paper_title: Governing AI Beyond the Grave

authors: Alberto B. Lopez and Yonathan A. Arbel

citation: Alberto B. Lopez & Yonathan A. Arbel, Governing Generative AI Beyond the Grave (Florida State University Law Review forthcoming 2025), SSRN No. 5380233

source_type: August 2025 working-paper PDF

section: Introduction

claim: Post-mortem generative emulation creates a digital-aristocracy problem because ordinary people are vulnerable to realistic resurrection but lack celebrities’ legal and planning protections

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 3–6, that post-mortem generative emulation, or GenEm, can synthesize a dead person’s likeness, voice, style, and conversational behavior with enough fidelity to support suspended disbelief. Publicity statutes and bespoke estate plans may protect famous or wealthy people, but ordinary people leave similarly exploitable digital footprints while often lacking a commercially valuable lifetime identity, a will, or any plan for digital remains. This is significant because a regime tied to celebrity, prior commercialization, or expensive planning produces a digital aristocracy precisely when consumer tools make high-fidelity emulation broadly possible. It connects to deadbots, digital resurrection, posthumous publicity rights, estate planning, digital remains, dignity, privacy, and distributive equality.

significance: The claim reframes digital resurrection as a general problem of posthumous dignity and unequal legal protection rather than a niche issue concerning celebrity publicity rights.

connections:
post-mortem generative emulation
deadbots
right of publicity
estate planning
digital remains
posthumous dignity
distributive equality

limitations: The article argues that ordinary people are increasingly exposed, but the actual fidelity and availability of consumer GenEm will vary with the volume and quality of data left by each person.

evidence_summary: The introduction defines GenEm, contrasts memorial and exploitative uses, surveys publicity and trademark limits, notes widespread intestacy and missing digital-estate plans, and uses unauthorized manipulation of an ordinary decedent’s image to illustrate the protection gap.

review_status: machine-drafted-source-checked


claim: GenEm governance must allocate both control over the source identity and authority over particular newly generated uses

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 6–8, that GenEm is not merely preservation or reproduction because it creates new performances and statements that never existed during life. That generative step complicates ordinary property logic: an estate may control source photographs, recordings, or writings, while a later user claims the new output. A workable regime therefore must answer two distinct questions—who may control or activate a decedent’s digital remains, and which memorial, educational, recreational, commercial, political, or other uses are permissible. This is significant because assigning ownership alone cannot protect autonomy or dignity when the principal harm lies in what an emulation is made to say or do. It connects to accession, transformative use, digital estates, testamentary intent, purpose-based restrictions, dignity, and the difference between source data and generated output.

significance: The two-dimensional framework prevents courts from treating control of inputs as blanket authorization for every downstream portrayal or message.

connections:
source data
generated outputs
accession
transformative use
testamentary intent
purpose-based regulation
digital estates

limitations: The article proposes presumptions for users and uses rather than a complete theory of ownership in every input or output, and it leaves some borderline categories for adjudication.

evidence_summary: The introduction explains that GenEm can produce new material, identifies the source-versus-output entitlement problem, separates the identity of the controller from the nature of the use, and previews a tailored default based on both dimensions.


section: Part I, Technology, e-Mortality, and Novelty

claim: GenEm is qualitatively different from older mimicry because transformer systems can generate authentic-seeming conduct rather than merely replay recorded traces

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 9–11, that photography, sound recording, and film preserved fixed traces, whereas contemporary generative systems can produce a person seeming to respond, reason, and perform in circumstances never captured during life. They describe this as computational verisimilitude: the output can feel authentic enough to move preservation toward apparent resurrection. Because transformer models learn role-consistent patterns through next-token prediction and attention, roleplay is an emergent feature of the general architecture rather than a detachable novelty module. This is significant because regulators cannot assume that persona emulation can be cleanly prohibited by removing a single product feature after the fact. It connects to transformer architecture, emergent behavior, roleplay, deepfakes, digital identity, fraud, privacy, and the qualitative difference between reproduction and generation.

significance: The technological account explains why GenEm presents new governance problems even though humans have long imitated and recorded the dead.

connections:
transformer models
next-token prediction
emergent roleplay
computational verisimilitude
deepfakes
digital identity
privacy

limitations: The paper gives a functional legal account of model behavior rather than proving that every model or deployment achieves the same degree of persona fidelity.

