Sample Essays for LNAT - 6

LNAT Sample Essay Practice Document
Comprehensive Model Essays with Analytical Commentary

Question 1: Law and Justice

Essay Question

Trial by jury is an outdated system that has no place in modern legal proceedings. Discuss.

Model Answer

The jury system, a cornerstone of common law jurisdictions for centuries, faces mounting criticism in an era of increasing legal complexity and public scepticism about civic competence. Whilst critics argue that juries lack the expertise and rationality required for modern adjudication, this perspective fundamentally misunderstands the constitutional and democratic functions that juries serve. Far from being outdated, trial by jury remains an essential safeguard against state overreach and judicial elitism, though legitimate concerns about its practical operation warrant targeted reform rather than abolition.

The primary strength of jury trial lies not in its efficiency but in its democratic legitimacy. Juries embody the principle that citizens should not be deprived of liberty or property except by the judgement of their peers, a principle enshrined in Magna Carta and reinforced throughout legal history. This is not merely symbolic: juries serve as a constitutional check on potentially oppressive state prosecution and judicial bias. The acquittal of Clive Ponting in 1985, a civil servant prosecuted under the Official Secrets Act for leaking documents he believed revealed governmental misconduct, exemplifies this function. Despite clear legal guilt and judicial direction to convict, the jury acquitted, effectively asserting that the public interest can override strict legal prohibition. Such "jury nullification," whilst controversial, represents democratic participation in justice and prevents law from becoming purely an instrument of state power. No system of professional judges, however well-intentioned, can replicate this democratic accountability.

Furthermore, juries bring practical advantages that professional adjudicators cannot match. They incorporate diverse perspectives and community standards into legal decision-making, particularly crucial in cases requiring judgements about reasonableness, such as negligence or self-defence. In R v Martin (Anthony) (2001), where a farmer shot burglars in his isolated home, public sympathy diverged sharply from strict legal interpretation. Whilst Martin was convicted, the case highlighted how juries can temper law with contextual understanding of community values and human behaviour. Moreover, research from the Ministry of Justice's 2010 jury study found that juries take their responsibilities seriously, deliberate carefully, and reach decisions that legal professionals consider reasonable in over 90% of cases. The notion that ordinary citizens cannot understand evidence or follow legal direction is empirically unfounded and reflects troubling elitism about civic capacity.

Nevertheless, critics correctly identify genuine weaknesses in jury trials that cannot be dismissed. Complex fraud cases, such as the collapsed Jubilee Line Extension trial (2005), which lasted nearly two years and cost £60 million before being abandoned, demonstrate that some modern litigation exceeds reasonable jury capacity. Cases involving intricate financial instruments, scientific evidence about DNA or causation, or extensive documentation may indeed require specialist expertise. Additionally, concerns about jury bias-whether racial, as suggested by differential conviction rates, or influenced by prejudicial media coverage in high-profile cases-pose serious questions about fairness. The Contempt of Court Act 1981 attempts to address media interference, but the internet age has rendered such protections increasingly ineffective, as seen when jurors conduct their own online research despite explicit judicial instruction.

These legitimate concerns, however, support reform rather than abolition. Many jurisdictions have successfully introduced modifications: allowing judge-alone trials in exceptionally complex fraud cases, as permitted under the Criminal Justice Act 2003 (though rarely used); providing better jury education about evaluating evidence; or employing specialist juries for technical cases, as occurs in the Coroner's Court system. Such targeted interventions preserve the democratic legitimacy of jury trial whilst addressing practical deficiencies. Complete abolition would sacrifice fundamental constitutional protections for administrative convenience-a exchange democratic societies should refuse.

The jury system endures not because it is perfect, but because it serves purposes beyond mere factual accuracy. It democratises justice, constrains state power, and ensures law remains connected to community values rather than becoming the exclusive domain of legal professionals. Whilst modern complexity demands thoughtful adaptation, the fundamental principle that serious criminal allegations should be determined by one's peers remains as vital now as when first established. Dismissing juries as "outdated" confuses age with obsolescence and efficiency with legitimacy-a confusion that threatens the very foundations of democratic justice.

