{"id":14998,"date":"2025-12-01T14:41:19","date_gmt":"2025-12-01T19:41:19","guid":{"rendered":"https:\/\/sites.law.duq.edu\/juris\/?p=14998"},"modified":"2025-12-01T14:41:21","modified_gmt":"2025-12-01T19:41:21","slug":"pennsylvanias-right-to-reputation-in-the-age-of-algorithms","status":"publish","type":"post","link":"https:\/\/sites.law.duq.edu\/juris\/2025\/12\/01\/pennsylvanias-right-to-reputation-in-the-age-of-algorithms\/","title":{"rendered":"Pennsylvania’s Right to Reputation in the Age of Algorithms"},"content":{"rendered":"
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Photo courtesy of Pixabay.com<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n

By Chase Boss, Senior Editor<\/p>\n\n\n\n

\u201cTechnology can change the facts to which doctrine applies faster than courts can adjust the doctrine.\u201d[1]<\/sup><\/a> AI algorithms are bound to be a prime example; this technology may very well alter the application of a right unique to citizens of the Keystone State\u2014the right to reputation.<\/p>\n\n\n\n

Introduction<\/strong><\/p>\n\n\n\n

Article I, Section 1 of the Pennsylvania Constitution provides for an \u201cinherent and indefeasible right\u201d to reputation.[2]<\/sup><\/a> As acknowledged by the Pennsylvania Supreme Court in the nineteenth century, this textual protection places reputation on equal footing with the well-known interests of life, liberty, and property.[3]<\/sup><\/a> Federal doctrine, following Paul v. Davis<\/em>, considers reputational injury only a basic stigma unless coupled with a change in legal status;[4]<\/sup><\/a> Pennsylvania, however, has read Article I, Section 1 to recognize reputation as a fundamental right that cannot be abridged without due process.[5]<\/sup><\/a><\/p>\n\n\n\n

This right has generally emerged in disputes as a \u201cbelt-and-suspenders\u201d constitutional argument, separate from common-law defamation suits.[6]<\/sup><\/a> In the last half-century, however, the Pennsylvania Supreme Court has recognized this right as alive and well today, as it was recently used to narrow the application of law that impose official stigma and permanently mark individuals as dangerous.[7]<\/sup><\/a><\/p>\n\n\n\n

Harm to reputation will inevitably grow as algorithmic classification technologies improve in reach and scale. Predictive policing systems, person-based \u201crisk\u201d scores, and automated risk assessments now influence and, in some cases, essentially determine decisions about whom to stop and the identification of \u201chigh risk\u201d persons.[8]<\/sup><\/a> These systems, while designed to merely classify individuals and recommend police action, operate with little transparency and limited opportunities to challenge improperly assigned labels.[9]<\/sup><\/a> A growing body of research by lawyers and ethicists alike seeks to explain how these tools reinforce racial and socioeconomic disparities despite the increased use of algorithms in policing nationwide.<\/p>\n\n\n\n

Pennsylvania\u2019s right to reputation may offer a strong framework for assessing and restraining algorithmic categorization of individuals without due process. When state and local officials solely rely on automated systems to label individuals as dangerous\u2014and then base actions on an algorithm\u2019s conclusions\u2014the right to reputation may be directly implicated. <\/p>\n\n\n\n

The Origin and Development of the Right<\/strong><\/p>\n\n\n\n

Pennsylvania added \u201creputation\u201d to its Declaration of Rights in 1790, signaling an early and explicit commitment to protecting personal reputation against government action; however, early records offer little indication as to why \u201creputation\u201d was included in the final draft.[10]<\/sup><\/a> This very clear textual choice, however, elevates reputation to the same level as life, liberty, and property.[11]<\/sup><\/a><\/p>\n\n\n\n