evidence_summary: Part I contrasts fixed historical media with generative outputs and explains how attention-based next-token systems acquire roleplaying capacity as part of their ordinary operation.


section: Part I.B, Assessing Roleplaying Capabilities

claim: Evidence of population, personality, and individual emulation supports GenEm’s practical plausibility while revealing fidelity and bias tradeoffs

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 12–15, that language models already reproduce aggregate moral judgments, survey patterns, personality profiles, developmental behavior, and aspects of particular people with meaningful accuracy. They highlight a small personal Turing test in which acquaintances mistook model-generated answers for the target person’s answers in 48.3 percent of trials. Yet faithful mimesis presents a paradox: reproducing a person or population may also reproduce bias, while safety-oriented debiasing can flatten or homogenize marginalized identities and reduce fidelity. This is significant because GenEm’s plausibility and its ethical defects grow from the same capacity to infer identity from data. It connects to persona simulation, psychometrics, algorithmic bias, representational harm, personal Turing tests, safety alignment, and the quantity and quality of digital footprints.

significance: The evidence grounds the paper’s legal proposal in demonstrated representational capacity while cautioning that technical accuracy is neither neutral nor uniformly distributed.

connections:
persona simulation
psychometrics
personal Turing test
algorithmic bias
representational harm
model alignment
digital footprints

limitations: The cited studies use different metrics and settings, the personal Turing test is small, and performance on experimental personas does not guarantee a faithful emulation of any particular decedent.

evidence_summary: The section surveys studies of moral judgment, surveys, personality, character evaluation, developmental simulation, and individual imitation, then identifies bias and marginalized-group homogenization as limits on faithful roleplay.


section: Part II.A, A Default Rule Theory Primer

claim: An evolutionary default can supply immediate GenEm protection while generating evidence that lets courts and legislatures revise the rule as preferences mature

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 16–21, that GenEm regulation confronts an exploration-exploitation dilemma: society needs protection now, but later experience will reveal better rules. Their evolutionary default answers both needs by applying broadly without requiring an existing publicity right or sophisticated estate plan and by turning disputes, opt-outs, and judicial decisions into evidence for future refinement. Unlike a conventional majoritarian default that merely reflects known preferences or a penalty default that forces information between private parties, this default is designed to help public institutions learn. This is significant because it treats legal adaptation itself as part of the rule’s function in a field where both technology and social norms remain unsettled. It connects to adaptive governance, experimentalist regulation, majoritarian defaults, penalty defaults, common-law learning, epistemic humility, and the exploration-exploitation tradeoff.

significance: The evolutionary feature is the paper’s distinctive contribution to default-rule theory: immediate protection and institutional learning are jointly designed rather than treated as opposing goals.

connections:
evolutionary defaults
adaptive governance
exploration-exploitation dilemma
majoritarian defaults
penalty defaults
common-law learning
epistemic humility

limitations: The learning function depends on observable disputes and reliable interpretation of opt-outs; litigated cases may not represent people who lack the resources or awareness to contest misuse.

evidence_summary: Part II introduces the exploration-exploitation analogy, reviews why contractual incompleteness makes defaults inevitable, and distinguishes the proposed institution-facing information function from traditional majoritarian and penalty theories.


claim: Default design requires choices about majority preference, information forcing, and alterability, all under severe informational constraints

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 17–20, that defaults matter because parties cannot anticipate or cheaply draft for every contingency, and the selected background rule shapes behavior before any dispute occurs. Majoritarian defaults economize on transaction costs by supplying what most people would choose; penalty defaults deliberately supply an unwanted term to elicit private information; and altering rules determine whether either kind is sticky or slippery. Each design, however, requires information lawmakers may not possess about actual preferences, reactions, and opt-out costs. This is significant because a GenEm rule cannot be justified simply by calling it a default—the content and mechanics must respond to predictable information failures and unequal ability to opt out. It connects to incomplete contracts, majoritarian and penalty defaults, transaction costs, altering rules, status quo bias, information revelation, and access to legal planning.

significance: The taxonomy supplies the evaluative criteria used later to explain why a user-and-use framework should be presumptive, rebuttable, and capable of revision.

