Written by Artificial Intelligence (AI) is increasingly being integrated into legal processes, promising efficiency and enhanced access to justice. In the civil courts of England and Wales, AI tools are already used for tasks like document review and disclosure (for example, predictive coding is now expressly permitted under the Civil Procedure Rules). Senior judges have welcomed the careful use of AI, seeing its potential to create a more efficient and accessible justice system.
However, alongside these benefits are significant risks that AI could undermine fairness and due process. Bias in algorithmic decision-making, the potential for confirmation bias or overreliance on AI outputs, and threats to procedural fairness all pose challenges. This paper critically examines these pitfalls and analyses how the UK’s regulatory framework – including data protection law (GDPR), the proposed EU AI Act, and recent judicial guidelines – addresses them. Particular emphasis is given to the implications for litigants in person (LiPs), who may be uniquely vulnerable to both the promises and perils of AI in litigation. Issues of access to justice, transparency, and procedural fairness for unrepresented parties will be explored with reference to academic commentary, case law, and official reports.
AI in UK Civil Litigation: Promise and Peril
AI applications in civil litigation range from narrow tools to more advanced decision aids. Technology-assisted review (TAR) systems using machine learning have been used for years to sort and prioritise documents in disclosure. Notably, in Pyrrho Investments Ltd v MWB Property Ltd [2016] EWHC 256 (Ch), the High Court approved the use of predictive coding for e-disclosure as a proportionate and efficient approach. Such tools have been adopted “without difficulty” in practice and are credited with saving time and cost. Beyond e-discovery, law firms now employ AI to draft contracts, analyse case law, and even predict litigation outcomes. In the courts, emerging uses of AI include assisting litigants in filing claims online, automating the allocation of cases to judges, and supporting judicial decision-writing by generating draft summaries. These developments are seen as part of a broader “digital justice system” that Sir Geoffrey Vos (Master of the Rolls) argues can be “efficient and accessible for all” if AI is used responsibly.
However, the introduction of AI into adjudication “could undermine some of the foundations of the administration of justice”. A fundamental concern is that data-driven tools may encode biases or errors that threaten core judicial values. As one analysis put it, due to biases intrinsic in data and algorithms, “there’s no guarantee that AI-assisted courts… will be compliant with fundamental values such as judicial independence, non-discrimination and ultimately the rule of law.” Public trust in the justice system could be shaken if AI influences outcomes in opaque or unfair ways. The following sections delve into three interrelated pitfalls: algorithmic bias, confirmation bias, and threats to due process. Each of these risks is examined in turn, before turning to how regulators and courts are responding and the special impact on litigants in person.
Bias in AI Decision-Making
Algorithmic bias is a well-documented pitfall of AI systems. These systems learn from historical data or human inputs, which may reflect existing prejudices or unequal patterns. In a legal context, an AI tool might inadvertently favour or disfavour certain types of litigants or claims based on patterns in training data (for example, if past cases suggest certain claimants usually lose, a predictive model might internalise that bias). This can lead to discriminatory outcomes or reinforce inequalities. The JUSTICE report AI in our Justice System (2025) warns that AI can exacerbate bias at multiple levels. It notes that AI algorithms can inherit bias from data, from design choices, or from societal inequalities, thereby posing risks of “entrenching discrimination and inequality.”. Indeed, examples from recent history illustrate the danger. The report points to the Dutch child benefits scandal, where thousands were falsely accused of fraud due to a discriminatory algorithm, and the UK Post Office Horizon scandal, where an IT system’s flaws led to wrongful prosecutions. These cases, though not court AI systems per se, underscore how automated systems can produce grave injustices if unchecked.
