The issue of fairness in AI has received an increasing amount of attention in recent years. The problem can be approached by looking at different protected attributes (e.g., ethnicity, gender, etc) independently, but fairness for individual protected attributes does not imply intersectional fairness. In this work, we frame the problem of intersectional fairness within a geometrical setting. We project our data onto a hypercube, and split the analysis of fairness by levels, where each level encodes the number of protected attributes we are intersecting over.
We prove mathematically that, while fairness does not propagate "down" the levels, it does propagate "up" the levels. This means that ensuring fairness for all subgroups at the lowest intersectional level (e.g., black women, white women, black men and white men), will necessarily result in fairness for all the above levels, including each of the protected attributes (e.g., ethnicity and gender) taken independently. We also derive a formula describing the variance of the set of estimated success rates on each level, under the assumption of perfect fairness. Using this theoretical finding as a benchmark, we define a family of metrics which capture overall intersectional bias.
Finally, we propose that fairness can be metaphorically thought of as a “fractal” problem. In fractals, patterns at the smallest scale repeat at a larger scale. We see from this example that tackling the problem at the lowest possible level, in a bottom-up manner, leads to the natural emergence of fair AI. We suggest that trustworthiness is necessarily an emergent, fractal and relational property of the AI system.
Schedule a call with one of our experts
DISCLAIMER: The information provided on this website does not, and is not intended to, constitute legal advice; instead, all information, content, and materials available on this site are for general informational purposes only. Information on this website may not constitute the most up-to-date legal or other information. This website contains links to other third-party websites. Such links are only for the convenience of the reader, user or browser; Holistic AI does not recommend or endorse the contents of the third-party sites.
Readers of this website should contact their attorney to obtain advice with respect to any particular legal matter. No reader, user, or browser of this site should act or refrain from acting on the basis of information on this site without first seeking legal advice from counsel in the relevant jurisdiction. Only your individual attorney can provide assurances that the information contained herein – and your interpretation of it – is applicable or appropriate to your particular situation. Use of, and access to, this website or any of the links or resources contained within the site do not create an attorney-client relationship between the reader, user, or browser and website authors, contributors, contributing law firms, or committee members and their respective employers.
The views expressed at, or through, this site are those of the individual authors writing in their individual capacities only – not those of their respective employers, Holistic AI, or committee/task force as a whole. All liability with respect to actions taken or not taken based on the contents of this site are hereby expressly disclaimed. The content on this posting is provided "as is;" no representations are made that the content is error-free.
