ALL EYES ON U.S.: REGULATING THE USE & DEVELOPMENT OF FACIAL RECOGNITION TECHNOLOGY.

AuthorLowe, Matthew R.

ABSTRACT 3 I. INTRODUCTION 3 II. BACKGROUND 5 A. The Inextricable Relationship Between Racial Tension in America & Facial Recognition Technology 5 a. The First Attempts at Facial Recognition Technology 5 b. Recognizing a Pattem of Enduirng Bias 8 B. U.S. Law vs. the Rest of the World and the EU's Gold Standard 13 a. The Current U.S. Regulatory Posture 13 b. Enter: GDPR & Maximilian Schrems 18 III. ANALYSIS 23 A. What's in a Face: The Public Policy Need to Protect Facial Image Data 23 a. HIPPA as Federal Acknowledgement of a Need to Protect Sensitive Biometric Data 23 b. Other Statutory & Regalatory Examples of the Need to Federally Regulate Biometric Facial Data 26 c. The Issue with Digital Photography Itself 27 B. The Business Case for Passing a Federal Biometric Data Law 29 a. The Impact of Privacy in Corporate Culture 29 b. The Cost of Failing to Respect Privacy in the Marketplace 31 c. The Marketplace Benefits of Embracing Strong Data Privacy Policies 39 C. Approaches to Effective Biometric Data Regulations 41 a. How the FAA Approached Dron Regulation and Why the U.S. Government Should Do the Same for Facial Recognition Technology 41 b. The California Model: CCPA, CPRA, and CPPA as a Framework for Federal Policy 43 c. Next Steps: Drafting a Federal Biometric Data Privacy Law 46 IV. CONCLUSION 49 Facial recognition technology is developing faster than laws are being passed to properly regulate it. Because of its chilling effect on Americans' First Amendment rights, risk to international data transfers, demonstrated biased outcomes, and threat to our most sensitive data, the U.S. must take steps toward effectively controlling its use. This Article is aimed at first, addressing the history and social context surrounding facial recognition technology development and its use, and second, analyzing and identifying best practices that legislators, scholars, and corporate policymakers alike can take into account in considering robust protective measures to shield our most precious class of data.

  1. INTRODUCTION

    2020 was a year defined by tumult on a global scale. Much of the adversity the world faced was caused by what many perceived as a series of firsts, but in reality, all of the worst parts of the year stemmed from problems that have existed for centuries. Three hardships in particular stood out: (1) the death of George Floyd and the global protests that erupted as a result, (1) (2) the role of facial recognition technology and the U.S. response to it during a racially charged time, and (3) a global pandemic raging in the background. While some may argue that it was the combination of these three factors that created the novelty of the year 2020, it was not so long ago that the U.S. was faced with the convergence of parallel factors. In fact, in the 1960's, the U.S. encountered the civil rights movement, the advancement of facial recognition technology, (2) and a deadly global flu pandemic that originated in China and claimed more lives than the coronavirus. (3) The critical distinction, however, is in the heightened collective social conscience that has drawn clearer connections between these factors, especially facial recognition technology and racial bias. Further, as individuals have become more concerned with their human rights, including their right to privacy, there have been commensurate developments in the law to reflect this. In many ways, these developments can serve as a framework for drafting a much-needed biometric data privacy law to ensure that our most precious data is proportionally protected.

    Biometric data is critical because once compromised, there is no effective remediation that can undo the potential damage caused. In 2020, the convergence of racial tension in the U.S., following George Floyd's death, and increasing self-awareness of data subject rights, following the enactment of the EU's General Data Protection Regulation (GDPR), brought facial recognition's premature uses to the forefront. While Americans exercised their First Amendment right to protest, joined shortly thereafter by the rest of the world, some companies joined in the fight by changing corporate policies to turn away from biased technologies.

    The U.S. is at an inflection point from a regulatory perspective, where it is facing expectations set by domestic social standards and global pressures alike. The current fragmented regulatory environment is simply ineffective and dysfunctional. For the sake of streamlined, feasible compliance, and ensuring continued international economic prosperity, the U.S. must re-examine and reinforce its federal statutory infrastructure to meet its current challenges in the privacy space; especially when it comes to sensitive biometric data. This Article will first explain the historical background leading up to the present challenges in data privacy and then analyze and propose best practices for legislators, scholars, and corporate policymakers alike to consider.