One major development in reputation rights occurred in Simon v. Commonwealth<\/em>, in which the Commonwealth Court ruled that state agencies could not publicly spread stigmatizing findings without first giving affected individuals adequate notice and opportunity to respond.[12]<\/sup><\/a> Even government employee cases like Hunter v. Port Authority<\/em> demonstrate that a government\u2019s reputation-based judgments invoke the due process protections.[13]<\/sup><\/a> The Supreme Court\u2019s recent ruling in In re J.B<\/em>.\u2014which struck down lifetime juvenile sex-offender registration\u2014held that statutes imposing irrebuttable presumptions of dangerousness violate due process when they cannot be challenged.[14]<\/sup><\/a> Collectively, today\u2019s right to reputation involves three key doctrines that make up a basic framework: (1) reputational harm is a fundamental right; (2) stigmatizing classifications must provide notice and a chance to rebut; and (3) irrebuttable presumptions of dangerousness are constitutionally disfavored.<\/p>\n\n\n\n

The Rise of Algorithmic Classification and Changing Reputations<\/strong><\/p>\n\n\n\n

The use of predictive policing tools is likely to introduce new reputational risks. Many tools can assign labels such as \u201chigh risk,\u201d \u201cdangerous,\u201d or \u201cgang-affiliated\u201d with little to no transparency, verification, or proper process for rebutting an incorrect label.[15]<\/sup><\/a> <\/strong>Predictive policing platforms are trained using historical arrest data; this practice, understandably, tends to replicate historical racial disparities and makes recommendations based on those disparities for future policing.[16]<\/sup><\/a> For example, one researcher discusses how these historic models have directed officers disproportionately to Black neighborhoods, thereby reinforcing biased crime data patterns from the past and importing disparity into the present.[17]<\/sup><\/a><\/p>\n\n\n\n

Unfortunately, this suggests a troubling, circular relationship between policing then and policing today: historically overpoliced communities may generate more enforcement data, which the algorithm interprets as increased criminal activity, leading to additional surveillance.[18]<\/sup><\/a> Take Chicago\u2019s Strategic Subject List (SSL) for example; research on that program highlights the risks of person-based risk scoring.[19]<\/sup><\/a> Those classifications appeared in police records and influenced patrol decisions, directly affecting how officers interacted with specific individuals.[20]<\/sup><\/a><\/p>\n\n\n\n

Predictive policing was once described as an \u201cexperiment\u201d conducted on communities without informed consent or oversight.[21]<\/sup><\/a> AI-driven analytics, while unquestionably useful for agencies attempting to expand reach and capabilities, enable police to gather and interpret data at a scale previously impossible.[22]<\/sup><\/a> Automated decision systems (\u201cADS\u201d) further encode value judgments instead of objective assessments, leading to real-world action.[23]<\/sup><\/a><\/p>\n\n\n\n

Applying Pennsylvania\u2019s Right to Reputation to Algorithmic Classification<\/strong><\/p>\n\n\n\n

Simon<\/em> requires notice and a chance to respond before the state shares stigmatizing information with the public, an event that algorithmic systems usually do not meet, given the limited use of such information by internal law enforcement and third-party AI providers.  However, individuals flagged as \u201chigh risk\u201d rarely know they have been classified, cannot access the underlying data, and are denied any opportunity to challenge the designation.[24]<\/sup><\/a> It\u2019s important to note that an agency\u2019s limited designation of an individual may not reach the public-<\/em>facing reputational damage as seen in Simon <\/em>and its progeny; however, the effects of the labeling can still be felt. <\/p>\n\n\n\n

Under In re J.B<\/em>., irrebuttable presumptions are deemed unconstitutional when they are not true in all cases; algorithmic labels based on broad correlations act as modern irrebuttable presumptions. Risk scores generated from opaque models assume risk without providing any way to challenge them.[25]<\/sup><\/a> Reputational stigma, when <\/em>combined with tangible consequences of reputational damage\u2014such as enhanced surveillance, denial of release, and employment exclusion\u2014may very well trigger due-process protections under Pennsylvania law. This is not to suggest that injury to reputation alone is not a constitutional violation;[26]<\/sup><\/a> rather, the violation may arise when the state relies on false or improper reputational classifications that, once relied upon, used, or disseminated, go on to inflict harm on an individual\u2019s reputation.<\/p>\n\n\n\n

