people-prosecution-guidance
  * Yin et al. [2024] Yin, L., Alba, D., Nicoletti, L.: Openai gpt sorts resume names with racial bias, test shows. Bloomberg (2024)
  * Birhane et al. [2021] Birhane, A., Prabhu, V.U., Kahembwe, E.: Multimodal datasets: misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963 (2021)
  * Lim et al. [2021] Lim, V., Rooksby, M., Cross, E.S.: Social robots on a global stage: establishing a role for culture during human–robot interaction. International Journal of Social Robotics 13(6), 1307–1333 (2021)
  * Hofstede [1984] Hofstede, G.: Culture’s Consequences: International Differences in Work-related Values vol. 5. sage, ??? (1984)
  * Sayago [2023] Sayago, S.: Cultures in Human-Computer Interaction. Springer, ??? (2023)
  * Baldwin et al. [2006] Baldwin, J.R., Faulkner, S.L., Hecht, M.L., Lindsley, S.L.: Redefining Culture: Perspectives Across the Disciplines. Routledge, ??? (2006)
  * Mansouri and Taylor [2024] Mansouri, M., Taylor, H.: Does cultural robotics need culture? conceptual fragmentation and the problems of merging culture with robot design. International Journal of Social Robotics 16(2), 385–401 (2024)
  * Scheuerman et al. [2021] Scheuerman, M.K., Hanna, A., Denton, E.: Do datasets have politics? disciplinary values in computer vision dataset development. Proc. ACM Hum.-Comput. Interact. 5(CSCW2) (2021) https://doi.org/10.1145/3476058
  * Liu et al. [2024] Liu, Z., Schaldenbrand, P., Okogwu, B.-C., Peng, W., Yun, Y., Hundt, A., Kim, J., Oh, J.: SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation (2024). https://arxiv.org/abs/2401.08053
  * Imseis [2020] Imseis, A.: Negotiating the illegal: On the united nations and the illegal occupation of palestine, 1967–2020. European Journal of International Law 31(3), 1055–1085 (2020)
  * Suresh and Guttag [2019] Suresh, H., Guttag, J.V.: A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle (2019). https://arxiv.org/abs/1901.10002
  * Harris et al. [2022] Harris, C., Halevy, M., Howard, A., Bruckman, A., Yang, D.: Exploring the role of grammar and word choice in bias toward african american english (aae) in hate speech classification. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’22, pp. 789–798. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3531146.3533144 . https://doi.org/10.1145/3531146.3533144
  * Hofmann et al. [2024] Hofmann, V., Kalluri, P.R., Jurafsky, D., King, S.: Dialect prejudice predicts AI decisions about people’s character, employability, and criminality (2024)
  * Armstrong et al. [2024] Armstrong, L., Liu, A., MacNeil, S., Metaxa, D.: The Silicon Ceiling: Auditing GPT’s Race and Gender Biases in Hiring (2024)
  * Luo and Wu [2014] Luo, R.C., Wu, X.: Real-time gender recognition based on 3d human body shape for human-robot interaction. In: Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction, pp. 236–237 (2014)
  * Scheuerman et al. [2019] Scheuerman, M.K., Paul, J.M., Brubaker, J.R.: How computers see gender: An evaluation of gender classification in commercial facial analysis services. Proceedings of the ACM on Human-Computer Interaction 3(CSCW), 1–33 (2019)
  * Loukissas [2019 - 2019] Loukissas, Y.A.Y.A.: All Data Are Local : Thinking Critically in a Data-driven Society. The MIT Press, Cambridge, Massachusetts (2019 - 2019)
  * Tomasev et al. [2021] Tomasev, N., McKee, K.R., Kay, J., Mohamed, S.: Fairness for unobserved characteristics: Insights from technological impacts on queer communities. In: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. AIES ’21, pp. 254–265. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3461702.3462540 . https://doi.org/10.1145/3461702.3462540