connections:
incomplete contracts
majoritarian defaults
penalty defaults
altering rules
transaction costs
status quo bias
information revelation

limitations: The discussion draws principally from contract-default scholarship, whose bilateral bargaining assumptions do not transfer intact to unilateral testamentary decisions.

evidence_summary: The primer explains inevitable gaps, majority-preference and penalty approaches, the difficulty of measuring preferences and strategic responses, and the tradeoff between sticky and slippery opt-out mechanisms.


section: Part II.B, Default Rules in Wills and Trusts

claim: The dead-hand, unilateral, and systematically opt-out-prone character of wills pushes testamentary defaults toward probable intent rather than bargaining-based information forcing

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 21–22, that wills differ structurally from contracts in three linked respects: the instrument becomes operative when its author can no longer renegotiate or explain it; it expresses a unilateral disposition rather than a bilateral bargain; and estate planners often distrust defaults enough to opt out mechanically. Those features make penalty-default logic comparatively weak because there is no living counterparty negotiation through which an undesirable rule can elicit useful disclosure. This is significant because post-mortem gaps must be governed by the law’s best estimate of decedent intent at the moment correction is impossible. It connects to dead-hand control, testamentary intent, unilateral dispositions, will construction, estate-planning boilerplate, majoritarian defaults, and the limits of contract analogy.

significance: The argument supplies a domain-specific reason to center the GenEm default on probable decedent preference while retaining rebuttability for individual circumstances.

connections:
dead-hand control
testamentary intent
unilateral dispositions
will construction
estate-planning boilerplate
majoritarian defaults
contract analogy

limitations: Trusts and estates contain exceptions and information-producing mechanisms, so the authors characterize the majoritarian orientation as dominant rather than absolute.

evidence_summary: The section identifies the impossibility of post-death modification, the unilateral nature of wills, and planners’ distrust of legal defaults, then explains why courts treat testator intent as the guiding pole star.


section: Part II.C, Two Majoritarian Defaults in Wills Law

claim: Intestacy illustrates both the power of a majoritarian default and the danger that a once-plausible family model can lag changing social relationships

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 23–25, that intestacy is the foundational testamentary default: for the many people who die without a will, statutes effectively write an estate plan prioritizing spouses, children, parents, and collateral kin. Empirical work broadly supports that ordering for conventional families, but static kinship categories exclude growing numbers of unmarried partners, stepchildren, and other relationships. The statutory order also helps constitute future norms by signaling what counts as family. This is significant because a default can be broadly majoritarian yet become progressively less representative as the population and its relationships change. It connects to intestate succession, probable intent, family definition, unmarried partners, blended families, expressive law, demographic change, and periodic empirical recalibration.

significance: Intestacy provides the model for broad coverage and the warning that durable defaults must be reassessed rather than mistaken for timeless preference.

connections:
intestate succession
probable intent
family definition
unmarried partners
blended families
expressive law
demographic change

limitations: The cited survey evidence supports the statutory structure for a majority, not every household, and property outside probate may follow different beneficiary designations.

evidence_summary: The section describes intestacy as an opt-out regime, explains its probable-intent rationale and conventional order, cites empirical fit, and shows how cohabitation and blended families expose growing exclusions.


claim: Anti-lapse law shows how an asserted majoritarian default can systematically contradict measured testamentary preferences

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 25–26, that anti-lapse statutes were enacted to improve on the common-law rule sending failed gifts to a residuary estate, but empirical studies often find that testators prefer surviving children over descendants of a predeceased child—the reverse of the statutory result. The statutes can therefore operate as accidental penalty defaults: they impose unwanted outcomes without deliberately producing useful disclosure, and some are sticky enough to be difficult to displace. This is significant because good intentions and the label of probable intent do not establish actual majoritarian fit. It connects to lapse, anti-lapse statutes, empirical wills scholarship, accidental penalty defaults, legislative revision, default stickiness, and the need to test GenEm presumptions against observed preferences.

significance: The anti-lapse mismatch makes empirical measurement and future recalibration central safeguards for any newly constructed GenEm default.

connections:
lapse
anti-lapse statutes
empirical wills scholarship
accidental penalty defaults
default stickiness
probable intent
legislative revision

limitations: Surveyed preferences about lapse vary by beneficiary relationship and study design, so the critique is evidence of systematic mismatch rather than proof of one universal preferred rule.

