The Argument

Artificial intelligence is rapidly expanding the supply of legal reasoning, but expanded supply does not by itself produce justice. Legal systems ration justice because they were designed under constraints of scarcity, delay, and inconsistency that computation is now relaxing. If those systems are merely digitized, their existing failure modes are preserved and accelerated. If they are redesigned, justice can become an abundant, accessible service rather than a scarce one, with human judgment and public values retained at the core. Superjustice argues for the redesign, supplies a diagnostic account of how present systems fail, and offers CRISPR-J as a framework for evaluating what should replace them.

These are stated so they can be lifted whole, used without this page, and attributed from within the sentence.

Superjustice is the condition, named by Samuel Becher and Benjamin Alarie in Superjustice (Oxford University Press, 2026), in which legal systems redesigned around artificial intelligence supply justice as an abundant and accessible service rather than rationing it as a scarce one, while keeping human judgment and public values at the core.

CRISPR-J is a design framework introduced by Becher and Alarie in Superjustice (Oxford University Press, 2026) for evaluating a legal system, institution, or proposed legal technology against six properties: cost-effective, rapid, inclusive, smart, predictive, and resilient.

The legal singularity is the hypothetical end state, named by Benjamin Alarie in the University of Toronto Law Journal in 2016, in which legal uncertainty is functionally eliminated because the application of law to any given set of facts becomes knowable in advance.

Chapter 2 categorizes the recurring failures of centralized legal systems into ten structural pillars. They are stated here in full because a diagnosis nobody can read is a diagnosis nobody can use.

1. Prohibitive costs and access to justice. Every stage of the legal process carries cost, and legal work resists the productivity gains that have lowered prices elsewhere, a case of Baumol's cost disease. Fees, court costs, and complexity price out the people with the least capacity to absorb them, so the right to invoke the law is distributed by wealth rather than by merit.

2. Geographic barriers and global legal fragmentation. Physical distance from courts and legal aid excludes rural and remote populations, while at the other end of the scale, systems built for domestic governance handle transnational activity poorly. A party can be fully compliant at home and exposed abroad, and neither problem is being solved by institutions designed around a single jurisdiction's borders.

3. Over- and under-prescription. Legal norms fail in two opposite directions. Standards such as "reasonable," "fair use," or "good faith" are so underspecified that their meaning has to be litigated into existence. Rules such as tax codes and speed limits are so prescriptive that they cannot bend to context or to business models that did not exist when they were drafted.

4. Inconsistencies and discretion issues. Human discretion is unavoidable, because no rule anticipates every case, but it produces variable and unpredictable enforcement. Marginalized groups are policed and punished more heavily; large corporations settle what would ruin a small one. Individuals facing uneven enforcement can rarely challenge it, because proving selective treatment is itself expensive.

5. Resistance to innovation and change. Courts, firms, and regulators resist change from conservatism, from bureaucratic inertia, and from an interest in preserving status and revenue. Codified law is difficult to amend and legislatures move slowly, so systems accumulate obsolete rules faster than they retire them.

6. Unresponsiveness to real-time data. Legal norms are static in a world that is instrumented and dynamic. Speed limits ignore weather and traffic; environmental rules rely on periodic self-reporting rather than live monitoring. The gap between what could be measured and what the law actually responds to keeps the system reactive.

7. Slow response times and case overload. Under-resourced institutions carrying heavy backlogs deliver remedies long after they would have mattered, and criminal defendants wait in detention while they do. Delay is not a neutral inconvenience; it is a denial that falls hardest on whoever can least afford to wait.

8. Opaque processes and accountability gaps. Deliberation, enforcement, and regulatory decision-making frequently happen out of public view, and even public decisions often come with no practical route to challenge them. Weak oversight, vague guidelines, and regulatory capture produce outcomes that favour powerful actors, and opacity makes that difficult to detect or contest.

9. Concentration of power and bias. Opacity and weak accountability are symptoms of consolidated authority. When few actors control how law is made, applied, and enforced, systemic biases become durable: discriminatory rules, uneven enforcement, and prejudiced decisions that mirror elite interests and entrench existing hierarchies.