  * Queerinai et al. [2023] Queerinai, O.O., Ovalle, A., Subramonian, A., Singh, A., Voelcker, C., Sutherland, D.J., Locatelli, D., Breznik, E., Klubicka, F., Yuan, H., J, H., Zhang, H., Shriram, J., Lehman, K., Soldaini, L., Sap, M., Deisenroth, M.P., Pacheco, M.L., Ryskina, M., Mundt, M., Agarwal, M., Mclean, N., Xu, P., Pranav, A., Korpan, R., Ray, R., Mathew, S., Arora, S., John, S., Anand, T., Agrawal, V., Agnew, W., Long, Y., Wang, Z.J., Talat, Z., Ghosh, A., Dennler, N., Noseworthy, M., Jha, S., Baylor, E., Joshi, A., Bilenko, N.Y., Mcnamara, A., Gontijo-Lopes, R., Markham, A., Dong, E., Kay, J., Saraswat, M., Vytla, N., Stark, L.: Queer in ai: A case study in community-led participatory ai. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’23, pp. 1882–1895. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3593013.3594134 . https://doi.org/10.1145/3593013.3594134
  * Khlaaf et al. [2022] Khlaaf, H., Mishkin, P., Achiam, J., Krueger, G., Brundage, M.: A Hazard Analysis Framework for Code Synthesis Large Language Models (2022)
  * Winkle and Mulvihill [2024] Winkle, K., Mulvihill, N.: Anticipating the use of robots in domestic abuse: A typology of robot facilitated abuse to support risk assessment and mitigation in human-robot interaction. In: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, pp. 781–790 (2024)
  * Johnson [2020] Johnson, M.: Undermining Racial Justice: How One University Embraced Inclusion and Inequality. Cornell University Press, ??? (2020)
  * Gebru [2021] Gebru, T.: Hierarchy of knowledge in machine learning and related fields and its consequences. In: The Future Is Intersectional: Black Women Interrogating Technology (2021). Spelman College Center of Excellence for Minority Women in STEM. Announcement: https://www.spelman.edu/coe-mws/news-events/the-future-is-intersectional-series/2021/04/14/default-calendar/the-hierarchy-of-knowledge-in-machine-learning-and-related-fields-and-its-consequences. https://youtu.be/OL3DowBM9uc
  * Scott [1998] Scott, J.C.: Seeing Like a State : How Certain Schemes to Improve the Human Condition Have Failed. Yale agrarian studies. Yale University Press, New Haven (1998)
  * Zhu et al. [2024] Zhu, Y., Wen, R., Williams, T.: Robots for social justice (r4sj): Toward a more equitable practice of human-robot interaction. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI) (2024)
  * Birhane et al. [2022] Birhane, A., Isaac, W., Prabhakaran, V., Diaz, M., Elish, M.C., Gabriel, I., Mohamed, S.: Power to the people? opportunities and challenges for participatory ai. In: Equity and Access in Algorithms, Mechanisms, and Optimization. EAAMO ’22. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3551624.3555290 . https://doi.org/10.1145/3551624.3555290
  * Deng et al. [2022] Deng, W.H., Nagireddy, M., Lee, M.S.A., Singh, J., Wu, Z.S., Holstein, K., Zhu, H.: Exploring how machine learning practitioners (try to) use fairness toolkits. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’22, pp. 473–484. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3531146.3533113 . https://doi.org/10.1145/3531146.3533113
  * Meyer et al. [2022] Meyer, M., Horowitz, A., Marshall, E., Lum, K.: Flipping the script on criminal justice risk assessment: An actuarial model for assessing the risk the federal sentencing system poses to defendants. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’22, pp. 366–378. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3531146.3533104 . https://doi.org/10.1145/3531146.3533104
  * Stapleton et al. [2022] Stapleton, L., Lee, M.H., Qing, D., Wright, M., Chouldechova, A., Holstein, K., Wu, Z.S., Zhu, H.: Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’22, pp. 1162–1177. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3531146.3533177 . https://doi.org/10.1145/3531146.3533177
  * Suresh et al. [2022] Suresh, H., Movva, R., Dogan, A.L., Bhargava, R., Cruxen, I., Cuba, A.M., Taurino, G., So, W., D’Ignazio, C.: Towards intersectional feminist and participatory ml: A case study in supporting feminicide counterdata collection. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’22, pp. 667–678. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/%****␣main.bbl␣Line␣3000␣****10.1145/3531146.3533132 . https://doi.org/10.1145/3531146.3533132