Unfairness By Algorithm

Unfairness By Algorithm: Distilling the Harms of Automated Decision-Making by Lauren Smith.

From the post:

Analysis of personal data can be used to improve services, advance research, and combat discrimination. However, such analysis can also create valid concerns about differential treatment of individuals or harmful impacts on vulnerable communities. These concerns can be amplified when automated decision-making uses sensitive data (such as race, gender, or familial status), impacts protected classes, or affects individuals’ eligibility for housing, employment, or other core services. When seeking to identify harms, it is important to appreciate the context of interactions between individuals, companies, and governments—including the benefits provided by automated decision-making frameworks, and the fallibility of human decision-making.

Recent discussions have highlighted legal and ethical issues raised by the use of sensitive data for hiring, policing, benefits determinations, marketing, and other purposes. These conversations can become mired in definitional challenges that make progress towards solutions difficult. There are few easy ways to navigate these issues, but if stakeholders hold frank discussions, we can do more to promote fairness, encourage responsible data use, and combat discrimination.

To facilitate these discussions, the Future of Privacy Forum (FPF) attempted to identify, articulate, and categorize the types of harm that may result from automated decision-making. To inform this effort, FPF reviewed leading books, articles, and advocacy pieces on the topic of algorithmic discrimination. We distilled both the harms and potential mitigation strategies identified in the literature into two charts. We hope you will suggest revisions, identify challenges, and help improve the document by contacting lsmith@fpf.org. In addition to presenting this document for consideration for the FTC Informational Injury workshop, we anticipate it will be useful in assessing fairness, transparency and accountability for artificial intelligence, as well as methodologies to assess impacts on rights and freedoms under the EU General Data Protection Regulation.

The primary attraction are two tables, Potential Harms from Automated Decision-Making and Potential Mitigation Sets.

Take the tables as a starting point for analysis.

Some “unfair” practices, such as increased auto insurance prices for night-shift workers, which results in differential access to insurance, is an actuarial question. Insurers are not public charities and can legally discriminate based on perceived risk.

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