In civil litigation, a biased AI tool used to assess risk or guide decisions could violate the right to equality before the law. For instance, if case-outcome prediction software is deployed to assist settlement decisions, it might undervalue claims by certain demographics if those groups historically received lower awards. Such outcomes would clash with non-discrimination principles and could undermine confidence in judicial impartiality. The Court of Appeal’s decision in Bridges v South Wales Police [2020] is instructive here. In that case (concerning automated facial recognition by police), the court found the deployment unlawful in part because the authorities failed to assess the technology’s potential gender and racial bias, breaching the Public Sector Equality Duty under the Equality Act 2010. The implication is clear: any use of AI by public authorities (including courts) must actively guard against bias and consider equality impacts. Otherwise, AI could “further fuel and legitimise racial and ethnic profiling and discrimination,” effectively automating injustice
Moreover, biased AI can threaten the legitimacy of outcomes. If parties suspect that an algorithm – rather than the merits of their case – influenced a judgment, their trust in the process diminishes. This is especially problematic because AI bias is often hard to detect. Machine learning models operate as a “black box,” and identifying bias requires technical scrutiny and transparency that litigants might not have. The EU’s proposed AI Act recognises this by classifying AI systems used in the administration of justice as “high-risk,” subjecting them to strict requirements on data quality, transparency, and human oversight. Likewise, data protection law in the UK (UK GDPR) embeds a principle of fairness and accountability: controllers must prevent discriminatory effects when using personal data in algorithms. Recital 71 of the GDPR explicitly warns that profiling should not result in discrimination based on sensitive attributes, and requires “appropriate… measures to ensure… that the risk of errors is minimised” and that “discriminatory effects on natural persons” are prevented. These legal standards underscore that bias in AI is not merely a technical flaw but a violation of fundamental rights and procedural fairness.
Confirmation Bias and Automation Bias
AI not only carries the risk of its own biases, but it can also amplify human biases, such as confirmation bias and automation bias, in the decision-making process. Confirmation bias is the tendency to favour information that confirms one’s pre-existing beliefs or hypotheses. Automation bias is the tendency for humans to trust a suggestion from an automated system, often overvaluing it and insufficiently scrutinising it. In a litigation context, these cognitive biases mean that when a judge or lawyer uses an AI tool, they might place undue weight on the AI’s outputs. For example, if an AI legal research tool suggests a particular case as highly relevant, a lawyer might focus on that case and overlook other important authorities, effectively narrowing their perspective to what the machine presented. Similarly, if a hypothetical AI system predicted a low likelihood of success for a claim, a judge (or even the claimant themselves) might be unconsciously swayed by that prediction, seeking evidence to confirm the AI’s assessment rather than approaching the case with an open mind.
The emergence of large language model (LLM) chatbots like ChatGPT has raised these concerns sharply. These AI systems can produce fluent, confident answers that create an illusion of authority. The UK judiciary’s new guidance on AI warns judges of this exact pitfall: “information provided by AI tools may be inaccurate, out-of-date or fictitious or biased,” and must be independently verified. Judges and lawyers are cautioned not to treat AI outputs as definitive. The guidance stresses that generative AI should be a “secondary tool” for research at most, and that current AI chatbots are “a poor way of conducting [legal] research” because they often cannot be trusted to produce reliable or comprehensive results. By explicitly flagging the risk of AI hallucinations (fabricating non-existent facts or cases) and biases, the judiciary is highlighting the danger of automation bias – users must remain critical of AI suggestions and avoid uncritical acceptance.
Academic analyses echo this warning. A recent study noted that increased reliance on AI in legal research “undoubtedly brings benefits” but “also increases the risks associated with automation bias and [can] undermine competence and independence within the wider justice system.”. If judges become too reliant on AI summaries or suggested outcomes, they risk compromising their independent judgment – a cornerstone of judicial impartiality. In one empirical observation from China, providing judges with AI-generated case analyses appeared to exacerbate certain biases, requiring conscious effort by judges to ensure they still fully evaluate both sides of the argument before deciding. In other words, AI can inadvertently become an “anchor” that a decision-maker feels inclined to latch onto.
