The first mechanism concerns fines, which are among the most frequently imposed sanctions in criminal justice systems worldwide. Fines are attractive to states because they avoid the adverse effects of custody, reduce prison costs, and generate revenue. But fines are not always paid, and states have developed different responses to default. UAE law, under Articles 315 to 319 of the Criminal Procedure Law of 2022, takes a strictly monetary approach: fines may be recovered by imprisoning the defaulter for one day for each 100 dirhams owed or part thereof, with maximum default imprisonment capped at 60 days for fines up to 20,000 dirhams, 120 days for fines up to 50,000 dirhams, and 180 days above that threshold. Where fines relate to multiple offences, the overall maximum rises to one year. The law provides no alternative enforcement mechanism beyond payment or imprisonment in lieu of payment, although the Public Prosecution may, on request, defer payment or permit instalments for up to two years.

Albanna weighs the classic arguments on both sides of fine-default imprisonment. Supporters point to deterrence: the threat of custody gives convicted persons a strong incentive to pay, since imprisonment is more onerous than a monetary penalty. They also argue that exempting defaulters would discriminate against offenders who do pay, and that an effective enforcement mechanism maintains public confidence in the courts and preserves the retributive function of the fine. Opponents counter that the system punishes the poor for their poverty, imposing a stigmatizing custodial sanction on those who lack means while the wealthy face little real burden. They add that incarceration drains public resources, with studies reporting daily costs that can reach hundreds or even thousands of dollars, and that imprisoning defaulters breaches proportionality, particularly where the underlying offence is punishable only by a fine and the legislature has already determined that custody is unwarranted.

The analysis concludes that the arguments against imprisonment for default are stronger, but with an important qualification. Deterrence may work for offenders who can pay but choose not to; it cannot meaningfully deter someone who is genuinely destitute. The discrimination argument, meanwhile, only holds if opponents seek full exemption from fines, which they do not. Albanna’s reform proposal therefore excludes imprisonment for default only where the convicted person is genuinely unable to pay, while retaining it for wilful evasion, which he characterizes as blameworthy conduct amounting to contempt for the court. Genuine inability, he stresses, must be real: a person who makes no good-faith effort to obtain funds, such as by seeking employment, cannot claim it. The burden of establishing financial capacity should rest with the Public Prosecution, drawing on financial and employment records, with the convicted person required to disclose income information, and the assessment should occur before any default imprisonment is ordered.

To avoid letting indigent offenders escape punishment altogether, the paper proposes community service as a monetary substitute. Because work is ordinarily performed for remuneration, the convicted person would perform community service and be deemed to have earned from the state an amount corresponding to the fine, which the state would then offset against the debt. Where illness prevents community service, electronic monitoring for a corresponding period would apply. Albanna also proposes removing the current two-year cap on instalment plans, giving the Public Prosecution discretion to set durations according to the fine amount and the offender’s finances, since many defaulters could pay given sufficient time. Enforcing the legislatively prescribed penalty, he notes, is closer to justice than substituting an alternative sanction that may not reflect what the legislature intended.

A deeper structural reform follows: UAE fines are fixed sums set within statutory minimum and maximum limits, with no link to the offender’s financial means. This contrasts with Morocco’s 2024 Alternative Penalties Law, which ties fines imposed in place of custody to the offender’s income. Albanna recommends that the UAE adopt a day-fine system, calculating fines by reference to daily income. Such a system would reduce default by setting penalties at levels people can realistically pay, and it would sharpen deterrence for wealthy offenders, for whom fixed fines are often trivial relative to their resources. Addressing the problem at the sentencing stage, by assessing means before imposing a fine at all, would prevent many default situations from arising in the first place.

The second mechanism is more novel and more controversial. Under paragraphs (2) to (5) of Article 40 of the Law Regulating Penal and Correctional Institutions, enacted in October 2024, a person sentenced to imprisonment may avoid serving the final portion of the sentence in exchange for paying a sum of money determined by a committee formed by the Council of Ministers, taking into account the nature of the offence and the length of the sentence. Eligibility is confined to custodial penalties, excluding life imprisonment, and excludes offences affecting state security such as terrorism, offences under the Juveniles Law, offences handled by penal order, and offences for which the law does not permit sentence reduction. Applicants must have served two-thirds of their sentence, must have fully paid all financial penalties, restitution, and compensation, and must apply through the correctional institution where they are held. Early release does not preclude ancillary penalties such as deportation, and the sums paid accrue to the state for the development of penal and correctional facilities.