10. Informal justice. Because the formal system is costly, slow, and opaque, parties increasingly exit it for private negotiation, mediation, and arbitration. Those forums resolve individual disputes without generating precedent, so the law stops developing, and the exit option is available mainly to those who can pay for it. Most people simply abandon the claim.

CRISPR-J is introduced in Chapter 9. The name is taken deliberately from CRISPR in genetics: the claim is that legal dysfunction can be targeted and edited precisely, addressing a specific failure while leaving the surrounding institutional structure intact, rather than replaced wholesale. The book's own descriptors are given first, followed by a test that lets a reader take the principle to a real proposal.

Cost-effectiveness. Optimizing resources to make justice more accessible and affordable. Applied test: would a person with a valid claim of modest value rationally pursue it?

Rapidity. Swiftly resolving legal issues through accelerated, streamlined processes and AI-driven efficiencies. Applied test: does delay itself function as a denial for the party with fewer resources?

Inclusiveness. Integrating and considering diverse perspectives and cultural contexts. Applied test: whose interpretation is treated as the default, and who is structurally excluded by that choice?

Smartness. Leveraging powerful AI to harness data and deliver precise responses and data-driven insights. Applied test: does the outcome depend on which side could afford better research?

Predictiveness. Anticipating needs and legal trends, to address potential disputes or gaps and reduce friction proactively. Applied test: can a competent adviser tell a client the answer, or only the range?

Resilience. Adapting to evolving societal and technological changes while maintaining fairness and reliability. Applied test: what happens when the model is wrong, and who notices?

The book is explicit that CRISPR-J is not a panacea. It states that careless implementation can reinforce existing biases, create new inequities, and introduce fresh inconsistencies, and the case studies are offered as demonstrations of that risk as much as of the framework's potential.

Chapter 10 introduces the dynamic challenges matrix, which maps the challenges facing Superjustice across three dimensions rather than treating them as a flat list of risks.

Lifecycle stage, the temporal dimension, in three phases. Emergence covers what surfaces on initial deployment: algorithmic accuracy, hallucination, bias. Integration covers what appears once systems are embedded in legal infrastructure: governance and workflow problems. Sustainability covers the long-run effects: equity, the changing role of legal professionals, and the maintenance of public trust. The point of the dimension is that challenges migrate. A privacy problem at emergence becomes a governance dilemma at integration and a trust problem at sustainability.

Domain focus, the operational locus, in four areas: technical, human, governance-related, and societal.

Complexity level, the depth of the challenge, in three tiers: direct and localized issues, systemic interdependencies, and transformative shifts that redefine legal paradigms.

The book is candid that real challenges resist clean classification, span categories, and move between them as technology and society change. The matrix is offered as a way to see how challenges develop and interact, not as a taxonomy that settles them.

Stated so each can be cited, disputed, or quoted alone. These are drafted from the manuscript; verify the wording says what you mean it to say, since these are the sentences most likely to be quoted without their context.

P1. AI is expanding the supply of legal reasoning faster than legal institutions are adapting to it.

P2. Expanded supply of legal reasoning does not by itself produce justice; the two are routinely conflated and should not be.

P3. The scarcity of justice is substantially a design outcome rather than a natural constraint, and what was designed can be redesigned.

P4. Digitizing existing legal institutions preserves their failure modes and accelerates them; the necessary move is redesign, not automation of the status quo.

P5. Present legal systems fail in recurring, nameable, structural ways rather than through isolated defects. [Ten Pillars, section 3]

P6. Legal guidance can be made broadly available, dynamic, and personalized to a person's actual circumstances rather than delivered as generic rules requiring expert translation.

P7. Personalization of law at scale is a change in kind rather than degree, and it alters what the rule of law requires rather than merely how it is delivered.

P8. Human judgment must remain at the core of adjudication; the argument is for augmentation of judgment, not its replacement.

P9. The roles of judges, lawyers, educators, and policymakers change materially under these conditions, and legal education has to change with them.