evidence_summary: The article traces the judicial lapse rule, the legislative anti-lapse response, and empirical findings that statutory presumptions are over- and under-inclusive and may reverse common preferences.


section: Part III.A, Prior Empirical Data

claim: Prior evidence strongly favors respecting express consent but does not justify a universal prohibitory default when consent is unknown

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 27–28, that the first empirical GenEm study revealed strong posthumous autonomy preferences: 89 percent rejected emulation contrary to a decedent’s wishes, while objection fell sharply when the use matched those wishes. But its recommendation of an opt-in-only default rested on vignettes that treated informal messages as consent, limited users to friends, and did not vary family, commercial, public, or substantive uses. Preferences may also change as respondents become familiar with the technology. This is significant because opposition under one under-specified scenario cannot answer the legally distinct questions of who may use digital remains and for what purpose. It connects to informed consent, posthumous autonomy, opt-in defaults, external validity, preference formation, user identity, use context, and empirical legal design.

significance: The critique motivates a factorial, context-sensitive survey rather than dismissing prior work or importing its blanket prohibition into probate law.

connections:
posthumous autonomy
informed consent
opt-in defaults
external validity
preference formation
user identity
use context

limitations: The authors’ discussion of the earlier study is a methodological critique; their own survey likewise measures stated attitudes rather than actual testamentary choices over time.

evidence_summary: Part III reports the earlier study’s consent effects and unknown-consent rejection, then identifies limitations involving the form of consent, exclusive focus on friends, omitted purposes, and unstable preferences.


section: Part III.B, Survey Methods and Study 1 Design

claim: Matched AI and non-AI scenarios can separate objections to generative technology from objections to the underlying posthumous act

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 28–29, that legal design needs to know whether respondents experience an AI-specific moral aversion or instead condemn the same intrusion regardless of technique. Their first study therefore randomly presented matched third-person scenarios involving intimate images, avatars and journals, jokes, advertising, voices and songs, films, and grief counseling, with the decedent leaving no will or instructions. The 156-person Positly sample was selected and screened to approximate national demographics and reduce AI-generated responses. This is significant because a blanket GenEm prohibition would be poorly targeted if disapproval is primarily use-specific rather than technology-specific. It connects to experimental design, paired comparisons, the AI-Ick hypothesis, third-person framing, nationally representative sampling, technological neutrality, and posthumous consent.

significance: The design turns a vague intuition about creepy technology into a comparison between the moral effect of generation and the moral effect of the underlying conduct.

connections:
AI-Ick hypothesis
paired comparisons
experimental design
third-person framing
technological neutrality
posthumous consent
survey sampling

limitations: The sample contains modest demographic deviations, has 156 respondents, and uses hypothetical third-person judgments that may differ from actual decisions or first-person testamentary preferences.

evidence_summary: The methods section describes recruitment, demographic alignment and deviations, human-response safeguards, random ordering, the paired scenarios, and the absence of express decedent instructions.


section: Part III.B.1, Study 1: Third Person Preferences

claim: The survey finds no general AI-Ick: posthumous acceptance depends principally on the act and context, although generation intensifies objection in some visual uses

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 29–32, that respondents did not reject AI versions of posthumous representation across the board. They preferred conversational avatars to publishing private journals and AI-supported grief counseling to the traditional comparison; they judged AI and non-AI memorial songs or jokes almost identically. Yet AI-generated intimate images and digital film appearances were materially less acceptable than discovered images or lookalike actors, suggesting that creating an unreal visual performance can cross an additional moral line. This is significant because the evidence rejects both categorical technological panic and categorical equivalence: the underlying use generally drives judgment, but generation can aggravate particular violations. It connects to contextual privacy, dignitary harm, intimate imagery, grief technology, synthetic performances, technological neutrality, and graduated rights.

significance: The results support a purpose-sensitive regime capable of distinguishing comforting mediation, ordinary appropriation, and fabrication that makes a decedent appear to act.

connections:
contextual privacy
dignitary harm
intimate imagery
grief counseling
synthetic performances
technological neutrality
graduated rights

limitations: The paired scenarios are not identical in every non-AI respect—for example, discovery differs from fabrication and a lookalike differs from a digital insertion—so differences should not be attributed to a single technical feature with certainty.

