TY - JOUR
T1 - Algorithmic Fairness in the Roberts Court Era
AU - Wagner, Jennifer K.
N1 - Funding Information:
This work was supported in part by the NHGRI Grant No. R01HG011051. The content of this article is the author’s responsibility and might not represent the views of the author’s current or former funding sources, employers, clients, or any other person or entity. The author is appreciative of the constructive feedback on an early conceptualization of a portion of this work she received from colleagues during Biolawlapalooza 4.2 at Stanford Law School in May 2022.
Publisher Copyright:
© 2022 The Authors.
PY - 2023
Y1 - 2023
N2 - Scientists and policymakers alike have increasingly been interested in exploring ways to advance algorithmic fairness, recognizing not only the potential utility of algorithms in biomedical and digital health contexts but also that the unique challenges that algorithms - in a datafied culture such as the United States - pose for civil rights (including, but not limited to, privacy and nondiscrimination). In addition to the technical complexities, separation of powers issues are making the task even more daunting for policymakers - issues that might seem obscure to many scientists and technologists. While administrative agencies (such as the Federal Trade Commission) and legislators have been working to advance algorithmic fairness (in large part through comprehensive data privacy reform), recent judicial activism by the Roberts Court threaten to undermine those efforts. Scientists need to understand these legal developments so they can take appropriate action when contributing to a biomedical data ecosystem and designing, deploying, and maintaining algorithms for digital health. Here I highlight some of the recent actions taken by policymakers. I then review three recent Supreme Court cases (and foreshadow a fourth case) that illustrate the radical power grab by the Roberts Court, explaining for scientists how these drastic shifts in law will frustrate governmental approaches to algorithmic fairness and necessitate increased reliance by scientists on self-governance strategies to promote responsible and ethical practices.
AB - Scientists and policymakers alike have increasingly been interested in exploring ways to advance algorithmic fairness, recognizing not only the potential utility of algorithms in biomedical and digital health contexts but also that the unique challenges that algorithms - in a datafied culture such as the United States - pose for civil rights (including, but not limited to, privacy and nondiscrimination). In addition to the technical complexities, separation of powers issues are making the task even more daunting for policymakers - issues that might seem obscure to many scientists and technologists. While administrative agencies (such as the Federal Trade Commission) and legislators have been working to advance algorithmic fairness (in large part through comprehensive data privacy reform), recent judicial activism by the Roberts Court threaten to undermine those efforts. Scientists need to understand these legal developments so they can take appropriate action when contributing to a biomedical data ecosystem and designing, deploying, and maintaining algorithms for digital health. Here I highlight some of the recent actions taken by policymakers. I then review three recent Supreme Court cases (and foreshadow a fourth case) that illustrate the radical power grab by the Roberts Court, explaining for scientists how these drastic shifts in law will frustrate governmental approaches to algorithmic fairness and necessitate increased reliance by scientists on self-governance strategies to promote responsible and ethical practices.
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U2 - 10.1142/9789811270611_0047
DO - 10.1142/9789811270611_0047
M3 - Conference article
C2 - 36541005
AN - SCOPUS:85144323359
SN - 2335-6936
SP - 519
EP - 530
JO - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
JF - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
IS - 2023
T2 - 28th Pacific Symposium on Biocomputing, PSB 2023
Y2 - 3 January 2023 through 7 January 2023
ER -