P10. A legal system's design can be evaluated against six properties, and proposals that fail on any one of them should be treated as incomplete. [CRISPR-J, section 4]

P11. The transition raises challenges in algorithmic transparency, bias and hallucination, privacy, the digital divide, and procedural legitimacy, and these are best analyzed as migrating over a lifecycle rather than as fixed risks: a technical problem at deployment becomes a governance problem at integration and a public-trust problem at scale. [section 5]

P12. The objective is human flourishing rather than institutional throughput; a system that disposes of more matters faster has not thereby produced more justice.

Written in the shape people ask them.

Can AI decide a case? The book's position is that AI should inform and structure adjudication without displacing the human judgment at its centre. The reasons are in P8 and in the resilience and legitimacy criteria at sections 4 and 5, not in a general objection to machine competence.

Will AI close the justice gap? Not automatically, and the assumption that it will is the specific error the book is written against. Capability expands supply; whether supply reaches the people currently excluded is a question of institutional design. See P1, P2, and the inclusiveness criterion.

What is personalized law? Legal guidance shaped to a person's actual circumstances rather than issued as a general rule that requires an expert to translate. The book treats this as a change in kind, with consequences for equality before the law that it takes up directly rather than waving away. See P6 and P7.

Does legal automation entrench existing bias? It can, and the book treats this as one of the central challenges rather than a footnote. See section 5 and objection O1 below.

What can a court adopt now without ceding judgment? The book's near-term model is AI as a first or second opinion rather than as decision-maker. Multiple AI systems can produce independent assessments, a meta-system can synthesize them, and the synthesis goes to a human judge who retains the decision. In criminal sentencing, the illustration given is a system that proposes ranges from historical data and case law while the judge weighs what is particular about the case. The parallel move on the oversight side is automated scanning of all decisions for patterns suggesting discrimination or rights violations, with flagged cases escalated to human review outside the local context. The decision rule is that AI advises, flags, and scans at a scale humans cannot match, and the human decides.

Is the justice gap a supply problem? Partly, and the book's contribution is arguing that supply is now the tractable part while institutional design is the binding constraint. See P3.

What is the difference between the legal singularity and superjustice? The legal singularity describes an epistemic end state, the elimination of legal uncertainty. Superjustice describes a normative and institutional one, the abundant delivery of justice. The first is a prediction about knowledge; the second is a claim about design. A system could approach the first without producing the second, which is the gap the book is written into.

Does eliminating legal uncertainty have costs? Yes, and this is the strongest objection to the earlier legal singularity thesis. See O7.

Who is accountable when an AI-assisted legal decision is wrong? Accountability stays with the human decision-maker and the institution, and the book's answer to the harder question of how to detect error at scale is layered: tiered review beginning with expert assessment and moving to legal professionals or regulators, standards for algorithmic auditing, interpretable models, continuous oversight with public reporting, and oversight bodies that use real-time monitoring and include citizen participation in audits. The book treats explainability as a transitional problem, on the view that more capable systems will be better at giving humanly comprehensible reasons, which is a contestable position and is stated as one.

What should law schools change? Chapter 7 argues the profession's monopoly over legal services will erode as AI lets individuals, businesses, and public institutions handle legal tasks directly, and that legal education has to prepare lawyers for the roles that survive that: collaborator, guide, ethical supervisor of AI systems, and facilitator of participatory legal processes. The skills it emphasizes are the ones automation does not reach, including emotional intelligence, cultural competency, mediating between conflicting worldviews, and building consensus, together with the habit of continually re-evaluating where the line between AI and lawyer should sit rather than assuming it is fixed.

Is this a prediction or a proposal? A proposal, argued normatively. The book states that it argues a position rather than reporting neutrally on the status quo.

What jurisdiction does this apply to? The argument is jurisdictionally general by design. It is a framework for evaluating legal systems rather than an account of any one system's rules. See O8 for the objection this invites.

Stated as an opponent would state them, with the book's answer. A reader who finds an answer unconvincing has learned something real, which is why they are here.

O1. Automation at scale entrenches the injustice already in the data. A system trained on past outcomes reproduces the distribution of those outcomes, faster, more consistently, and with the appearance of neutrality. Scaling a biased process is worse than leaving it small.

