mlsanityData to 7 Oct 2026

A Mental Trespass? Unveiling Truth, Exposing Thoughts and Threatening Civil Liberties with Non-Invasive AI Lie Detection

arXiv 2102.08004 ↗First posted 16 Feb 2021cs.CY cs.AIcited by 0

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Abstract (arXiv)

Imagine an app on your phone or computer that can tell if you are being dishonest, just by processing affective features of your facial expressions, body movements, and voice. People could ask about your political preferences, your sexual orientation, and immediately determine which of your responses are honest and which are not. In this paper we argue why artificial intelligence-based, non-invasive lie detection technologies are likely to experience a rapid advancement in the coming years, and that it would be irresponsible to wait any longer before discussing its implications. Legal and popular perspectives are reviewed to evaluate the potential for these technologies to cause societal harm. To understand the perspective of a reasonable person, we conducted a survey of 129 individuals, and identified consent and accuracy as the major factors in their decision-making process regarding the use of these technologies. In our analysis, we distinguish two types of lie detection technology, accurate truth metering and accurate thought exposing. We generally find that truth metering is already largely within the scope of existing US federal and state laws, albeit with some notable exceptions. In contrast, we find that current regulation of thought exposing technologies is ambiguous and inadequate to safeguard civil liberties. In order to rectify these shortcomings, we introduce the legal concept of mental trespass and use this concept as the basis for proposed regulation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 114 references that are in the map. First posted on arXiv 16 Feb 2021.

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3 of 114 references.

References · 3

  1. 2020Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditingcited by 89
  2. 2019A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Imagescited by 7
  3. 2017Automated Dyadic Data Recorder (ADDR) Framework and Analysis of Facial Cues in Deceptive Communicationcited by 4

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