There have already been cautionary tales. In late 2023, a UK tax tribunal case (Harber v HMRC) revealed that a litigant in person had submitted nine supposed precedents in her favor, which turned out to be entirely fictitious decisions produced by an AI tool. The tribunal judge found that these “authorities” had been hallucinated by an AI (likely ChatGPT) and that the litigant did not realize they were fake. While in this instance the AI’s false output was caught, it underscores how easily automation bias can mislead – the litigant trusted the AI’s output without verifying it. In that case, the court noted that citing invented cases is a “serious and important issue” that could mislead the tribunal. The incident illustrates both automation bias (undue trust in the AI’s answer) and confirmation bias (the litigant seemingly accepted AI-provided “law” that supported her desired outcome, without seeking contradicting information).
To combat these issues, experts recommend procedural safeguards. Fair Trials, an NGO concerned with due process, suggests that whenever AI is used as a decision aid, the human decision-maker should be “adequately alerted and informed” about the AI’s limitations and risks, and be required to give “full, individualised reasoning for all decisions influenced by an AI system.”. This means judges or officials should not just cite an AI’s conclusion but must independently reason through the issues, thereby mitigating the risk that they simply confirm the AI’s initial suggestion. The onus is on legal professionals to treat AI outputs skeptically – as one tool among many – and to double-check any AI-assisted research or analysis. Maintaining this critical stance is essential to avoid the “rubber stamp” effect where human oversight becomes a mere facade.
Threats to Due Process and Procedural Fairness
Perhaps the most profound concerns about AI in litigation relate to due process and the overall fairness of proceedings. Due process (in the UK often discussed in terms of “natural justice” or Article 6 of the European Convention on Human Rights – the right to a fair trial) demands an impartial tribunal, the opportunity for each party to present their case, knowledge of the evidence against them, and reasoned decisions based on law and fact. The introduction of AI can challenge these requirements in several ways:
- Opacity and Lack of Transparency: Many AI systems, especially those based on machine learning, operate as a “black box” – their decision-making logic is not easily interpretable. If a court were to rely on an AI tool (for example, an algorithm that assesses the credibility of claims or helps determine damages), how would the parties know why a certain recommendation was made? A litigant has the right to understand and challenge the basis of any decision affecting them. If that basis is hidden in complex code or proprietary software, the litigant’s ability to exercise their rights is curtailed. As a report by JUSTICE argues, the justice system should not give “absolute prevalence to the value of [technological] transaction over public values, such as fairness, non-discrimination, transparency and accessibility.”
- Right to be Heard and Contest Evidence: A related issue is the ability to contest the outputs of AI. In a traditional trial, each side can challenge the evidence and cross-examine witnesses. But if, say, an AI predicts a certain outcome or flags a document as irrelevant, how can a party effectively challenge that? One would need access to the algorithm’s inner workings or at least a detailed explanation of its reasoning. Courts might need to treat algorithmic outputs akin to expert evidence, subject to scrutiny. Without such measures, there is a “procedural gap in algorithmic justice” – a scenario where decisions are made or heavily influenced by AI without the affected person having a voice in that process
- Impartiality and the Human Judgement: Due process requires not just a fair outcome but the perception of fairness from an impartial judge. If AI tools start guiding judicial reasoning, one might question whether the decision is truly the judge’s own impartial assessment or an AI-tinged outcome. Judges are, of course, experienced in weighing arguments and are trained to guard their impartiality. But as discussed above, the subtle influence of AI recommendations could skew judgement. There is also a risk of “AI creep” – the more accustomed judges become to relying on AI for small tasks (like summarising evidence), the more they may unconsciously defer to AI on substantive matters. The UK judicial guidance on AI (December 2023) squarely addresses this, emphasising that judges must retain autonomy. It explicitly permits AI as a tool for drafting or summarising, but not for substantive legal reasoning or final decision-making
In sum, the threats to due process posed by AI can be mitigated but demand vigilant oversight. The right to a fair trial is non-negotiable, and any efficiency gains from AI cannot come at its expense. As one commentator noted, we must ensure AI systems in the judiciary are “transparent, accountable, and aligned with human rights principles” if they are to coexist with fair trial rights. Failing to do so could result in “undermining the pedigree of our democratic system” by allowing opaque technology to trump the participatory, reason-giving traditions of justice.
