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The modern lie detector: Ai-powered affect screening and the Employee Polygraph Protection Act (EPPA)

The Georgetown law journal, 2021-04, Vol.109 (5), p.1201-1262 [Peer Reviewed Journal]

COPYRIGHT 2021 Georgetown University Law Center ;Copyright Georgetown University Law Center Apr 2021 ;ISSN: 0016-8092

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  • Title:
    The modern lie detector: Ai-powered affect screening and the Employee Polygraph Protection Act (EPPA)
  • Author: Hinkle, Courtney
  • Subjects: Artificial intelligence ; CONSUMER PROTECTION ; Data protection ; DISCRIMINATION ; Employee recruitment ; Employees ; Employment discrimination ; Evaluation ; Law and legislation ; Law enforcement ; Laws, regulations and rules ; Lie detectors and detection ; Methods ; Prevention ; PRIVACY ; Privacy, Right of ; Remedies ; Software ; Studies
  • Is Part Of: The Georgetown law journal, 2021-04, Vol.109 (5), p.1201-1262
  • Description: Predictive algorithms are increasingly being used to screen and sort the modern workforce. The delegation of hiring decisions to AI-powered software systems, however, will have a profound impact on the privacy of individuals. This Note builds on the foundational work of legal scholars studying the growing trend of algorithmic decision-making in recruiting and hiring practices. However, this Note will differ from their analysis in critical ways. Although this issue has primarily been studied through the lens of federal antidiscrimination law and for the potential for algorithmic bias, this Note will explore how federal privacy law, namely the oft- forgotten Employee Polygraph Protection Act (EPPA), offers a more robust regulatory framework. This Note will specifically analyze the use of video-interviewing screens that rely upon affect-recognition technology, which analyze an applicant's voice tonality, word choice, and facial movements. The cur-rent vogue for AI-powered affect screening is, however, reminiscent of an early period of employee screening tests: the lie detector. Congress prohibited the use of lie detectors by employers in the 1980s. By embracing old analytical shortcuts, which purport to correlate psychophysiological responses with desired character traits, namely honesty, this growing industry is operating in violation of federal law. This Note will also critique the limits of antidiscrimination law, data protection law, and consumer protection law to address the scope of privacy harms posed by these screens.
    Predictive algorithms are increasingly being used to screen and sort the modern workforce. The delegation of hiring decisions to AI-powered software systems, however, will have a profound impact on the privacy of individuals. This note builds on the foundational work of legal scholars studying the growing trend of algorithmic decision-making in recruiting and hiring practices. However, this note will differ from their analysis in critical ways. Although this issue has primarily been studied through the lens of federal anti-discrimination law and for the potential for algorithmic bias, this note will explore how federal privacy law, namely the oft-torgotten Employee Polygraph Protection Act (EPPA), offers a more robust regulatory framework. This note will specifically analyze the use of video-interviewing screens that rely upon affect-recognition technology, which analyze an applicant's voice tonality, word choice, and facial movements. The cur-rent vogue for AI-powered affect screening is, however, reminiscent of an early period of employee screening tests: the lie detector. Congress prohibited the use of lie detectors by employers in the 1980s. By embracing old analytical shortcuts, which purport to correlate psychophysiological responses with desired character traits, namely honesty, this growing industry is operating in violation of federal law. This note will also critique the limits of anti-discrimination law, data protection law, and consumer protection law to address the scope of privacy harms posed by these screens.
  • Publisher: Washington: Georgetown University Law Center
  • Language: English
  • Identifier: ISSN: 0016-8092
  • Source: Alma/SFX Local Collection
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