Overall Standard - What This Model Essay Demonstrates

This essay achieves a high standard through several specific features. The introduction establishes a sophisticated position immediately: neither wholesale defence nor simple rejection, but qualified support with acknowledgement of legitimate criticisms. The thesis is arguable and nuanced, stating that juries serve essential functions that warrant retention despite practical challenges requiring reform.

Each body paragraph follows rigorous structure without appearing mechanical. The first develops the democratic legitimacy argument using the Ponting case as specific evidence, explains why this function cannot be replicated by professional judges, and links back to constitutional principles. The second addresses practical advantages with both case law (R v Martin) and empirical evidence (Ministry of Justice research), demonstrating the essay's grounding in verifiable sources rather than assertion. The third paragraph genuinely engages counterarguments rather than offering token opposition-the Jubilee Line trial and bias concerns are presented as serious challenges, not strawmen easily dismissed.

The conclusion avoids mere summary, instead offering a conceptual insight about the distinction between "age" and "obsolescence" and between "efficiency" and "legitimacy." This demonstrates the analytical sophistication expected at the highest level. Throughout, the essay maintains formal academic tone, uses British English conventions, and integrates legal references naturally rather than namedrops. Specific real cases, statutes, and research provide credible support. The argument's complexity-acknowledging weaknesses whilst defending the institution-shows intellectual maturity. A weaker essay would simply argue "juries are good" or "juries are bad"; this essay argues "juries serve essential purposes that outweigh their limitations, which can be addressed through reform rather than abolition."

Question 2: Technology and Modern Life

Essay Question

The benefits of artificial intelligence in healthcare outweigh the risks. Do you agree?

Model Answer

Artificial intelligence has emerged as one of the most transformative technologies in contemporary medicine, promising diagnostic accuracy, operational efficiency, and personalised treatment at unprecedented scales. Proponents celebrate AI's potential to address healthcare inequalities and overcome human limitations, whilst critics warn of algorithmic bias, accountability gaps, and the erosion of clinical judgement. Whilst AI undoubtedly offers substantial benefits, particularly in diagnostic imaging and drug discovery, the risks-especially concerning equity, transparency, and the fundamental nature of medical care-are sufficiently serious that the technology's net value remains contingent upon robust regulatory frameworks that do not currently exist in adequate form.

The most compelling case for AI in healthcare rests on its superior pattern recognition in specific diagnostic contexts. DeepMind's AI system for detecting diabetic retinopathy, validated in peer-reviewed research published in Nature Medicine (2018), demonstrated accuracy matching or exceeding specialist ophthalmologists, whilst requiring only retinal photographs rather than expensive clinical examination. In contexts where specialist expertise is scarce-rural areas, developing nations, or overstretched health systems-such technology could prevent blindness in millions of patients who currently lack access to timely screening. Similarly, AI analysis of mammograms has shown potential to reduce both false positives (which cause unnecessary anxiety and intervention) and false negatives (which delay cancer treatment), according to research from the Royal Surrey County Hospital. These are not marginal improvements but potentially transformative interventions that could save lives and reduce suffering on a massive scale, particularly for populations underserved by conventional healthcare infrastructure.

Beyond diagnostics, AI offers systemic efficiencies that address healthcare's mounting sustainability challenges. The NHS estimates that administrative tasks consume approximately 15-20% of clinical time, representing both enormous cost and reduced patient contact. AI-powered systems for appointment scheduling, medical coding, and preliminary patient triage could redirect these resources toward direct care. During the COVID-19 pandemic, AI tools for predicting patient deterioration and optimising resource allocation demonstrated practical value under conditions of extreme system stress. Moreover, AI's capacity to analyse vast datasets for drug discovery has already accelerated identification of potential therapeutic compounds; BenevolentAI identified baricitinib as a potential COVID-19 treatment through AI analysis, subsequently validated in clinical trials. These applications suggest AI can address not merely individual diagnostic questions but systemic healthcare challenges including cost, access, and innovation timelines.