evidence_summary: Study 1 reports means and significance tests for intimate images, avatars and journals, grief counseling, films, songs, and jokes, then interprets both significant differences and null results as evidence of contextual judgment.


section: Part III.B.2, Study 2: First Person Preferences

claim: Respondents consistently prefer family control over public control of their own posthumous emulations

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 32–35, that first-person preferences turn strongly on the identity of the user. Overall acceptance was mildly skeptical at 40.98, but family uses averaged 47.7 compared with 34.33 for public uses, a highly significant difference that persisted across memorial, educational, recreational, research, and commercial categories. Family memorialization reached the study’s highest acceptance score, 66.82, while public memorialization fell to 44.66. This is significant because the same nominal purpose can be understood as intimate legacy within a family and appropriation when performed by strangers. It connects to relational privacy, family stewardship, testamentary intent, contextual integrity, digital remains, probate priority, and user-classified defaults.

significance: The repeated family-public gap supplies the empirical basis for making family identity the first prong of the proposed GenEm presumption.

connections:
family stewardship
relational privacy
testamentary intent
contextual integrity
digital remains
probate priority
user identity

limitations: Family is treated as a broad survey category even though actual families may be conflicted, estranged, abusive, or differently situated, and the study does not resolve priority among relatives.

evidence_summary: Study 2 compares aggregate family and public scores and reports purpose-specific interaction results showing a roughly 15-to-20-point family advantage across nearly every category.


claim: Purpose independently structures posthumous AI preferences, with memorial and educational uses favored and political and commercial uses rejected

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 33–35, that respondents distinguished sharply among purposes for their own posthumous avatars. Memorialization scored 55.7 and education 54.4, statistically indistinguishable results suggesting that preserving knowledge can resemble preserving memory. Commercial use fell to 30.6 and political use to 25.1; unlike other categories, political use remained strongly disfavored whether controlled by family or the public. Recreational use occupied a middle ground. This is significant because neither a property transfer to family nor an abstract consent to emulation captures the decedent’s likely view of what the persona may be made to do. It connects to memorialization, educational legacy, political appropriation, commercial exploitation, purpose limitation, knowledge preservation, and use-classified defaults.

significance: The purpose hierarchy supplies the second prong of the proposal and identifies political speech as a particularly sensitive form of posthumous appropriation.

connections:
memorialization
educational legacy
political appropriation
commercial exploitation
purpose limitation
knowledge preservation
use identity

limitations: The findings are current stated preferences from one sample; categories such as education, research, politics, and commerce can overlap or be framed differently in concrete disputes.

evidence_summary: Study 2 reports purpose means, Tukey comparisons, and user-purpose interactions, emphasizing the equivalence of memorial and educational uses and the uniformly low political scores.


section: Part IV, Tailored Default Rules for GenEm

claim: Probate courts should serve as first responders to GenEm disputes because they can supply an immediate default within an institution already built to administer post-mortem control

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 36–37, that probate courts will encounter GenEm disputes before legislatures can complete a slow statutory response and should therefore take the first pass at deciding who may activate a decedent’s digital identity and for what purposes. This role fits probate rather than being an alien expansion: wills, defeasible estates, support trusts, and spendthrift trusts have long enforced conditions on how property may be used after death, without limiting such control to celebrities or lifetime commercialization. This is significant because immediate, general protection can arise from existing adjudicative authority while still provoking later legislative correction. It connects to probate jurisdiction, judicial gap filling, dead-hand control, conditional gifts, trusts, institutional competence, and court-legislature dialogue.

significance: The proposal identifies a practical institution capable of supplying protection now and a familiar doctrinal home for purpose-limited post-mortem control.

connections:
probate courts
judicial gap filling
dead-hand control
conditional gifts
trusts
institutional competence
court-legislature dialogue

limitations: The article argues for judicial initiative but does not establish that every state probate court has identical jurisdiction, remedial authority, or procedural capacity over third-party platform and AI disputes.

evidence_summary: Part IV describes legislative delay, identifies probate judges as frontline decisionmakers, and analogizes GenEm restrictions to established conditions on probate and nonprobate transfers.


section: Part IV.A, The Proposed Framework

claim: Common-law adaptation can bridge a dangerous decade-long lag between technological harm and comprehensive probate legislation