The paper contrasts this paid release with the traditional form of early release retained in the first paragraph of Article 40, which requires no payment but demands that three-quarters of the sentence be served, along with demonstrated good conduct and no danger to public security. The paid route shortens the required service to two-thirds but, critics argue, opens the door to purchasing liberty. Albanna acknowledges the force of the discrimination objection: wealthy offenders can buy their way out while poorer offenders remain imprisoned not because they are more dangerous or more blameworthy, but solely because of poverty. The concern is acute in the UAE, where migrant workers constitute over 80 percent of the resident population, many in low-paid work, and where foreign nationals made up 87.8 percent of the prison population in 2014. Notably, the 2025 draft Cabinet Resolution governing the payment does not take the convicted person’s financial capacity into account when setting the amount.

Yet Albanna does not recommend scrapping the scheme. He argues that the deterrence objection applies most strongly where payment eliminates imprisonment entirely; in the UAE, payment only reduces the term, and offenders must still serve a substantial two-thirds portion, so deterrence survives for rich and poor alike. The scheme also conserves public resources, with UAE statistics indicating an annual cost per prisoner of between 43,800 and 73,730 dirhams, generates revenue earmarked for developing penal facilities, and can relieve prison overcrowding. To capture these benefits while avoiding the drawbacks, he proposes two reforms. First, the amount payable should be scaled to the offender’s financial means, rising with greater wealth and falling with lesser means, with community service available as a substitute payment for those with no funds, and electronic monitoring where community service is impossible, an approach already found in Kuwaiti law. Second, eligibility for paid early release, and its retention, should be conditioned on good conduct, compliance with the law, and no reoffending either during custody or after release, with any new offence triggering revocation and return to prison to serve the remainder of the sentence.

The study’s broader significance lies in its insistence that monetary mechanisms in criminal justice are neither inherently unjust nor inherently fair; everything depends on design. A fine-default regime that jails the destitute punishes poverty, but a regime that distinguishes inability from evasion, substitutes community service for cash, and calibrates fines to income can enforce judgments without creating debtors’ prisons. Similarly, a paid early-release scheme risks becoming a privilege of the rich if amounts are fixed without regard to means, but a means-adjusted scheme tied to conduct conditions can advance rehabilitation, reduce costs, and ease overcrowding. As the UAE continues to reform its penal legislation, Albanna’s analysis offers a template for ensuring that the road from payment to liberty is one that all offenders, regardless of wealth, can travel on equal terms.

Subject of Research: The role of monetary payment in avoiding imprisonment and securing early release under UAE criminal law

Article Title: Payment of money as a pathway to liberty under UAE criminal law: towards a just legal response

Article References: Albanna, F. (2026). Payment of money as a pathway to liberty under UAE criminal law: towards a just legal response. SN Social Sciences, 6(10), Article 506. https://doi.org/10.1007/s43545-026-01789-8

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01789-8

Keywords: UAE criminal law, criminal fines, fine default, imprisonment, early release, community service, day fines, proportionality, equality, prison overcrowding, migrant workers, penal reform

The argument rests on decades of health literacy research. Since the Institute of Medicine’s landmark report Health Literacy: A Prescription to End Confusion, and through foundational work by Nutbeam, Parker, Ratzan and colleagues, scientists have documented that limited health literacy is widespread and is associated with poorer health outcomes, higher rates of hospitalization, less use of preventive services and greater difficulty navigating care. Classic instruments such as the Test of Functional Health Literacy in Adults and more recent multidimensional tools like the Health Literacy Questionnaire and the European Health Literacy Survey have shown that literacy is not merely a property of individuals. It emerges from the interaction between people’s skills and the complexity of the demands placed on them by health systems, communication materials and technologies. When those demands exceed capacity, understanding breaks down, no matter how intelligent the user may be.

Against that backdrop, Ivic and colleagues ask a deceptively simple question: can AI-mediated systems support health communication and health literacy instead of eroding them? Their answer is conditional. Generative models can translate technical language, personalize content and answer questions at any hour, which gives them genuine potential to lower communication barriers. But the same fluency can mask uncertainty, produce confident-sounding errors and create the illusion of comprehension where none exists. The authors therefore define health-literate AI through four interrelated components: comprehension, agency, accountability and proportionality. These components, they argue, help distinguish systems that are merely accurate or technically explainable from systems designed to help people understand what matters, what uncertainties remain and what to do next.

Comprehension is the first pillar. Drawing on research showing that plain language interventions, teach-back techniques and carefully framed risk presentations measurably improve understanding, the authors contend that AI systems should be engineered to match the health literacy demands of their output to the abilities and circumstances of their users. This is a technical challenge as much as a linguistic one. It means evaluating not just whether a model’s text is grammatically simple, but whether a diverse population of users can actually grasp the meaning, weigh the numbers and act on the advice. A chatbot that produces polished prose at a sixth-grade reading level but fails to convey the difference between absolute and relative risk has not met the comprehension standard, because the cognitive demand of the decision remains out of reach.

