Biomanipulation
Scientific and technological advances in the latter part of the twentieth century transformed the field of biometrics. Carleton Simon, for instance, first postulated using retinal vasculature for biometric identification in 1935, Footnote #1 content: See generally Carleton Simon & Isadore Goldstein, A New Scientific Method of Identification, 35 N.Y. STATE J. MED. 901 (1935) (detailing new method of identification based on correlation of the optic nerve with patterns of blood vessels in the eye). but it was not until forty years later that an Eyedentify patent brought the idea to fruition. Footnote #2 content: See Apparatus & Method for Identifying Individuals Through Their Retinal Vasculature Patterns, U.S. Patent No. 4,109,237 (filed Jan. 17, 1977) (issued Aug. 22, 1978). In 1937, John Henry Wigmore anticipated using oscilloscopes to identify individuals by speech patterns. Footnote #3 content: JOHN HENRY WIGMORE, THE SCIENCE OF JUDICIAL PROOF AS GIVEN BY LOGIC, PSYCHOLOGY, AND GENERAL EXPERIENCE AND ILLUSTRATED IN JUDICIAL TRIALS 284–85 (3d ed. 1937) (“Vocal Traits. By means of a well-understood principle, having many applications, the vibrations of the spoken voice on a diaphragm may be accurately translated, through an electrical current, into oscillations of a needle, and … arranged to leave a continuous variable ink-tracing as a record. . . . [T]he spoken voice … can now … be made to leave a … record having minute differences of individuality,” serving as a “mode of identification.”). Decades later, digitization and speech processors made voiceprint identification possible. Footnote #4 content: See, e.g., Voiceprint Identification Sys., U.S. Patent No. 6,356,868 (filed Oct. 25, 1999) (issued Mar. 12, 2002). In the 1970s, biological discoveries similarly led to the development of deoxyribonucleic acid (DNA) sequencing. Footnote #5 content: See INTECHOPEN, BIOMETRICS 139–52 (Jucheng Yang ed., 2011). And while Alphonse Bertillon in the late nineteenth century postulated iris distinctions, it was only in 1991 that John Daugman patented a means of extracting and encoding their unique patterns. Footnote #6 content: See ALPHONSE BERTILLON, IDENTIFICATION ANTHROPOME ´TRIQUE: INSTRUCTIONS SIGNALE ´TIQUES 28, 67–79 (1893) (classifying the morphological qualities of each part of the ear); id. at 45 (noting upper and lower eyelid, pupil size, iris contours and color); id. at 63–65 (noting nose characteristics); id. at 82 (noting distance from the base of the nose to the lips, prominence of the lips, etc.); id. at 129–33 (noting front and side photographs of the head); Biometric Pers. Identification Sys. Based on Iris Analysis, U.S. Patent No. 5,291,560 (filed Jul. 15, 1991) (issued Mar. 1, 1994); John Daugman, Iris Recognition: The Colored Part of the Eye Contains Delicate Patterns that Vary Randomly from Person to Person, Offering a Powerful Means of Identification, 89 AM. SCIENTIST 326, 329 (2001); John Daugman & Cathryn Downing, Epigenetic Randomness, Complexity and Singularity of Human Iris Patterns, 268 PROC. ROYAL SOC’Y: BIOLOGICAL SCIS. 1737, 1737 (2001). In this century, as algorithmic sciences, big data analytics, and artificial intelligence (AI) have gained ground, the biometric landscape again has radically altered. Footnote #7 content: See, e.g., PATRICK GROTHER, MEI NGAN & KAYEE HANAOKA, NAT’L INST. STANDARDS & TECH., ONGOING FACE RECOGNITION VENDOR TEST (FRVT) PART 2: IDENTIFICATION 2 (2018), https://nvlpubs. nist.gov/nistpubs/ir/2018/NIST.IR.8238.pdf [https://perma.cc/YQ86-TN8H]. The range of collectable Physiological Biometric Characteristics (PBCs), which measure innate human traits, has exploded. Footnote #8 content: I distinguish in this Article between unique markers associated with a particular body and attributes gleaned, such as age, gender, weight, hair or eye color, race, and ethnicity. Referred to in the literature, variously, as “soft” or “light” biometrics, they may aid in identification but lack distinctiveness and permanence. See generally Anil K. Jain et al., Can Soft Biometric Traits Assist User Recognition?, 5404 PROC. SPIE 561 (2004) (proposing integration of soft biometric features into outputs of primary biometric systems). The legal literature lags far behind, with almost every treatment of biometrics limited to a few PBCs, such as fingerprinting, facial recognition technology (FRT), or DNA. Footnote #9 content: See generally, e.g., Natalie Ram, America’s Hidden National DNA Database, 100 TEX. L. REV. 1253 (2022) (emphasizing DNA); Elizabeth A. Rowe, Regulating Facial Recognition Technology in the Private