In Pennsylvania, algorithmic classifications should be treated as state action, likely to adversely affect individuals\u2019 due process rights; procedural safeguards should, as a result, be implemented accordingly. Agencies that use algorithmic tools should provide notice, access to relevant data (even limited and subject to trade privilege), and, at a minimum, a human review of computer-generated classifications.[27]<\/sup><\/a> Judicial review must also be available to address constitutional violations in the criminal context, especially when privately-owned algorithmic systems are likely to hinder transparency.[28]<\/sup><\/a> When vendors refuse to disclose model details necessary for due process review,[29]<\/sup><\/a> their outputs should not be used in situations involving reputational rights.<\/p>\n\n\n\n

Pennsylvania\u2019s courts have long maintained that government actions must not be \u201cunduly oppressive\u201d or \u201cpatently beyond the necessities of the case,\u201d[30]<\/sup><\/a> and algorithmic tools with low accuracy and high bias fail would appear to fall short of that standard. The Commonwealth should take it upon itself to mandate independent audits and conduct bias evaluations before algorithms are used and relied upon in policing contexts.[31]<\/sup><\/a> Private models may also need to be prohibited from high-stakes applications where reputational harm is likely.[32]<\/sup><\/a><\/p>\n\n\n\n

Article I, Section 1 guarantees all Pennsylvanians an independent constitutional right to protect their reputation; in the approaching era of algorithmic governance, upholding that right requires transparency, fairness, and accountability.<\/p>\n\n\n\n

Conclusion<\/strong>

The growth of algorithmic classifications changes how the government sees and treats its citizens. Today\u2019s predictive policing, risk assessments, and other AI tools that assign reputational labels affecting surveillance and detention are error-prone, susceptible to historical racial biases, and can be used without meaningful procedural protections.<\/p>\n\n\n\n

However, the Pennsylvania Constitution may offer a strong safeguard against these harms. As algorithmic tools become ingrained in policing, Pennsylvania courts and legislators must decide whether an individual\u2019s right to reputation should serve as a shield against an algorithm\u2019s irrebuttable presumption, which, if unchallenged, can lead to very real, tangible consequences.<\/p>\n\n\n\n

\n\n\n\n

[1]<\/sup><\/a> Elena Kagan, J., Harvard Law School Class Address <\/em>(May 22, 2013); see, e.g.<\/em>, U.S. v. Jones, 565 U.S. 400, 427 (2012) (Alito, J., concurring in judgment) (\u201cBut technology can change [privacy] expectations. Dramatic technological change may lead to periods in which popular expectations are in flux and may ultimately produce significant changes in popular attitudes\u201d).<\/p>\n\n\n\n

[2]<\/sup><\/a> Pa. Const. art. I, \u00a7 1.<\/p>\n\n\n\n

[3]<\/sup><\/a> Meas v. Johnson, 39 A. 562, 563 (Pa. 1898).<\/p>\n\n\n\n

[4]<\/sup><\/a> See <\/em>the \u201cStigma-Plus Doctrine.\u201d Paul v. Davis, 424 U.S. 693, 711\u201312 (U.S. 1976). <\/p>\n\n\n\n

[5]<\/sup><\/a> R. v. Dept. of Pub. Welfare, 636 A.2d 142, 149 (Pa. 1994).<\/p>\n\n\n\n

[6]<\/sup><\/a> See, e.g.<\/em>, Balleta v. Spadoni, 47 A.3d 183 (Pa. Commw. 2012); Hatchard v. Westinghouse Broadcasting Co., 532 A.2d 346 (Pa. 1987); Thomas v. Kane, 2016 WL 6081868 (Pa. Commw. Oct. 17, 2016).<\/p>\n\n\n\n

