Page 141 - Proceedings of the 2018 ITU Kaleidoscope

A GENDERED PERSPECTIVE ON ARTIFICIAL INTELLIGENCE

Smriti Parsheera 1

1 National Institute of Public Finance and Policy, New Delhi

ABSTRACT Today, we are in a phase of AI boom. As per most accounts,
AI based systems will play a much greater role in the
Availability of vast amounts of data and corresponding coming decades, redefining business models, job markets
advances in machine learning have brought about a new and overall human development. In all the euphoria
phase in the development of artificial intelligence (AI). surrounding AI and its future, not enough was being said
While recognizing the field’s tremendous potential we must about the underlying processes that drive research in this
also understand and question the process of knowledge- field. This has begun to change in the last few years as
making in AI. Focusing on the role of gender in AI, this countries begin to adopt national or regional AI strategies,
paper discusses the imbalanced power structures in AI many of which incorporate an inclusion and ethics
processes and the consequences of that imbalance. We dimension in them (Dutton, 2018[2]).
propose a three-stage pathway towards bridging this gap.
The first, is to develop a set of publicly developed standards Like most human creations, AI artifacts tend to reflect the
on AI, which should embed the concept of “fairness by goals, knowledge and experience of their creators. They
design”. Second, is to invest in research and development also draw from the strengths and weaknesses of the data
in formulating technological tools that can help translate that is used to train them. It is therefore natural to expect
the ethical principles into actual practice. The third, and the limitations and biases of the creators and their datasets
perhaps most challenging, is to strive towards reducing to be reflected in their results. This leads us to ask some
gendered distortions in the underlying datasets to reduce basic questions. First, what is regarded as AI, who designs
biases and stereotypes in future AI projects. it and to what end? Second, what is the basis for
determining the elements of intelligence that are found
Keywords – Artificial intelligence, gender, ethics, fairness worth replicating in machines? Finally, to what extent do
these decisions reflect the diverse experience and needs of
1. INTRODUCTION human society?

The term artificial intelligence (AI) was coined in a These are complex questions, and the answers will
Dartmouth summer research proposal in 1955 that necessarily vary based on the respondent’s standpoint --
described itself as a “2 month, 10 man study of artificial education, gender, race, class, religion, nationality and the
intelligence”. John McCarthy, Marvin Minsky and their intersectionality of these factors. Despite recent attempts to
fellow drafters explained it as a “proposal to find how to “diversify” AI research, and more generally research in the
make machines use language, form abstractions and fields of science, technology, engineering and mathematics
concepts, solve kinds of problems now reserved for humans, (STEM), the discipline has retained a male-oriented focus.
and improve themselves” (McCarthy et al, 1955[1]). They It is telling that when the Institute of Electrical and
highlighted these as problems that needed a carefully Electronics Engineers (IEEE) instituted a Hall of Fame to
selected group of scientists to work on them and there acknowledge the leading contributors to AI, not one of the
seemed to be no doubt about the gender of those ten persons on the list was a woman (Wang, 2010[3]).
researchers.
A research environment that fails to account for the
Sixty years hence, AI is seen as one of the most promising worldview of one entire gender group is clearly lacking in
fields of computer science. Its latest boom is fueled by the many respects. In making this claim, we are cognizant of
availability of vast amounts of data and corresponding the fact that just as there is no universal “human
advances in machine learning and neural technology. Self- knowledge”, it is also not possible to classify “men’s
driving vehicles, cancer detection technologies, image knowledge” and “women’s knowledge” into distinct
recognition tools, language translation and virtual assistants buckets. There exist a multiplicity of viewpoints within
are some of the many AI applications that we encounter in these groups. A more inclusive, and indeed more fruitful,
everyday conversations. The field has, however, gone research agenda should ultimately be able to overcome
through its share of “AI winters”, characterized by cutbacks these binaries. Recognizing the existence of a gendered
in funding when research outcomes failed to keep up with perspective on AI is, however, the starting point for this
the claimed progress. conversation. While this paper uses the role of gender in AI

978-92-61-26921-0/CFP1868P-ART @ 2018 ITU – 125 – Kaleidoscope