Sector, 24 STAN. TECH. L. REV. 1 (2020) (focusing on FRT); Anne Logsdon Smith, Alexa, Who Owns My Pillow Talk? Contracting, Collateralizing, and Monetizing Consumer Privacy Through Voice- Captured Personal Data, 27 CATH. U. J.L. & TECH. 187 (2018) (isolating voice prints). Some articles focus on state biometric laws, which cover only a few biometrics. See, e.g., Lisa P. Angeles, Untag Me: Why Federal Judges Are Broadly Construing Illinois’s Biometric Privacy Law, 42 CARDOZO L. REV. 349, 353 (2020) (focusing on FRT aspects of Illinois’s Biometric Information Privacy Act (BIPA)). Other works focus on specific use cases, such as collection of athletes’ biometric data. See, e.g., Nicholas Zych, Collection and Ownership of Minor League Athlete Activity Biometric Data by Major League Baseball Franchises, 14 DEPAUL J. SPORTS L. 129, 132 (2018) (summarizing potential use of Minor League Baseball players’ Athlete Activity Biometric Data (AABD)); Skyler R. Berman, Note, Bargaining over Biometrics: How Player Unions Should Protect Athletes in the Age of Wearable Technology, 85 BROOK. L. REV. 543, 545 (2020) (advocating for a players’ bill of rights to protect their biometric data). Articles looking at biometric privacy do not provide an in-depth examination of the field, instead tending to mention a few biometrics and then focusing on the absence of adequate provisions to address privacy interests. See, e.g., Fiona Q. Nguyen, The Standard for Biometric Data Protection, 7 J.L. & CYBER WARFARE 61, 62 (2018); Andrew Serulneck, The Importance of a Private Right of Action in Federal Biometric Privacy Legislation, 73 RUTGERS U. L. REV. 1593, 1596–97 (2021); Hannah Zimmerman, The Data of You: Regulating Private Industry’s Collection of Biometric Information, 66 U. KAN. L. REV. 637, 638–39 (2018). Nor have scholars considered the rapid expansion in Behavioral Biometric Characteristics (BBCs)—biologically grounded habits and proclivities, such as voice prints, eye movement, or gait signatures. Instead, just a handful of pieces focus on one or two BBCs. Footnote #10 content: See, e.g., Ian Taylor Logan, Comment, For Sale: Window to the Soul, Eye Tracking as the Impetus for Federal Biometric Data Protection, 123 PA. ST. L. REV. 779, 782 (2019) (discussing eye tracking); Andrew McStay, Emotional AI, Soft Biometrics and the Surveillance of Emotional Life: An Unusual Consensus on Privacy, BIG DATA & SOC’Y, Jan.–June 2020, at 1, 2 (discussing “using computer sensing to interact with emotional life”). While numerous scholars consider privacy in the context of the breadth of information that can be obtained about individuals, they fall short of handling the unique challenge posed by biometric data. See generally, e.g., DANIEL J. SOLOVE, UNDERSTANDING PRIVACY (2008) (discussing technology and the rising concern in scholarship over privacy). Yet thousands of scientific articles over the past fifteen years have focused on how to collect, analyze, and use PBCs and BBCs. Footnote #11 content: See, e.g., Amjad Hassan Khan M.K. & P.S. Aithal, Voice Biometric Systems for User Identification and Authentication – A Literature Review, 6 INT’L J. APPLIED ENG’G & MGMT. LETTERS 198, 199 (2022); Yimin Yin et al., Deep Learning for Iris Recognition: A Review, ARXIV, Mar. 2023, at 1, 3–4; Shuaijie Shan et al., Prospect of Voiceprint Recognition Based on Deep Learning, J. PHYSICS: CONF. SERIES, 2021, at 1; Punam Kamari & Seeja K.R., Periocular Biometrics: A Survey, 32 J. KING SAUD U. – COMPUT. & INFO. SCIS. 1086, 1087 (2022); Jarina B. Mazumdar & S.R. Nirmala, Retina Based Biometric Authentication System: A Review, 9 INT’L J. ADVANCED RSCH. COMPUT. SCI. 711, 712 (2018). Hundreds of thousands of patent applications have kept pace. Footnote #12 content: See Patent Database of Applications Filed 1991–2023 (maintained by author). Looking at just six of the most prominent companies, the numbers are staggering: between 2012 and 2022, they collectively applied for or obtained 12,000 to 19,000 biometric-related patents per year. Footnote #13 content: Amazon: 20,318 patent applications 2000–2023; Apple: 51,045 patent applications 2000–2023; Samsung: 113,207 patent applications 2000–2023; NEC: 19,502 patent applications 2000–2023; Meta/ Facebook: 11,982 patent applications 2000–2023. Id. (last searched Feb. 2024). Some, like NEC, specifically market their biometric technologies. See Biometric Authentication, NEC, https://www.nec. com/en/global/solutions/biometrics/index.html [https://perma.cc/F9E4-F4VN] (last visited Dec. 31, 2024) (listing as the company’s “six original biometric authentication technologies”: