Genomics may increasingly be used to predict associations with social traits through a new field called sociogenomics. This approach includes developing genetic 'scores' to identify associations with individuals' traits like educational attainment, feelings of loneliness, aggressive behavior, and criminality. Companies are already testing embryos to select for some of these traits, and these scores could be adopted by industries and settings beyond commercialized reproductive genetic testing services. The nature of the scores raises concerns about the potential dangers of a passive regulatory approach. Although supporters argue that sociogenomic polygenic scores could help mediate social inequality, there are worries that their implementation into society could be discriminatory and inequitable. Without adequate safeguards, it could have severe consequences for adults using IVF services, students, health insurance beneficiaries, employees, and others in the future. While existing legal structures are in place to regulate medical genetic information, these protections have their own flaws, and further, do not clearly extend to polygenic scores. Policymakers must therefore consider the potential harms of sociogenomic polygenic scores, and how to maximize any benefits.
Polygenic embryo screening ("PES") analyzes embryos for hundreds or thousands of genomic loci to generate risk scores that estimate genetic susceptibility to conditions and traits compared to the general population. The technology is commercially marketed directly to consumers. Companies focus mostly on medical conditions, sometimes in ways that oversell its advantages and efficacy, encouraging fertility patients to "choose your healthiest embryo" and "protect your future child from genetic risks." The advertising of PES trades on norms of children's health and good parenting and reinforces those normative ideals. While it is easy to assume PES will be constrained in practice by its clinical limitations, high cost, and health burdens associated with in vitro fertilization, inflated marketing claims could exacerbate other legal and social forces to expand its use. Since the fall of Roe v. Wade, over a dozen states have banned abortion, forcing some people to give birth to children they would not otherwise have had. Others who are denied the abortion choice may seek to recover this lost sense of agency over their reproductive lives in other ways. This article examines the risks of decision fatigue and choice overload that PES may create in prospective parents, and the distinctive challenges that PES poses for legal liability over matters of truth in advertising and informed consent.
The high bar of proof to demonstrate either a disparate treatment or disparate impact cause of action under Title VII of the Civil Rights Act, coupled with the "black box" nature of many automated hiring systems, renders the detection and redress of bias in such algorithmic systems difficult. This Article, with contributions at the intersection of administrative law, employment & labor law, and law & technology, makes the central claim that the automation of hiring both facilitates and obfuscates employment discrimination. That phenomenon and the deployment of intellectual property law as a shield against the scrutiny of automated systems combine to form an insurmountable obstacle for disparate impact claimants.To ensure against the identified "bias in, bias out" phenomenon associated with automated decision-making, I argue that the employer's affirmative duty of care as posited by other legal scholars creates "an auditing imperative" for algorithmic hiring systems. This auditing imperative mandates both internal and external audits of automated hiring systems, as well as record-keeping initiatives for job applications. Such audit requirements have precedent in other areas of law, as they are not dissimilar to the Occupational Safety and Health Administration (OSHA) audits in labor law or the Sarbanes-Oxley Act audit requirements in securities law. I also propose that employers that have subjected their automated hiring platforms to external audits could receive a certification mark, "the Fair Automated Hiring Mark," which would serve to positively distinguish them in the labor market. Labor law mechanisms such as collective bargaining could be an effective approach to combating the bias in automated hiring by establishing criteria for the data deployed in automated employment decision-making and creating standards for the protection and portability of said data. The Article concludes by noting that automated hiring, which captures a vast array of applicant data, merits greater legal oversight given the potential for "algorithmic blackballing," a phenomenon that could continue to thwart many applicants' future job bids.
In this paper, we propose the creation of a system of personal data licenses that will help individuals determine conditions for granting access to their personal data. In this proposal, we foresee a future of user-held data, where each individual can easily control who can access their personal data. We suggest a system of shareable and understandable set of personal data licenses. We believe that personal data license system will instill the marketplace with needed shared trust and transparency, and help individuals “activate” their data for superior value and experiences, similar to what the Creative Commons licenses has achieved for the benefit for the entire market.