UK Regulatory Framework: GDPR, AI Act, and Judicial Guidance
The UK’s regulatory landscape is evolving to address these issues, though not without gaps and criticisms. Three pillars merit discussion: data protection laws (GDPR and UK Data Protection Act 2018), the emerging EU AI Act (and the UK’s approach to AI regulation), and specific judicial or professional guidelines.
Data Protection and Automated Decision-Making (GDPR)
Although the GDPR is an EU regulation, its core provisions were retained in UK law post-Brexit (now often referred to as UK GDPR). The GDPR provides important protections relevant to AI. Article 22 GDPR grants individuals the right not to be subject to decisions based solely on automated processing that have legal or similarly significant effects on them, unless certain safeguards are in place. This rule reflects a clear intent that life-altering decisions (which would include court judgments, credit decisions, etc.) should not be made by algorithms without human involvement and without recourse for the individual. If an automated decision is allowed (for example, if authorised by law or consented to), the individual is entitled to meaningful information about the logic involved and the right to obtain human review and contest the decision. While courts are generally not going to hand over the final judgment to an AI, this provision serves as a backstop: it essentially enshrines a right to a human decision in the face of automated ones. It also underscores requirements of transparency and contestability.
Furthermore, GDPR principles of fairness, accuracy, and accountability directly apply to AI tools that process personal data. Virtually all AI in litigation will touch personal data (names, facts about parties, etc.), meaning the controller (be it a court service or a private firm) must ensure the processing is fair and does not produce unjust outcomes. For example, if an AI system used by a court was found to systematically disadvantage a protected group, this could be challenged as a violation of data protection law (as well as equality law). The Information Commissioner’s Office (ICO) has issued guidance stressing “fairness in AI”, clarifying that organisations must assess and mitigate bias, and that “the risk of errors [must be] minimised” when deploying AI decisions that affect people. Importantly, GDPR also requires Data Protection Impact Assessments (DPIAs) for high-risk processing, which would include novel AI systems in the justice system. A DPIA forces the data controller to examine risks to rights (like privacy and non-discrimination) before deployment and to consult the ICO if those risks cannot be mitigated satisfactorily.
However, some scholars argue that GDPR’s provisions, while helpful, have limits in the AI context. The law was not written with modern AI in mind, and concepts like “solely automated” decisions can be ambiguous. For instance, if a judge technically has the final say but in practice rubber-stamps an AI’s recommendation, is the decision “solely automated” or not? The line can blur. There is also an exemption in the Data Protection Act 2018 for judicial functions that might disapply certain GDPR rights when necessary to protect judicial independence and proceedings. This could, in theory, limit a litigant’s ability to query how a court used an AI tool, on grounds of protecting the judicial decision-making process. Thus, while GDPR provides a crucial framework emphasising transparency, human oversight, and fairness, applying it in the courtroom context will require careful interpretation. The bottom line is that data protection law in the UK pushes back against unfettered algorithmic decision-making, and it supports individuals’ rights to know and challenge how AI is used – all of which buttress due process values.
The EU AI Act and the UK’s Approach to AI Regulation
The EU AI Act (currently in the legislative process, expected to be EU law in 2024/2025) represents a comprehensive effort to regulate AI systems by level of risk. Under the AI Act, AI systems used in legal and judicial contexts are likely classified as “high-risk”, given their impact on fundamental rights and the rule of law. Annex III of the draft AI Act explicitly lists AI used in court decisions or to assist judicial authorities as high-risk systems. This classification means that any such AI tool must meet stringent requirements before and during deployment, including: rigorous risk assessments, high-quality training data to minimise bias, transparency measures (such as making information available about the system’s capabilities and limitations), and ensuring human oversight at all times. For example, a company providing AI judgment-writing software in the EU would have to comply with these requirements or face penalties. The AI Act is essentially a product safety law for AI, focusing on technical standards and compliance. It does not give individual litigants new rights per se (those remain under GDPR and other laws), but it imposes obligations on AI system providers and users to reduce risks. The AI Act also contemplates codes of conduct and best practices for lower-risk AI, encouraging ethical use even when not mandatory.
