mlsanityData to 14 Oct 2026

Regulating Facial Processing Technologies: Tensions Between Legal and Technical Considerations in the Application of Illinois BIPA

arXiv 2205.07299 ↗First posted 15 May 2022cs.CY cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

Harms resulting from the development and deployment of facial processing technologies (FPT) have been met with increasing controversy. Several states and cities in the U.S. have banned the use of facial recognition by law enforcement and governments, but FPT are still being developed and used in a wide variety of contexts where they primarily are regulated by state biometric information privacy laws. Among these laws, the 2008 Illinois Biometric Information Privacy Act (BIPA) has generated a significant amount of litigation. Yet, with most BIPA lawsuits reaching settlements before there have been meaningful clarifications of relevant technical intricacies and legal definitions, there remains a great degree of uncertainty as to how exactly this law applies to FPT. What we have found through applications of BIPA in FPT litigation so far, however, points to potential disconnects between technical and legal communities. This paper analyzes what we know based on BIPA court proceedings and highlights these points of tension: areas where the technical operationalization of BIPA may create unintended and undesirable incentives for FPT development, as well as areas where BIPA litigation can bring to light the limitations of solely technical methods in achieving legal privacy values. These factors are relevant for (i) reasoning about biometric information privacy laws as a governing mechanism for FPT, (ii) assessing the potential harms of FPT, and (iii) providing incentives for the mitigation of these harms. By illuminating these considerations, we hope to empower courts and lawmakers to take a more nuanced approach to regulating FPT and developers to better understand privacy values in the current U.S. legal landscape.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 11 of its 82 references that are in the map. First posted on arXiv 15 May 2022.

Builds on

11 of 82 references.

References · 11

  1. 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 260
  2. 2020Large image datasets: A pyrrhic win for computer vision?cited by 237
  3. 2015YFCC100M: The New Data in Multimedia Researchcited by 235
  4. 2021Fake It Till You Make It: Face analysis in the wild using synthetic data alonecited by 200
  5. 2020LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihoodcited by 87
  6. 2021A Study of Face Obfuscation in ImageNetcited by 83
  7. 2019Diversity in Facescited by 77
  8. 2020"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairnesscited by 56
  9. 2021Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Paperscited by 49
  10. 2021About Face: A Survey of Facial Recognition Evaluationcited by 27
  11. 2018I Know How You Feel: Emotion Recognition with Facial Landmarkscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.

5 papers.