[7]<\/sup><\/a> Perhaps most notably, the right to reputation was a vehicle to strike down the Sex Offender Registration and Notification Act (SORNA) as applied to juvenile offenders. See <\/em>In re J.B., 107 A.3d 1, 19\u201320 (Pa. 2014).<\/p>\n\n\n\n

[8]<\/sup><\/a> Sarah Valentine, Impoverished Algorithms: Misguided Governments, Flawed Technologies, and Social Control<\/em>, 46 Fordham Urb. L. J. 364, 371 (quoting <\/em>Virginia Eubanks, Automating Inequality: How High-Tech Tools Profile, Police, and Punish The Poorrrr, p. 38 (2017)).<\/p>\n\n\n\n

[9]<\/sup><\/a> Ben Winters, Layered Opacity: Criminal Legal Technology Exacerbates Disparate Impact Cycles and Prevents Trust<\/em>, 12 J. Nat\u2019l Sec. L. & Pol\u2019y 327, 337\u2013340 (2022); David Freeman Engstrom & Daniel E. Ho, Algorithmic Accountability in the Administrative State<\/em>, 37 Yale J. on Reg. 800, 821 (describing AI-based tools as \u201cblack box[es]\u201d which may erode overall accountability); see also <\/em>Kim Lyons, Fetterman joins lawmakers\u2019 letter urging DOJ to stop funding so-called \u2018predictive\u2019 policing tools<\/em>, Pa. Capital-Star (Jan. 29, 2024).<\/p>\n\n\n\n

[10]<\/sup><\/a> Jeremy E. Abay, Simon Says Protect My Reputation: Understanding Pennsylvania\u2019s Constitutional Right to Reputation<\/em>, 94 Pa. B. A. Q. 68, 71 (Apr. 2023).<\/p>\n\n\n\n

[11]<\/sup><\/a> Meas, supra <\/em>note 3.<\/p>\n\n\n\n

[12]<\/sup><\/a> Simon v. Commonwealth, 659 A.2d 631 (Pa. Commw. 2012); Abay, supra <\/em>note 10 at 69\u201370. <\/p>\n\n\n\n

[13]<\/sup><\/a> Hunter v. Port Auth., 419 A.2d 631 (Pa. Super. 1980). <\/p>\n\n\n\n

[14]<\/sup><\/a> In re J.B., 107 A.3d 1 (Pa. 2014).<\/p>\n\n\n\n

[15]<\/sup><\/a>\u00a0Winters,\u00a0supra\u00a0<\/em>note 9, at 337; Rashida Richardson,\u00a0Defining and Demystifying Automated Decision Systems<\/em>, 81\u00a0Md. L. Rev.\u00a0785, 826\u201327 (2022).<\/p>\n\n\n\n

[16]<\/sup><\/a> Namrata Kakade, Sloshing Through the Factbound Morass of Reasonableness: Predictive Algorithms, Racialized Policing, and Fourth Amendment Use of Force<\/em>, 88 Geo. Wash. L. Rev. 788, 797 (2020).<\/p>\n\n\n\n

[17]<\/sup><\/a> Renata M. O\u2019Donnell, Challenging Racist Predictive Policing Algorithms Under the Equal Protection Clause<\/em>, 94 N.Y.U. L. Rev. 544, 561 (2019).<\/p>\n\n\n\n

[18]<\/sup><\/a> Winters, supra <\/em>note 9, at 334, n. 27; Fleur G. Ok\u00e9, The Minority Report: How the Use of Date in Law<\/em>, 63 How. L. J. 87, 105 (discussing the \u201crunaway feedback loops\u201d that occur from predictive policing).<\/p>\n\n\n\n