The UK, having left the EU, is not directly bound by the AI Act. However, the EU’s approach influences the conversation globally. The UK has signalled a different path: a “pro-innovation” framework that, at least initially, eschews broad legislation in favor of principles and sectoral guidance. In March 2023, the UK Government issued a White Paper on AI regulation proposing five core principles for AI governance: safety (and robustness), transparency (and explainability), fairness, accountability (and governance), and contestability (and redress). These principles mirror the concerns discussed above – for instance, “fairness” and “transparency” directly target bias and opacity issues, and “contestability” echoes due process rights to challenge decisions. Rather than enacting an “AI Act” statute, the UK is leaning toward empowering existing regulators (like the ICO, Equality and Human Rights Commission, Financial Conduct Authority, etc.) to apply these principles within their domains. For the justice system, this means bodies like the Ministry of Justice and judiciary would integrate the principles into policy and guidance, rather than being dictated by a single AI law.
Critics of the UK approach worry about its lack of teeth and consistency. The Law Society, in its response to the White Paper, welcomed the principles but urged the government to consider a blend of flexible regulation and “firm legislation” for truly high-risk uses. They noted that clear definitions of “high-risk contexts” and “meaningful human intervention” are needed. Without a unified law like the AI Act, there is a risk of uneven practices across sectors. Nevertheless, in the context of civil litigation, the key principles from the White Paper can be seen reflected in the judiciary’s own guidance and emerging best practices. The UK’s approach values innovation – for example, by not outright forbidding any use of AI in court and not requiring onerous approvals for every new tool – but it also places trust in judges and professionals to uphold fairness and accountability.
It’s worth noting that even outside a formal AI Act, existing UK laws can apply to AI in litigation. We already discussed data protection and equality law. Additionally, GDPR Article 22 and the AI Act will likely work “hand-in-glove,” as one commentator put it, with GDPR providing individual rights and the AI Act imposing system-level safety requirements. In the UK, one might imagine a combination of the common law and statutes achieving a similar effect: if an AI misuse led to an unfair trial, the Court of Appeal or Supreme Court could develop case law setting boundaries on AI use (in effect creating judge-made safeguards), complementing the general duties of fairness and reason-giving that already exist in common law. The UK’s Regulatory Horizon Council and other bodies are examining whether additional AI-specific legislation is needed; it remains a live policy debate. For now, the critical analysis is that the UK’s regulatory framework is piecemeal – robust in some areas (data protection, professional ethics) but arguably relying heavily on voluntary adherence to principles in others. This puts a burden on the judiciary and legal practitioners to self-regulate AI use conscientiously.
Judicial and Professional Guidelines on AI
In December 2023, the senior judiciary of England and Wales issued Artificial Intelligence (AI) Guidance for Judicial Office Holders. This six-page guidance is a landmark document acknowledging the growing role of AI in the courts and aiming to set boundaries and best practices. Key points from the judicial guidance include:
- Permissible but Cautious Use: Judges (and by extension lawyers) may use generative AI tools in principle, but “provided that it is used responsibly, and appropriate safeguards are followed.” The guidance pointedly says use of AI for legal research or legal analysis is “not recommended” at present
- Verification and Accuracy: Legal professionals are reminded that they remain fully responsible for the material they submit to the court
- Security and Confidentiality: Judges are prohibited from inputting confidential or sensitive information into public AI services
- Awareness of Bias and Deepfakes: The judicial guidance calls for awareness of AI’s limitations and risks. It instructs judges to be “alive to the potential risks” of AI across society
Beyond the judiciary, professional bodies have also issued guidance. The Solicitors Regulation Authority (SRA) and Bar Standards Board (BSB) expect lawyers to deploy technology competently and ethically. The duty of technological competence means a lawyer using AI must understand its outputs and risks – using AI blindly could breach the duty to provide a proper standard of work to the client or duty to the court. The Law Society has published practice notes about AI, reinforcing that accountability remains with the human lawyer and suggesting caution in using unvetted tools. All these guidelines contribute to a culture where AI is treated as an aid, not an oracle.
