翻訳待ち：Professional AI's Dual Trust Problem

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

Balkinization --> Balkinization: Professional AI’s Dual Trust Problem Balkinization &nbsp Front page Balkin.com Balkinization an unanticipated consequence of Jack M. Balkin link 1 link 2 etc. --> Balkinization Symposiums: A Continuing List &nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp &nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp &nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp E-mail: Jack Balkin: jackbalkin at yahoo.com Bruce Ackerman bruce.ackerman at yale.edu Ian Ayres ian.ayres at yale.edu Corey Brettschneider corey_brettschneider at brown.edu Mary Dudziak mary.l.dudziak at emory.edu Lee Epstein lee-epstein at northwestern.edu --> Paul Finkelman pfink at albanylaw.edu --> Joey Fishkin joey.fishkin at gmail.com Heather Gerken heather.gerken at yale.edu Abbe Gluck abbe.gluck at yale.edu Mark Graber mgraber at law.umaryland.edu Stephen Griffin sgriffin at tulane.edu Jonathan Hafetz jonathan.hafetz at shu.edu Bernard Harcourt harcourt at uchicago.edu --> Scott Horton shorto at law.columbia.edu --> Jeremy Kessler jkessler at law.columbia.edu Andrew Koppelman akoppelman at law.northwestern.edu Marty Lederman msl46 at law.georgetown.edu Sanford Levinson slevinson at law.utexas.edu David Luban david.luban at gmail.com Gerard Magliocca gmaglioc at iupui.edu Jason Mazzone mazzonej at illinois.edu Linda McClain lmcclain at bu.edu John Mikhail mikhail at law.georgetown.edu Neil Netanel netanel at law.ucla.edu --> Frank Pasquale pasquale.frank at gmail.com Nate Persily npersily at gmail.com Michael Stokes Paulsen michaelstokespaulsen at gmail.com Deborah Pearlstein dpearlst at yu.edu Rick Pildes rick.pildes at nyu.edu David Pozen dpozen at law.columbia.edu Richard Primus raprimus at umich.edu K. Sabeel Rahman sabeel.rahman at brooklaw.edu Alice Ristroph alice.ristroph at shu.edu Neil Siegel siegel at law.duke.edu Kim Scheppele kimlane at princeton.edu --> David Super david.super at law.georgetown.edu Brian Tamanaha btamanaha at wulaw.wustl.edu Nelson Tebbe nelson.tebbe at brooklaw.edu Mark Tushnet mtushnet at law.harvard.edu Adam Winkler winkler at ucla.edu Compendium of posts on Hobby Lobby and related cases The Anti-Torture Memos: Balkinization Posts on Torture, Interrogation, Detention, War Powers, and OLC The Anti-Torture Memos (arranged by topic) Recent Posts Professional AI’s Dual Trust Problem Just A Few Blogs ACS Blog Alas, a Blog Althouse Arts and Letters Daily Atrios (Eschaton) Bill of Health Buzzflash.com Buzz Machine Cato at Liberty Juan Cole (Informed Comment) Concurring Opinions The Constitution in 2020 Corrente Crooked Timber Daily Howler Daily Kos Dana Boyd Brad DeLong Digby (Hullabaloo) Discriminations Daniel Drezner Kevin Drum (Mother Jones) Electrolite En Banc Eunomia (Daniel Larison) Fafblog Michael Froomkin (Discourse.net) GovLab (Beth Noveck) Rick Hasen (Election Law) History News Network How Appealing Ignatz (Sam Heldman) The Importance of (Ernie Miller) Infolaw Instapundit International Economic Law and Policy Blog IntLawGrrls Jacob Levy Jesus' General Jurisdynamics The Kitchen Cabinet Mark Kleiman Law Blog Central Larry Lessig Lawyers, Guns and Money Liberal Oasis Brian Leiter's Law School Reports The Leiter Reports Marginal Revolution Megan McArdle Memeorandum Metafilter Mirror of Justice The New Republic Newseum No More Mister Nice Blog Brendan Nyhan Opinio Juris Orcinus The Originalism Blog Pandagon Passport (Foreign Policy) Overcoming Bias Political Animal (Washington Monthly) Political Theory Daily Review Political Wire (Taegan Goddard) The Poor Man Virginia Postrel Prawfsblawg Public Reason Jonathan Rauch Raw Story Redstate ReligiousLeftLaw.com Reporters Committee For Freedom of the Press Reproductive Rights Blog Rothman's Roadmap to the Right of Publicity SCOTUS Blog Seeing the Forest Clay Shirky The Shifted Librarian The Situationist Larry Solum (Legal Theory) Andrew Sullivan Talking Points Memo Talk Left Tapped Tbogg TechPresident The Paper Chase (Jurist) Tom Paine Tom Tomorrow (This Modern World) Eve Tushnet Uggabugga University of Chicago Law School Faculty Blog Unqualified Offerings The Volokh Conspiracy War and Piece (Laura Rozen) Wampum Oliver Willis Wonkette Written Description Matthew Yglesias Yin Your Choice of Feeds 1. XML powered by 2. Atom Feed 3. RSS 2.0 Tuesday, September 01, 2026 Professional AI’s Dual Trust Problem Guest Blogger For the Balkinization Symposium on the Global Political Economy of Artificial Intelligence. Claudia E. Haupt Professional AI’s Dual Trust Problem Each day, more than forty million people ask ChatGPT health questions. When OpenAI and Anthropic launched dedicated health AI tools that let users upload their medical records and receive personalized guidance, the obvious question was: “Should you trust them?” The question has only grown more pressing: since early 2026, five major technology companies (OpenAI, Anthropic, Microsoft, Amazon, and Perplexity) have released or expanded dedicated consumer-facing AI health applications, each allowing users to connect medical records, lab results, and wearable data to receive personalized guidance. That question, it turns out, has more than one layer. Instinctively, we might assume the concern is about output accuracy: will the AI give bad professional advice? But a second, perhaps less obvious, problem arises that reaches beyond any individual bad outcome. Untrustworthy AI undermines the entire system of trust that makes human professional advice work in the first place. It’s a dual trust problem. I examine professional AI’s dual trust problem in a forthcoming essay, Misplaced Trust in Artificial Professional Advice, which makes the argument in two steps. The First Problem: AI’s Professional Advice Isn’t Trustworthy The professional relationship