  2. BACKGROUND

    1. The Inextricable Relationship Between Racial Tension in America & Facial Recognition Technology

      1. The First Attempts at Facial Recognition Technology

        Facial recognition has made its way into the global mainstream in recent years due to its rapid advancements. Some of its most common consumer-facing applications are used via security features and digital image filters. From the security standpoint, the advancement of the technology can be illustrated through the example of Apple, Inc.'s response to COVID-19 as it began testing software to permit iPhone users to access their devices through facial recognition, even if the individual is wearing a face mask. (4) However, what is less frequently discussed are the origins of the technology, which can be traced back over half a century ago and only a few years following the coining of the term "Artificial Intelligence." (5)

        In the 1960's, a man named Woodrow "Woody" Wilson Bledsoe ("Bledsoe") first identified facial recognition as a viable biometric. (6) In partnership with his colleague, Iben Browning ("Browning"), Bledsoe co-created the n-tuple recognition method; a type of weightless neural network, which is a practical pattern recognition method based on distributed computation and amenable to description in terms of neural network metaphors. (7) To this day, the n-tuple method continues to be recognized for its speed and simplicity when analyzing certain large data sets. (8) The analysis Bledsoe and Browning conducted is simpler to understand with an illustrative example; one of their first demonstrations leveraged alphabetical character recognition:

        They started by projecting a printed character--the letter Q, say--onto a rectangular grid of cells, resembling a sheet of graph paper. Then each cell was assigned a binary number according to whether it contained part of the character: Empty got a 0, populated got a 1. Then the cells were randomly grouped into ordered pairs, like sets of coordinates. (The groupings could, in theory, include any number of cells, hence the name n-tuple.) With a few further mathematical manipulations, the computer was able to assign the character's grid a unique score. When the computer encountered a new character, it simply compared that character's grid with others in its database until it found the closest match. (9) These advancements allowed for a wider application of pattern recognition, beyond just letters and characters, (10) effectively serving as the foundation of facial recognition technology.

        Two particularly noteworthy aspects of Bledsoe and Browning's discoveries were: first, they asked the question of "how can we make a machine do something like what people do?" (11) Secondly, however, these critical first steps into the examination of the math and science behind pattern recognition were framed by a backdrop of civil rights activism. When harkening back to the 1960's, it is not facial recognition technology that defined the Zeitgeist. Instead, it was a time of protest with unprecedented, iconic, history-shaping moments like those brought about by the Freedom Riders, (12) the March on Washington, (13) the passage of the Civil Rights Act of 1964, (14) the signing of the Voting Rights Act of 1965, (15) and the assassination of major civil rights figures of the era, including Malcolm X, Dr. Martin Luther King, Jr., and Robert F. Kennedy, among many others. (16)

        It is also important to note the intended application of facial recognition technology. Bledsoe, Browning, and another colleague formed Panoramic Research, Inc. ("Panoramic") and its primary source of funding came from the U.S. government via the Central Intelligence Agency ("CIA"); what is more, the CIA helped keep Panoramic financially viable when the team proved unsuccessful at raising funding through alternate channels. (17) At the time when Panoramic started working with them, the CIA was engaging in questionable, unethical, illegal experiments--among one of the most infamous of these experiments was Project MK-Ultra, the U.S. government's attempt at "mind control" through the psychological torture of unwilling participants. (18) But it was in 1963 that Bledsoe would successfully pitch a minimal viable product ("MVP"), a facial recognition machine capable of recognizing ten faces in the hopes of eventually being able to scale the MVP's capability up to analyze and recognize thousands of faces. (19) The facial recognition machine proved mostly unsuccessful. However, between that attempt and another government-funded assignment that Bledsoe would take on later in 1967 aimed at assisting the police with identifying faces, (20) Bledsoe made objectively forward progress towards developing a functional prototype, although at the time technological limitations represented a significant constraint. These limitations surfaced in a number of ways, for instance, the three-dimensional, non-static features of a face and its ability to distort substantially through...

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