[19]<\/sup><\/a> Kakade, supra <\/em>note 16, at 796.<\/p>\n\n\n\n

[20]<\/sup><\/a> Id.<\/em>; see also <\/em>Matthew D. Zampa, Caught in the Crosshairs: Predictive Policing and the Use of Force<\/em>, 59 U.S.F. L. Rev. 371, 381 (2025) (\u201cThe records . . . are then \u2018fed\u2019 to software, which sorts the data, identifies patterns, and returns a result that reflects the department\u2019s preexisting racial biases\u201d).<\/p>\n\n\n\n

[21]<\/sup><\/a> Elizabeth E. Joh, Police Technology Experiments<\/em>, 125 Colum. L. Rev. F. 1, 10 (Jan. 31, 2025).<\/p>\n\n\n\n

[22]<\/sup><\/a> Id. <\/em>at  41\u201342; see also <\/em>Andrew Keane Woods, ROBOPHOBIA<\/em>, 93 U. Col. L. Rev. 51, 51\u201352 (\u201cWe are entering an age when one of the most important policy questions will be how and where to deploy machine decision-makers\u201d). <\/p>\n\n\n\n

[23]<\/sup><\/a> O\u2019Donnell, supra <\/em>note 17 at 562; Richardson, supra <\/em>note 15 at 832.<\/p>\n\n\n\n

[24]<\/sup><\/a> Winters, supra <\/em>note 9, at 328; Artificial Intelligence and Criminal Justice Final Report, U.S. Dep\u2019t of Just., p. 65 (Dec. 3, 2024).<\/p>\n\n\n\n

[25]<\/sup><\/a> Id.<\/em><\/p>\n\n\n\n

[26]<\/sup><\/a> See infra<\/em> notes 4\u20135.<\/p>\n\n\n\n

[27]<\/sup><\/a> Artificial Intelligence and Criminal Justice Final Report, U.S. Dep\u2019t of Just., p. 50 (Dec. 3, 2024) (\u201cAgencies should also ensure a central role <\/em>for humans in choosing which data to use as inputs, deciding which metric to use for evaluating accuracy . . . and determining which practices to implement in response to predictions\u201d (emphasis added)).<\/p>\n\n\n\n

[28]<\/sup><\/a> Ok\u00e9, supra <\/em>note 18 at 109.<\/p>\n\n\n\n

[29]<\/sup><\/a> Artificial Intelligence and Criminal Justice Final Report, U.S. Dep\u2019t of Just., p. 65 (Dec. 3, 2024).<\/p>\n\n\n\n

[30]<\/sup><\/a> Gambone v. Commonwealth, 101 A.2d 634, 637 (1954).<\/p>\n\n\n\n

[31]<\/sup><\/a> Winters, supra <\/em>note 9, at 347; See generally <\/em>Artificial Intelligence and Criminal Justice Final Report, U.S. Dep\u2019t of Just. pp. 67\u201369 (Dec. 3, 2024).<\/p>\n\n\n\n

[32]<\/sup><\/a> Kakade, supra <\/em>note 16, at 798.<\/p>\n","protected":false},"excerpt":{"rendered":"

By Chase Boss, Senior Editor \u201cTechnology can change the facts to which doctrine applies faster than courts can adjust the doctrine.\u201d[1] AI algorithms are bound to be a prime example; this technology may very well alter the application of a right unique to citizens of the Keystone State\u2014the right to reputation. [\u2026] <\/p>\n

<\/div>\n

Read More<\/a><\/p>\n","protected":false},"author":1,"featured_media":14999,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[3800,2345,900,1441],"class_list":["post-14998","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-juris-features","tag-algorithms","tag-civil-rights","tag-pennsylvania-constitution","tag-surveillance"],"_links":{"self":[{"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/posts\/14998","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/comments?post=14998"}],"version-history":[{"count":1,"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/posts\/14998\/revisions"}],"predecessor-version":[{"id":15000,"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/posts\/14998\/revisions\/15000"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/media\/14999"}],"wp:attachment":[{"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/media?parent=14998"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/categories?post=14998"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.law.duq.edu\/juris\/wp-json\/wp\/v2\/tags?post=14998"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}