The Algorithmic Auditing Trap
‘Bias audits’ for discriminatory tools are a promising idea, but current approaches leave much to be desired
This op-ed was written by Mona Sloane, a sociologist and senior research scientist at the NYU Center for Responsible A.I. and a fellow at the NYU Institute for Public Knowledge. Her work focuses on design and inequality in the context of algorithms and artificial intelligence.
We have a new A.I. race on our hands: the race to define and steer what it means to audit algorithms. Governing bodies know that they must come up with solutions to the disproportionate harm algorithms can inflict.
This technology has disproportionate impacts on racial minorities, the economically disadvantaged, womxn, and people with disabilities, with applications ranging from health care to welfare, hiring, and education. Here, algorithms often serve as statistical tools that analyze data about an individual to infer the likelihood of a future event—for example, the risk of becoming severely sick and needing medical care. This risk is quantified as a “risk score,” a method that can also be found in the lending and insurance industries and serves as a basis for making a decision in the present, such as how resources are distributed and to whom.
Now, a potentially impactful approach is materializing on the horizon: algorithmic auditing, a fast-developing field in both research and application, birthing a new crop of startups offering different forms of “algorithmic audits” that promise to check algorithmic models for bias or legal compliance.
Audits as a regulatory tool for hiring algorithms
Recently, the issue of algorithmic auditing has become particularly relevant in the context of A.I. used in hiring. New York City policymakers are debating Int. 1894–2020, a proposed bill that would regulate the sale of automated employment decision-making tools. This bill calls for regular “bias audits” of automated hiring and employment tools.
These tools — résumé parsers, tools that purport to predict personality based on social media profiles or text written by the candidate, or computer vision technologies that analyze a candidate’s…