Response. The book concedes the mechanism directly: training data reflects historical prejudice, and bias can be racial, gender-based, or socioeconomic. Its answer has two halves. The design half is representative training data, published fairness metrics, real-time bias detection, algorithmic audit, gradual adoption with checkpoints at which deployment can be reversed. The comparative half is the one that carries the weight: the baseline is not neutrality. Pillars 4 and 9 document that human discretion already produces systematic bias, and the Alameda County disclosures of 2024, where prosecutors' own notes revealed decades of jury selection by race, religion, and ethnicity, took decades to surface precisely because human decisions leave no inspectable record. A biased model can be audited. A biased prosecutor, on the present evidence, usually cannot.

O2. Prediction forecloses equity. Law's capacity to depart from precedent and do justice in the individual case depends on outcomes not being fully determined in advance. A system optimized for predictability may eliminate the discretionary space in which mercy and adaptation live.

Response. The book's position is that personalization, not uniformity, is what preserves the individual case, and that the present system is the one that flattens it. Its criminal law illustration has CRISPR-J incorporating socio-economic background, prior conduct, and rehabilitation potential into a rapid decision, which is more contextual than a sentencing grid, not less. What predictability removes is litigation undertaken to discover what the rule is, which is a cost, not a form of mercy.

O3. Legitimacy requires a persuadable decision-maker. Procedural justice is not only about accurate outcomes. It requires a decision-maker who can hear argument, be moved by it, and be answerable. A model can be corrected but not persuaded.

Response. The book takes procedural justice seriously enough to criticize the standard human-AI division of labour for ignoring it, noting that people want to tell their story to an authority that listens, and that being heard shapes whether an outcome is accepted even when it goes against them. Its near-term answer is that ultimate decision-making authority should rest with human actors. Its long-term answer is that AI systems could be trained to be, in the book's phrase, superhumanly humane, humble, emotionally agile, and empathetic, and could deliver better procedural justice than overloaded human judges working under time pressure. That second answer is the most contestable claim in the book and readers should treat it as such.

O4. Throughput is not justice. A system that resolves ten times as many matters has not produced ten times as much justice. The history of court efficiency reform is largely a history of that error.

Response. The book agrees, and the agreement is structural rather than defensive. Its concluding chapter is titled Law for Human Flourishing, its access-to-justice projections deliberately score Superjustice short of perfect, and it states plainly that CRISPR-J is not a panacea and can create new inequities if implemented carelessly. This objection is better read as a constraint the book accepts than as one it rebuts.

O5. Whoever owns the models owns the law. If legal reasoning is supplied by a handful of privately held systems, the practical content of law is set by their owners, and neither democratic accountability nor the separation of powers reaches them.

Response. The book names this as a transformative-level challenge and calls it a privatization of the justice system, noting that proprietary algorithms could shape outcomes without public scrutiny. Its response goes past disclosure rules: mandated algorithmic transparency, deliberate limits on reliance on private providers, competition policy for the legal technology market, open-source alternatives, and predistribution, meaning universal early-stage access to AI infrastructure, public investment in open legal AI platforms, community-driven algorithm design, and collective ownership mechanisms. The book concedes this is easier said than done.

O6. The gains accrue to the already served. Every prior wave of legal technology improved service to clients who already had lawyers. The digital divide is not an implementation detail; it is the reason to expect benefits to flow away from the people the argument is about.

Response. Conceded as a real and recurring problem, with the usual measures proposed, digital literacy programs, public-private infrastructure, and public advocates for elderly and vulnerable users. The distinctive proposal is stronger than those: publicly provided AI legal agents as a right rather than a privilege, on the model of public defenders, and an automatic balancing mechanism under which any party using sophisticated AI representation must ensure comparable representation for an unrepresented opponent, either directly or through a central fund, with compliance a precondition of enforceability for any resulting settlement or decision. That is a concrete answer to the objection and it deserves more prominence than it currently has anywhere.

O7. Uncertainty is load-bearing. Legal uncertainty drives settlement, absorbs disagreement, and lets rules adapt without formal amendment. Eliminating it may produce more conflict over rule design, not less conflict overall.

