IEEE Robotics & Automation Magazine - September 2019 - 13

shown that not all proposed fairness
criteria can be optimized at the same
time, therefore limiting guarantees that
an algorithm will always be fair. An
adequate enumeration of protected
attributes and their proxies (e.g., ZIP
codes are correlated with race in the
United States, thus leading to redlining
practices that exacerbate inequalities)
might be difficult without, for example,
working with sociologists who can
help put real-world abstract data into
categories and contextualize data in
particular socioeconomic and cultural
environments. Several companies are
releasing fairness tool kits to be integrated into software-development
pipelines early in the process. Industrial standards, such as the IEEE Ethics
Certification Program for Autonomous and Intelligent Systems within
the IEEE Global Initiative on Ethics of
Autonomous and Intelligent Systems,
are also being proposed [12]. However,
to date, the methodology for certification is not known, and it is not clear
whether such certification is enough to
truly avoid biased systems.
More holistic approaches that encompass a wider range of disciplines
have recently been advocated as necessary during the design of complex
AI or robotic systems [9] as a way
not only to provide technical solutions but also to better understand the
socioeconomic and cultural contexts
of use of the technology. This includes
taking special care in the construction,
documentation, and validation of data
sets and making the push for more

FROM THE EDITOR'S DESK

transparent algorithms that can be
monitored and audited during realworld operations, perhaps by independent institutions.
Wide societal acceptance and trust
of robotic systems will require a concerted effort involving sociologists,
ethicists, philosophers, and technologists to ensure fairness and build trust
in deploying autonomous and interactive systems. Perhaps Mark Twain
wasn't thinking about bias in robotics,
but he said it best when he noted,
"What gets us into trouble is not what
we don't know. It's what we know for
sure that just ain't so"!
References
[1] J. Vincent, "Google 'fixed' its racist algorithm by
removing gorillas from its image-labeling tech," The
Verge, Jan. 12, 2018. [Online]. Available: https://
www.theverge.com/2018/1/12/16882408/google
-racist-gorillas-photo-recognition-algorithm-ai
[2] I. D. Raji and J. Buolamwini, "Actionable auditing: Investigating the impact of publicly naming
biased performance results of commercial AI
products," in Proc. AAAI/ACM Conf. AI Ethics and
Society, 2019. [Online]. Available: http://www.aies
-conference.com/wp-content/uploads/2019/01/
AIES-19_paper_223.pdf
[3] J. Angwin, J. Larson, S. Mattu, and L. Kirchner,
"Machine bias: There's sof tware used across
the country to predict future criminals. And it's
biased against blacks," ProPublica, May 23, 2016.
[Online]. Available: https://www.propublica.org/
article/machine-bias-risk-assessments-in-criminal
-sentencing
[4] A. Datta, M. C. Tschantz, and A. Datta, "Automated experiments on ad privacy settings: A Tale
of opacity, choice, and discrimination," in Proc.

Privacy Enhancing Technologies, 2015, pp. 92-112.
doi: 10.1515/popets-2015-0007.
[5] S. Myers West, M. Whittake, and K. Crawford,
"Discriminating systems: Gender, race and power in
AI," AI Now Institute, New York Univ., New York,
2019. [Online]. Available: https://ainowinstitute
.org/discriminatingsystems.pdf
[6] A. Howard and J. Borenstein, "The ugly truth
about ourselves and our robot creations: The problem of bias and social inequity," Sci. Eng. Ethics,
vol. 24, no. 5, pp. 1521-1536, Oct. 2017. doi:
10.1007/s11948-017-9975-2.
[7] H. Han and A. K. Jain, "Age, gender and race
estimation from unconstrained face images,"
Michigan State Univ., East Lansing, Tech. Rep.
MSU-CSE-14-5, 2014. [Online]. Available: http://
biometrics.cse.msu.edu/Publications/Face/HanJain
_Unconst ra i ned AgeGenderR aceEst i mat ion
_MSUTechReport2014.pdf
[8] J. Kleinberg, S. Mullainathan, and M. Raghavan,
Inherent trade-offs in the fair determination of risk
scores. 2016. [Online]. Available: https://arxiv.org/
abs/1609.05807
[9] M. Whittaker et al., "AI now report," AI Now
Institute, New York Univ., New York, 2018. [Online].
Available: https://ainowinstitute.org/AI_Now_2018
_Report.pdf
[10] J. Dastin, "Amazon scraps secret AI recruiting
tool that showed bias against women," Reuters, Oct. 9,
2018. [Online]. Available: https://www.reuters.com/
article/us-amazon-com-jobs-automation-insight/
amazon-scraps-secret-ai-recruiting-tool-that
-showed-bias-against-women-idUSKCN1MK08G
[11] B. Wilson, J. Hoffman, and J. Morgenstern, Predictive inequity in object detection. 2019. [Online].
Available: https://arxiv.org/abs/1902.11097
[12] IEEE Global Initiative on Ethics of Autonomous
and Intelligent Systems, "Ethics in action." [Online].
Available: https://ethicsinaction.ieee.org/

(continued from page 4)

of the reasoning process was an essential part of AI expert systems in the
1980s, the research domain of explainable AI is still in its infancy for systems
that learn from so-called big data. A
thorough knowledge of AI algorithms is
necessary to guarantee aspects of justice
toward individuals and groups as well as
to prevent bias.

As usual, this September issue of
IEEE Robotics and Automation Magazine includes articles submitted on a
variety of topics rather than focusing
on a particular theme, as do special
issues. Calls for upcoming special issue
papers focus on deep learning and soft
robotics. Please check the Society website for more information. To support

the reproducibility of robotics and
automation research, the IEEE Robotics and Automation Society (RAS)
waives, for two years and a maximum
of five articles per year, open access
fees for reproducible articles (designated R-Articles), the first of which
appears in this issue. Enjoy your reading!

SEPTEMBER 2019

IEEE ROBOTICS & AUTOMATION MAGAZINE

https://ainowinstitute.org/discriminatingsystems.pdf https://ainowinstitute.org/discriminatingsystems.pdf http://biometrics.cse.msu.edu/Publications/Face/HanJain_UnconstrainedAgeGenderRaceEstimation_MSUTechReport2014.pdf http://biometrics.cse.msu.edu/Publications/Face/HanJain_UnconstrainedAgeGenderRaceEstimation_MSUTechReport2014.pdf https://www.theverge.com/2018/1/12/16882408/google-racist-gorillas-photo-recognition-algorithm-ai https://www.theverge.com/2018/1/12/16882408/google-racist-gorillas-photo-recognition-algorithm-ai https://arxiv.org/abs/1609.05807 https://arxiv.org/abs/1609.05807 https://ainowinstitute.org/AI_Now_2018_Report.pdf https://ainowinstitute.org/AI_Now_2018_Report.pdf https://www.media.mit.edu/publications/actionable-auditing-investigating-the-impact-of-publicly-naming-biased-performance-results-of-commercial-ai-products/ https://www.media.mit.edu/publications/actionable-auditing-investigating-the-impact-of-publicly-naming-biased-performance-results-of-commercial-ai-products/ https://www.media.mit.edu/publications/actionable-auditing-investigating-the-impact-of-publicly-naming-biased-performance-results-of-commercial-ai-products/ https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing https://www.arxiv.org/abs/1902.11097 https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing https://ethicsinaction.ieee.org/

IEEE Robotics & Automation Magazine - September 2019

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - September 2019