face recognition, iris recognition, fingerprint and palmprint recognition, finger vein recognition, voice recognition, and ear acoustic authentication). Others use it as part of their other products or services. The global biometric technology market, estimated to be worth $34.27 billion in 2022, is expected to expand at a compound annual growth rate of 20.4% until 2030. GRAND VIEW RSCH., BIOMETRIC TECHNOLOGY MARKET SIZE, SHARE & TRENDS ANALYSIS REPORT BY COMPONENT, BY OFFERING, BY AUTHENTICATION TYPE, BY APPLICATION, BY END-USE, BY REGION, AND SEGMENT FORECASTS, 2023 – 2030, https://www. grandviewresearch.com/industry-analysis/biometrics-industry [https://perma.cc/MQH2-YWFQ]. Legal scholarship has not only missed the depth and breadth of information that can be collected, analyzed, and deployed, but it also has largely overlooked a concerning new practice: biomanipulation, which I define as the use of biometric data to identify, analyze, predict, and manipulate a person’s beliefs, desires, emotions, cognitive processes, and/or behavior. Footnote #14 content: During a multi-year social media project that I directed at Georgetown Law, Jennifer Reich, the project coordinator, and I first coined the term to describe the future collection and use of biometric data in virtual reality. This Article builds on that work, further defining the term and offering a broader theoretical grounding. The word also exists in the environmental science literature, but it carries a very different meaning. See, e.g., Joseph Shapiro et al., Biomanipulation: An Ecosystem Approach to Lake Restoration, in THE PROCEEDINGS OF A SYMPOSIUM ON WATER QUALITY MANAGEMENT THROUGH BIOLOGICAL CONTROL 85, 85 (Patrick L. Brezonik & Jackson L. Fox eds., 1975) (using biomanipulation to describe the use of biological and nutrient solutions to shape water quality and combat eutrophication); Rinaldo Antonio Ribeiro Filho et al., Eutrophication Indexes Used as Fish Production Parameters in the Itaipu Reservoir (Brazil), 4 J. ENV’T. PROT. 151, 152 (2013) (using biomanipulation to describe control of phytoplankton by means of trophic cascade management). Books and articles on consumer and market manipulation, of course, have been around for decades; but the role of biometric data in presenting an immediate, more personalized, and more concerning form of insight and potential control has gone largely unnoticed. Footnote #15 content: See generally, e.g., Kirsten Martin, Manipulation, Privacy, and Choice, 23 N.C. J.L. & TECH. 452 (2022) (arguing for the regulation of companies able to manipulate individuals but not discussing biometrics); Shaun B. Spencer, The Problem of Online Manipulation, 2020 U. ILL. L. REV. 959 (2020) (discussing online tracking without mentioning biometrics); Tal Z. Zarsky, Privacy and Manipulation in the Digital Age, 20 THEORETICAL INQUIRIES L. 157 (2019) (weighing the usefulness of legal intervention against manipulative technology but not discussing biometrics); Daniel Susser, Beate Roessler & Helen Nissenbaum, Online Manipulation: Hidden Influences in a Digital World, 4 GEO. L. TECH. REV. 1 (2019) (discussing online manipulation without addressing biometrics); Ryan Calo, Digital Market Manipulation, 82 GEO. WASH. L. REV. 995 (2014) (omitting biometrics); SHOSHANA ZUBOFF, THE AGE OF SURVEILLANCE CAPITALISM: THE FIGHT FOR A HUMAN FUTURE AT THE NEW FRONTIER OF POWER (2019) (noting the use of behavioral data to target consumers/redirection without calling out biometrics); CASS R. SUNSTEIN, THE ETHICS OF INFLUENCE: GOVERNMENT IN THE AGE OF BEHAVIORAL SCIENCE (2016) (drawing a distinction between coercion and influence without focusing on biometrics). For the past fifteen years, companies have delved headlong into this realm, pushing the boundaries and looking for ways to capitalize on biometrically enabled inventions. Paralleled by scientific and technological advances, a fundamentally different world has emerged. Early on, emphasis was placed on consumer behavior. Meta, for example, has patented a system to extract linguistic data (words, word stems, and communication patterns) and facial markers, and pair them with demographic and social network information. Footnote #16 content: Determining User Personality Characteristics from Soc. Networking Sys. Commc’ns & Characteristics, U.S. Patent No. 9,740,752 B2 col. 1 ll. 56–61, col. 4 ll. 3–12 (filed June 3, 2016) (issued Aug. 22, 2017). It considers the level of influence wielded by a node in a network, the number of connections, and engagement patterns, as well as biographic data (e.g., affinities, work