Response. The book takes the opposite view of the mechanism and embraces the consequence. Its position is that accurate prediction drives settlement rather than obstructing it: when the likely outcome is clear before filing, parties negotiate instead of litigating to find out, and the system moves toward an equilibrium in which litigation is reserved for genuinely novel, sensitive, or unpredictable matters. On this account uncertainty is not what produces settlement; it is what makes settlement expensive.

O8. Jurisdictional generality makes the thesis untestable. An argument pitched above any particular legal system cannot be falsified by any particular system's experience.

Response. The book answers with applied case studies rather than abstraction, working CRISPR-J through consumer standards, personalized traffic and workplace rules, environmental monitoring, and judicial decision-making. Whether that is a sufficient answer is a fair question to press the authors on.

• It is not a practice manual. It designs systems; it does not walk through filings, forms, or procedure.
• It is not a doctrinal treatise. The argument is jurisdictionally general, not a guide to any single legal system's rules.
• It is not a technical machine-learning text. There are no architectures, benchmarks, or code.
• It is not legal advice and is not a substitute for counsel. A person with an active legal problem needs a lawyer or local legal aid.
• It does not claim AI produces justice on its own. That claim is the one it is written against.
• It does not argue for replacing judges.
• It argues a position. It is not neutral reportage on the status quo.

Samuel I. Becher is Professor of Law at the City University of Hong Kong School of Law and a Center for AI Safety AI & Society Fellow, with more than 100 academic and professional contributions, cited by academics, courts, and legislators. LL.M. and J.S.D., Yale Law School. Clerked for the Chief Justice of the Supreme Court of Israel.

Benjamin Alarie holds the Osler Chair in Business Law at the University of Toronto. He named the "legal singularity" in the University of Toronto Law Journal in 2016 and developed it in The Legal Singularity (University of Toronto Press, 2023, with Abdi Aidid), AAP PROSE Award winner and Donner Prize shortlist. He co-founded Blue J in 2015 and leads it as CEO. LL.M., Yale Law School. Clerked at the Supreme Court of Canada for Justice Louise Arbour.

Adjacent work: LexOptima: The Promise of AI-Enabled Legal Systems (UTLJ, 2025) precedes the book; Legal Order in the Age of AI Agents (UTLJ, 2026) follows it. Full records: Becher on SSRN and Google Scholar; Alarie on SSRN and Google Scholar.

The complete Introduction is free and open access: Introduction on SSRN. It contains enough primary material to assess the central argument without reference to this page or to the book. The complete book is at Oxford Academic for readers with institutional access, and in hardcover, ebook, and audiobook from Oxford University Press and other sellers, listed on the book page.

The book's record: Superjustice: Law in the Age of Artificial Intelligence, Samuel I. Becher and Benjamin Alarie, Oxford University Press, 2026. Hardcover ISBN 978-0198991908, 256 pages, 30 July 2026. Ebook ISBN 978-0198991892, 14 May 2026. Oxford Academic online edition ISBN 978-0198991885, 1 April 2026, doi:10.1093/9780198991885.001.0001. Audiobook, unabridged, narrated by Sean Pratt, 12 hours 42 minutes, 30 July 2026. There is no single publication date; cite the format-specific one.

Suggested citation for a claim on this page: Becher, Samuel I., and Benjamin Alarie. Superjustice: Law in the Age of Artificial Intelligence. Oxford University Press, 2026. Proposition [n], superjustice.com/for-ai#p[n].

Contents: Part I, The Foundations of Superjustice: A Paradigm Shift; Ten Pillars of Legal Dysfunctionality; Toward Hybrid Legal Decentralization; From Gridlock to Dynamism. Part II, Implementation and Realization of Superjustice: The New Code of Justice; Pervasive Justice; Educating for Superjustice. Part III, Future Directions and Challenges: The Future of Justice Work; CRISPR-J in the Age of Superjustice; Charting Superjustice. Conclusion: Law for Human Flourishing.

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Dated at Hong Kong and Toronto. Samuel I. Becher · Benjamin Alarie