Three Laws Blog: Law and Artificial Intelligence

Stathoulopoulos and Mateos-Garcia on Gender Diversity in AI Research @kstathou @JMateosGarcia @nesta_uk

Konstantinos Stathoulopoulos and Juan C. Mateos-Garcia, both of the National Gender Diversity in AI Research, have published Gender Diversity in AI Research. Here is the abstract.

Lack of gender diversity in the Artificial Intelligence (AI) workforce is raising growing concerns, but the evidence base about this problem has until now been based on statistics about the workforce of large technology companies or submissions to a small number of prestigious conferences. We build on this literature with a large-scale analysis of gender diversity in AI research using publications from arXiv, a widely-used preprints repository where we have identified AI papers through an expanded keyword analysis, and predicted author gender using a name-to-gender inference service. We study the evolution of gender diversity in various disciplines, countries and institutions, finding that while the share of female co-authors in AI papers is increasing, it has stagnated in disciplines related to computer science. We also find that geography plays an important role in determining the share of female authors in AI papers and that there is a severe gender gap in the top research institutions. We also study the link between female authorship in papers and the citations it receives, finding a strong, positive correlation in research domains related to the impact of information technology on society. Having done this, we examine the semantic differences between AI papers with and without female co-authors. Our results suggest that there are significant differences in machine learning and computer ethics between the United States and the United Kingdom as well as differences in the research focus of papers with female co-authors. We conclude by reporting the results of interviews with female AI researchers and other important stakeholders aimed at interpreting our findings and identifying policies to improve diversity and inclusion in the AI research workforce.

Download the article from SSRN at the link.

Parsheera on A Gendered Perspective on AI @SmritiParsheera

Smriti Parsheera, National Institute of Public Finance and Policy, has published A Gendered Perspective on Artificial Intelligence in the Proceedings of ITU Kaleidoscopte 2018--Machine Learning for a (sic) SG Future. Here is the abstract.

Rapid advances in machine learning have brought about a new phase in the development of artificial intelligence (AI). While recognizing the field’s tremendous potential we must also understand and question the process of knowledge-making in AI. Focusing on the role of gender in AI, this paper discusses the imbalanced power structures in AI processes and the consequences of that imbalance. From its inception, the field of AI has largely remained the domain of men, the gender of its founders and researchers playing a key role in shaping AI's developmental path. Further, AI's reliance on real-world data, which is often fraught with gender stereotypes and biases, also ends up reinforcing and sometimes exacerbating society's existing biases. The challenge therefore lies in finding ways to bundle the technological progression of AI research with the objectives of pursuing greater fairness in society -- for machines to eliminate rather than reinforce human biases. The paper proposes a three-stage pathway towards bridging this gap. The first, is to develop a set of publicly developed standards on AI, which should embed the concept of “fairness by design”. Second, is to invest in research and development in formulating technological tools that can help translate the ethical principles into actual practice. The third, and perhaps most challenging, is to strive towards reducing gendered distortions in the underlying datasets to reduce biases in future AI projects.

Download the paper from SSRN at the link.