with a doctor, lawyer, financial advisor, accountant, pharmacist, therapist, or another advice-giving professional is a specific social interaction. The professional possesses knowledge the client lacks; this results in a knowledge asymmetry that creates vulnerability. The law responds with a set of safeguards: licensing requirements, fiduciary duties, malpractice liability, informed consent. These mechanisms protect the conditions under which a client or patient can reasonably place confidence in a professional’s expertise. Public-facing AI eliminates the human professional. What remains looks like professional advice: conversational, personalized, authoritative in tone. But the legal and ethical framework that ensures professional advice is trustworthy is absent. The accuracy problem is real and documented: as of 2024, no commercially available AI app met professional standards for skin cancer detection. Earlier studies on general health queries found frequent errors, and more recent work reinforced those findings. A study published in Nature Medicine found that participants using AI chatbots to navigate common medical scenarios performed no better than a control group relying on ordinary home resources such as internet searches—and users describing the same symptoms sometimes received conflicting advice depending on how they phrased their questions. A separate Mount Sinai study found that ChatGPT Health under-triaged more than half of medical emergencies in structured clinical testing, potentially directing patients with serious conditions toward routine follow-up rather than urgent care. But accuracy is not even the core issue. Trust is an attitude; trustworthiness is a property. And as Ignacio Cofone argues in a companion piece to this symposium, as well as in more detail in a forthcoming article,[1] trustworthiness is a property of institutions, not of AI systems. The professional relationship, not the chatbot, carries that institutional trustworthiness. The Second Problem: Untrustworthy AI Undermines Trust in Human Professionals When a patient consults a public-facing AI and then sees a physician whose advice diverges, the patient faces a question they are not equipped to answer: who is right? And behind that question lurks a more unsettling one: where does expertise actually live? The proliferation of AI that mimics professional judgment creates epistemic uncertainty about institutional expertise itself. Beyond harming individual users, the AI systematically undermines confidence in the professionals it displaces or contradicts. The problem is structural, rooted in the same inequities of access that drive people to seek AI as a substitute for healthcare in the first place. Many people turn to AI health tools precisely because they lack access to affordable human care. A March 2026 KFF tracking poll found that about one in five adults who use AI for health advice cite inability to afford a provider as a major reason, a figure that rises to nearly three in ten among users ages 18 to 29. Uninsured adults are more than twice as likely as insured adults to rely on AI for mental health guidance. And the pattern tracks race: Black and Hispanic adults turn to AI for mental health advice at substantially higher rates than White adults. Viewed this way, AI health tools are an attempted patch for a broken system. The trust being displaced was already fragile, and unevenly distributed across race, income, and geography. Worse, the AI health tools with the most personalized features—those enabling direct integration with medical records—are increasingly behind paywalls, potentially placing them out of reach for those who are already struggling to afford care. What consumer-facing health AI offers is not a substitute for the human professional relationship. As the law and political economy literature would recognize, the roots of this problem predate AI. First Amendment doctrine, as I have argued elsewhere, has long assumed the availability of professional advice without reckoning with its unequal distribution.[2] This assumption places a heavier burden on those who can least afford expert counsel and who are most dependent on publicly available information (however unreliable) as a substitute. Consumer-facing health AI does not solve this problem; it exploits it, offering a widely available facsimile of expert advice. And the data suggests it’s relied on by users for whom the absence of access to professional advice was already most consequential. Trust in Institutions, Not AI The stakes extend beyond individual harm to institutional erosion. As Woodrow Hartzog and Jessica Silbey argue, AI has the capacity to destroy the civic and professional institutions on which public life depends.[3] It may do so by steadily undermining the trust that sustains them. The professions are no exception. Professional expertise generates trust because it is grounded in training, accountable to standards, and answerable to the people it serves. Deploying AI that mimics expertise without embodying any of those properties creates bad individual outcomes and casts doubt on where expertise lives. The question, then, is not simply whether we should trust AI. It is whether deploying untrustworthy AI erodes the very institutions whose trustworthiness we depend on, and what regulatory frameworks built around human professional relationships can do about it. Claudia E. Haupt is Professor of Law and Political Science, Northeastern University. You can reach her by e-mail at [email protected]. [1] Ignacio Cofone, Institutional Accountability and Legitimate Inference in Algorithmic Adjudication: Beyond Trustworthy AI, Cambridge Forum on AI Law and Governance (forthcoming 2026), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6516459 [2] Claudia E. Haupt, Assuming Access to [truncated for AI cost control]