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 37–40, that courts have historically adapted old doctrines to new technologies and created defaults when positive law left consequential gaps. Probate itself moved from a judicial lapse rule to anti-lapse statutes, while the digital-assets experience shows the cost of waiting: public disputes over access arose years before uniform-law work and widespread adoption of RUFADAA, a process exceeding a decade. GenEm capability is advancing too quickly to leave decedents unprotected through a similar interval. This is significant because judicial defaults can function as provisional infrastructure rather than an assertion that courts should have the final word. It connects to common-law evolution, technological disruption, RUFADAA, fiduciary access to digital assets, lapse doctrine, legislative latency, and provisional regulation.

significance: The historical examples provide an institutional justification for judicial action now followed by empirical and legislative refinement later.

connections:
common-law evolution
technological disruption
RUFADAA
fiduciary access
lapse doctrine
legislative latency
provisional regulation

limitations: Past adaptation does not guarantee doctrinal authority or uniform results in GenEm cases, and rapid judicial action can itself produce divergent or poorly informed rules.

evidence_summary: The section uses remote will execution, common-law contract defaults, lapse and anti-lapse, and the decade-long path to RUFADAA to illustrate both legal adaptability and legislative delay.


claim: The GenEm default should combine categorical ex ante presumptions with case-specific rebuttal: family memorial and educational uses are allowed, while public, commercial, and political uses are barred

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 40–43, that the binary defaults common in wills law cannot represent survey preferences that change with both user and use. Their two-prong default presumptively permits family members to employ a decedent’s digital remains for memorialization and education, presumptively prohibits family commercial and political uses, and presumptively bars public use. Unlike an open-ended tailored standard discovered only after litigation, these classifications announce rules ex ante, yet a will, documented preference, or sufficient case-specific evidence can reverse a presumption. This is significant because the hybrid captures much of the predictability of a rule, the fit of a tailored standard, and the disclosure incentive of a penalty default. It connects to tailored defaults, rebuttable presumptions, family stewardship, memorialization, educational use, commercial exploitation, political appropriation, and testamentary opt-outs.

significance: This is the operative doctrinal core of the article: a matrix of user and use replaces both laissez-faire transfer and blanket prohibition.

connections:
tailored defaults
rebuttable presumptions
family stewardship
memorialization
educational use
commercial exploitation
testamentary opt-outs

limitations: The categories can overlap, family members can disagree, and the article does not specify a complete evidentiary standard or priority hierarchy for every effort to rebut a presumption.

evidence_summary: The proposal contrasts binary and tailored defaults, states presumptions for family and public actors and for memorial, educational, commercial, and political purposes, and explains how express or circumstantial evidence can alter them.


claim: Rebuttability operationalizes testamentary intent when a decedent’s actual preference departs from the survey-based classification but was never formally recorded

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 42–43, that each user-and-use classification should be only a presumption rather than a conclusive command. A decedent may have wanted a public educational use, consented informally to family commercialization, or opposed education despite the majority pattern; a family member or other claimant may present sufficient evidence of that individual preference even when no will records it. The default remains the law’s best guess when evidence is absent, paralleling intestacy and lapse. This is significant because empirical majoritarianism serves autonomy only if it yields to credible proof about the person whose digital identity is at stake. It connects to rebuttable presumptions, individualized intent, evidentiary hearings, intestacy, will gaps, informal consent, and the distinction between defaults and mandatory rules.

significance: The rebuttal mechanism prevents a population average from becoming a mandatory moral judgment and gives courts a structured path for resolving atypical cases.

connections:
rebuttable presumptions
individualized intent
evidentiary hearings
intestacy
will gaps
informal consent
mandatory rules

limitations: The paper leaves questions about burdens, admissible evidence, proof thresholds, conflicts among survivors, and the risk that well-resourced users will rebut presumptions more successfully.

evidence_summary: The authors illustrate rebuttal with decedents who favored otherwise prohibited commercial or public educational uses and those who opposed presumptively permitted family education, then tie the structure to probate’s best-guess approach.


section: Part IV.B, Enforcing the GenEm Default with Technology

claim: Data control, preference registries, model guardrails, and provenance metadata can enforce GenEm limits before harmful outputs are generated