experience, education, hobbies, location, and preferences), for news feeds, ranking, advertising, and other activities. Footnote #17 content: Id. col. 1 ll. 31–33, col. 2 ll. 36–39. What is at stake, though, is more than just purchasing patterns. Biometric data can be used to generate insight into an individual’s beliefs, desires, emotions, and fears—and then to alter them. Footnote #18 content: See infra Sections II.A and II.B. Some propose relatively innocuous, or even welcome, shifts. One baby monitor design, for instance, anticipates the collection of auditory, cardiovascular, respiratory, and other sensory information. See Remote Biometric Monitoring Sys., U.S. Patent No. 10,643,081 B2 (filed Oct. 24, 2018) (issued May 5, 2020). The aim is to shape the target’s behavior by altering the environment around them. By combining actigraphy data (which measures motor activity) and respiration rates with the target’s typical sleep patterns, the system can ascertain whether or not the subject is in light sleep, rapid eye movement, or deep sleep and initiate changes in the temperature or humidity of the room to alter the sleeping state. Id. col. 4 ll. 9–10, col. 12 ll. 31–37. It may play music, change the lighting, project images, or release a scent into the air, based on the target’s profile. Id. col. 4 ll. 23–29, col. 8 ll. 3–9. However welcome such inventions might be, the fact that they are able to use biometric data to alter the subject’s mental and physical state represents something different in kind than what has hitherto existed. In 2022, for instance, Amazon secured a patent to analyze an individual’s emotional state, set a new target state, deliver content to get the individual to hit that goal, evaluate the impact of stimuli delivered, and continue to shape the individual’s emotions until the desired emotional state has been reached. Footnote #19 content: Interactive Media Facial Emotion-Based Content Selection Sys., U.S. Patent No. 11,373,446 B1 (filed Apr. 26, 2019) (issued June 28, 2022). See Figure 1, below. The company explained,
[I]f a content provider intends to scare a user playing a game, the system may select content known to be scary, such as monsters or zombies, or may present video or audio (e.g., dark colors, scary sounds, or the like) to present in the game to the user. . . . The system may modify content based on a target or desired emotion to cause. For example, additional zombies may be added to an existing scene, or the tone or pitch of audio may be adjusted without causing an interruption to the presentation of the content. Footnote #20 content: Id. col. 2 ll. 9–13, 18–22.
Prior systems fell short; they failed to “account for a user’s current emotional state and how significant the transition from the user’s current emotional state to a target emotional state at a given time may be.” Footnote #21 content: Id. col. 2 ll. 25–28. The proposed system selected and customized content to elicit the most direct emotional impact for each user, allowing it to obtain the “desired change to the user’s emotional state” within time limits. Footnote #22 content: Id. col. 2 ll. 30–33. It employed “cameras, microphones, heartrate monitors, biometric sensors, [and] other . . . devices . . . to analyze and identify a user’s emotional state at a given time.” Footnote #23 content: Id. col. 2 ll. 38–41 It could take into account body, arm, and hand position, heartrate, and other indicators, such as “fingerprints, face recognition, blood flow, retinal data, voice data, scents, and other data” to determine the user’s precise emotional state. Footnote #24 content: Id. col. 2 ll. 41–60. The information could yield insight into “which content is associated with causing certain emotions, how often, how long it takes a user to transition from one emotion to another emotion, and other data.” Footnote #25 content: Id. col. 2 ll. 62–65. The aim was to develop a system that could manipulate a target’s future emotions. Footnote #26 content: See infra fig. 1. Applied in the context of gaming or movies, such technological advances might appear relatively benign. People like to be entertained. But the fact that such information can be harvested and employed to any number of ends without restriction or oversight raises concern, as does the fact that such markers tend to be immutable: it can be very difficult, if not impossible, for targets to change their biometric markers, leaving the target vulnerable to manipulation for the rest of their lives from any actor with access to the data who may be driven by any number of purposes.
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