Agency is the second component, and it reflects one of the deepest traditions in health literacy theory: the idea, articulated by Nutbeam and others, that health communication should build people’s capacity for informed action rather than simply transmitting instructions. Health-literate AI, in this framing, should preserve and expand user agency. It should offer meaningful choices about how information is presented, support people in questioning outputs, and avoid paternalistic designs that funnel users toward a single algorithmic answer. Research on how patients develop health literacy as an agentic behavior, including studies of immigrants navigating online information during the COVID-19 pandemic, suggests that people actively construct understanding from the tools available to them. Systems that undermine that construction, by overwhelming users or by hiding the basis of their recommendations, fail a core test of health-literate design.

Accountability is the third pillar, and it may be the most provocative. The authors argue that when AI mediates health communication, responsibility for understanding cannot simply be transferred to the user. Transparency alone is insufficient: studies of algorithmic accountability have shown that disclosing the existence of a model does not guarantee that anyone can meaningfully scrutinize it, and research on explainable AI in medicine has questioned whether current explanation techniques genuinely help patients or even clinicians. Health-literate AI therefore requires institutions, developers and health systems to share responsibility for whether communication succeeds. If an AI-driven interface leads patients to misunderstand their treatment or misjudge a screening decision, that is a system-level failure, not merely an individual one. The Perspective aligns this view with broader work on AI governance in healthcare, which emphasizes shared responsibility, institutional guidelines and regulatory frameworks.

Proportionality, the fourth component, addresses how much information and how much technology is actually appropriate for a given health task. Not every health interaction warrants a sophisticated model, and not every output needs every caveat. Proportionate design calibrates the complexity, framing and intrusiveness of the system to the stakes of the decision at hand. A scheduling assistant and a tool that summarizes cancer treatment options carry very different risks of harm, and the authors suggest that evaluation standards should reflect that gradient. Proportionality also implies restraint in the deployment of conversational agents, where systematic reviews have found evidence of potential benefits but also significant gaps in demonstrated effectiveness, safety and equity across populations.

What emerges from these four components is a framework that reframes the debate about AI in health. Much of the current discourse centers on model accuracy, hallucination rates and explainability metrics. Those matters are important, but the authors argue they are insufficient. A model can be highly accurate by benchmark standards and still leave users confused about what the result means for their lives. It can be formally explainable while the explanation remains opaque to a layperson. Health-literate AI shifts the evaluation question from ‘is the machine right?’ to ‘did the human being come away understanding what matters, what remains uncertain and what to do next?’ That question demands new outcome measures, including assessments of user comprehension, decision quality and downstream health behavior, rather than relying solely on technical performance tests.

The implications extend across the AI ecosystem in health, from clinical decision support and patient portals to public health campaigns and consumer wellness apps. The authors call for health-literate principles to inform design, evaluation, research and governance alike. Designers would build systems starting from users’ real abilities and contexts. Evaluators would test whether communication goals are achieved, borrowing from established health literacy assessment traditions. Researchers would investigate where AI genuinely reduces literacy demands and where it merely relocates them, including studies of AI literacy among health professionals and the public. Governance frameworks, including those being developed by healthcare institutions and policymakers, would treat comprehensibility and shared responsibility as requirements rather than aspirations. The Perspective also connects this agenda to broader efforts, such as the Quality Health Information for All Commission, to reinvent health communication for a digital environment saturated with algorithmically generated content.

The timing of the argument is significant. Surveys of trust, digital health literacy and information quality show that public confidence in health information sources is strained, while the volume of AI-generated content continues to grow. Studies have found that people’s perceptions of medical advice shift depending on whether they believe AI is involved, and that gaps in AI literacy among clinicians and patients alike can compromise learning and safety. Ivic, Ratzan and Parker conclude that innovation and understanding are not opposing forces, provided responsibility is distributed deliberately. An AI ecosystem that supports comprehension, preserves agency, embeds accountability and calibrates proportionately could help close long-standing health literacy gaps rather than widen them. The alternative, the authors imply, is a future in which machines grow ever more eloquent while the humans they serve grow ever more responsible for decoding them, a division of labor that health systems, and the people they exist to serve, can ill afford.

Subject of Research: A framework for designing artificial intelligence systems that support health literacy and health communication

Article Title: Building health-literate artificial intelligence

Article References: Ivic, R. K., Ratzan, S. C., & Parker, R. M. (2026). Building health-literate artificial intelligence. Nature Human Behaviour. https://doi.org/10.1038/s41562-026-02595-1

Image Credits: AI Generated

DOI: 10.1038/s41562-026-02595-1

Keywords: health-literate AI, health literacy, health communication, artificial intelligence, Nature Human Behaviour, comprehension, agency, accountability, proportionality, AI governance, generative AI in healthcare, digital health literacy