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 43–45, that GenEm enforcement should begin upstream because a personalized model needs source material. Private diaries, recordings, and similar high-fidelity inputs should remain estate-controlled and be categorically excluded absent express documented consent; third-party platforms should not be free to repurpose shared material for a new emulation. Models could then consult trusted registries recording permitted purposes, prohibitions, licenses, and estate authorizations, while output metadata links generated content to training sources and permissions. This is significant because a court order after viral dissemination cannot fully restore dignity, whereas data and model controls can make legal defaults preventative and auditable. It connects to post-mortem data rights, training-data governance, consent registries, model guardrails, provenance, platform duties, privacy, and privacy-enhancing system design.

significance: The technical layer translates the legal user-and-use matrix into operational checks that can prevent misuse and preserve evidence when prevention fails.

connections:
post-mortem data rights
training-data governance
consent registries
model guardrails
provenance metadata
platform duties
privacy by design

limitations: The authors acknowledge leaks, public data, partial emulation, registry governance, interoperability, and model compliance as practical challenges; the proposal does not specify a complete technical standard.

evidence_summary: The enforcement section distinguishes private and third-party-held source material, proposes expanded post-mortem control, describes dynamic emulation-rights registries, and calls for model checks and traceable output metadata.


section: Part IV.C, Legal Safeguards for the GenEm Default

claim: Injunctions and constructive trusts can stop unauthorized GenEm and strip benefits from public actors, heirs, or fiduciaries who violate the default

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on pages 45–47, that familiar equitable remedies can police the back end of GenEm governance. An estate can seek an injunction to halt a prohibited commercial, political, or other deployment before irreparable dignitary harm spreads. When a wrongdoer acquires revenue, control, or other identifiable benefit through an unauthorized use, a constructive trust can prevent unjust enrichment and require conveyance; the same logic reaches an heir, third party, or personal representative who breaches fiduciary duties by authorizing or profiting from misuse. This is significant because the proposal does not require courts to invent a wholly new remedial vocabulary for digital identity. It connects to injunctive relief, constructive trusts, unjust enrichment, estate property, fiduciary loyalty, right of publicity, political appropriation, and equitable tracing.

significance: Traditional probate and equity remedies give the default practical consequences against both outsiders and insiders who misuse a decedent’s digital persona.

connections:
injunctive relief
constructive trusts
unjust enrichment
fiduciary duty
estate property
right of publicity
equitable tracing

limitations: Constructive trusts ordinarily require identifiable property or benefit and litigation can be slow; speech defenses, jurisdiction, insolvency, anonymity, and noncommercial harms may complicate relief.

evidence_summary: The section explains injunctions, traces constructive trusts through fraud, undue influence, conversion, and fiduciary breach cases, and applies those remedies to unauthorized political and other GenEm uses.


section: Conclusion

claim: A layered evolutionary default should operate as a living legal algorithm that protects digital identity now and updates through experience

thick_description: Professors Alberto B. Lopez and Yonathan A. Arbel claim, in “Governing Generative AI Beyond the Grave” on page 48, that the proposed default is a starting architecture rather than a final code. Its initial family-and-memorial orientation implements current evidence about probable intent, while probate decisions can debug user and use categories as technology and norms change. Data rights and source controls, model guardrails and registries, and injunctions and equitable remedies form mutually reinforcing preventative and reactive layers. This is significant because neither a single property entitlement nor a static prohibition can remain accurate or enforceable across rapidly changing forms of digital persistence. It connects to evolutionary defaults, layered governance, adaptive probate law, model interpretability, global registry standards, digital personhood, AI-generated wills, algorithmic trusts, autonomy, dignity, and legacy.

significance: The conclusion integrates the empirical, doctrinal, technical, and remedial components into an adaptive system capable of protecting both current decedents and future forms of digital identity.

connections:
evolutionary defaults
layered governance
adaptive probate law
model interpretability
digital personhood
AI-generated wills
algorithmic trusts

limitations: The framework requires future institutional design, standards, jurisdictional coordination, and repeated empirical validation; it cannot guarantee perfect prevention or global enforcement.

evidence_summary: The conclusion characterizes the default as a living algorithm, restates family memorialization as the initial presumption, combines data, model, and legal controls, and extends the framework to future digital-personhood disputes.