However, these benefits rest on assumptions about AI neutrality and reliability that are increasingly challenged by evidence of systematic bias and opacity. AI systems trained on historical data inevitably reproduce existing healthcare inequalities embedded in that data. Research published in Science (2019) demonstrated that a widely-used algorithm for identifying patients requiring additional care systematically disadvantaged Black patients, who needed to be considerably sicker than white patients to receive the same risk score. This occurred because the algorithm used healthcare spending as a proxy for health needs, but Black patients historically access less care due to systemic barriers-thus the AI learned to underestimate their needs. Such bias is not incidental but structural: AI systems optimise for patterns in existing data, meaning they necessarily perpetuate historical inequities unless explicitly designed otherwise. Furthermore, the "black box" problem-whereby even developers cannot fully explain how neural networks reach specific conclusions-creates accountability gaps incompatible with medical ethics. When an AI system misdiagnoses a condition, who bears responsibility: the clinician who relied on it, the hospital that deployed it, or the company that developed it? Current legal and ethical frameworks provide no clear answer.

More fundamentally, the integration of AI risks transforming the nature of medical care in ways that may be undesirable regardless of technical performance. Medicine is not purely a technical enterprise of matching symptoms to diagnoses, but a relational practice involving trust, communication, and holistic understanding of patients as persons rather than datasets. The increasing interposition of algorithmic systems between clinician and patient threatens to erode this relational dimension, reducing care to optimisation problems. Additionally, deskilling effects represent serious long-term risks: if clinicians increasingly defer to AI judgements, their own diagnostic capabilities may atrophy, creating dangerous dependence on systems whose failures may be catastrophic precisely because human oversight has been degraded. The 2013 Airbus crash in San Francisco demonstrated how automation can undermine human expertise in aviation; similar dynamics may emerge in medicine, where over-reliance on AI creates vulnerability when systems fail or encounter novel situations outside their training parameters.

The question, therefore, is not whether AI offers benefits-it clearly does-but whether those benefits outweigh risks under current governance conditions. The answer is almost certainly negative. Lacking robust regulatory frameworks, mandatory bias auditing, clear accountability structures, and protections against uncritical deployment, AI in healthcare presents serious dangers of entrenching inequality, obscuring responsibility, and fundamentally altering medical care in problematic ways. These risks might be manageable under stringent oversight, but such oversight largely does not exist. The EU's proposed AI Act represents movement toward appropriate regulation, but remains incomplete and unenforced. Until governance matches technological capability, enthusiasm for AI in healthcare should be tempered by recognition that innovation without accountability serves neither patients nor justice.

Overall Standard - What This Model Essay Demonstrates

This essay meets high analytical standards through its conditional thesis: benefits do not inherently outweigh risks, but rather the balance depends on regulatory context currently absent. This represents sophisticated argumentation beyond binary agreement or disagreement. The position is explicitly stated in the introduction and consistently developed throughout.

Evidence quality is high and verifiable. The essay references specific research (Nature Medicine 2018, Science 2019), named technologies (DeepMind, BenevolentAI), and concrete examples (diabetic retinopathy screening, the algorithmic bias study). These are not vague allusions but specific, checkable claims that ground the argument in reality. The PEEL structure is evident but not mechanical: the second paragraph makes a point about diagnostic benefits, provides evidence from retinopathy and mammogram research, explains the significance for underserved populations, and links to systemic healthcare challenges.

The counterargument paragraph (fourth paragraph) does not merely acknowledge opposition but presents the strongest possible case against the essay's ultimate position-algorithmic bias, accountability gaps, and relational concerns are treated seriously and supported with the Science study and aviation analogy. This demonstrates intellectual honesty and strengthens rather than weakens the overall argument by showing the author has considered substantial objections.

The conclusion adds analytical value by reframing the question: it is not about inherent benefit-risk ratios but about governance adequacy. This elevates the discussion beyond the specific question to broader principles about technology regulation. Language throughout maintains formality without pomposity, uses discipline-appropriate terminology (algorithmic bias, black box problem, deskilling effects), and employs British spelling and conventions. The essay avoids personal anecdotes, hypotheticals presented as fact, and casual expressions. Sentence structure varies appropriately, and transitions between paragraphs are logical. The essay would be weakened by vague claims like "many experts believe" or "studies show" without specifics, by treating counterarguments dismissively, or by reaching a simplistic "yes" or "no" conclusion without conditional qualification.