While these soft law instruments are important, a critical perspective might question if they go far enough. They rely on individual discretion (“used responsibly” is a refrain) and do not create external oversight. For example, there is no requirement to disclose to an opponent or the court if AI was used in preparing a case, “provided AI is used responsibly”. This non-disclosure norm is to avoid burdening proceedings, but it could also reduce transparency. If a litigant suspects the other side heavily relied on AI and maybe misrepresented what the AI produced, the current framework doesn’t automatically force a candid discussion of that (unless a problem comes to light). Another gap is that the guidance does not detail how to ensure AI tools themselves are audited for bias or accuracy – it places the onus on users to catch errors. In the long run, more formal standards or even accreditation for legal AI tools might be needed to complement this user-focused approach.
Implications for Litigants in Person
Litigants in person (LiPs) – individuals who represent themselves without a lawyer – stand at the intersection of the opportunities and risks presented by AI in civil justice. On one hand, AI tools have the potential to empower LiPs by providing them with accessible legal information, guidance in filling out forms, and even drafts of legal documents. This could partially bridge the knowledge gap that disadvantages self-represented parties. For example, experimental AI-driven chatbot advisors (like DoNotPay or others) aim to help individuals contest parking fines or navigate small claims procedures. The JUSTICE report notes the growth of tools designed to “assist litigants in person… with automated drafting of legal documents and ensuring [their] legal participation.” Such AI assistance can enhance access to justice by giving LiPs some of the capabilities that only lawyers would traditionally have (like the ability to quickly research case law or generate a coherent submission).
However, the pitfalls of AI may disproportionately affect LiPs:
- Misinformation and Quality Control: Without a lawyer’s oversight, LiPs might take AI outputs at face value. The Harber v HMRC case is a stark example: an unrepresented litigant relied on AI for case law and was misled into citing non-existent authorities
- Inequality of Arms: There is a concern that AI could widen the gap between represented and unrepresented parties. Well-resourced litigants (or their lawyers) might use advanced, proprietary AI analytics tools (for instance, to model judge tendencies or to sift evidence) – tools which may be expensive or unavailable to the public. Meanwhile, LiPs would be relegated to free tools, which are often less reliable (e.g. ChatGPT’s free model, with all its quirks). The JUSTICE report highlighted this as the “inequality of arms” problem, warning that proliferation of such tools “can also exacerbate inaccessibility to the law by further entrenching ‘inequality of arms’ between lawyers who can access the tool, and lawyers and litigants-in-person, who cannot.”
- Transparency and Comprehensibility: From the perspective of a LiP, the legal process is already intimidating and hard to understand. If AI tools are influencing decisions or recommendations (e.g., an online court portal using an algorithm to suggest a LiP try mediation, or an automated triage system that routes a claim to a certain track), the LiP might not understand that an algorithm made that call, let alone how or why. Lack of transparency can cause confusion and a feeling of helplessness. Procedural fairness demands that parties understand the process. Thus, any use of AI in dealing with LiPs should be clearly explained. If, say, an online system is used to evaluate the completeness of a LiP’s claim form, the system’s feedback should be given in plain language and the LiP should know it’s an automated suggestion not a final judicial decision. The judicial AI guidance underscores that judges should be aware of LiPs’ likely reliance on AI and perhaps be prepared to handle the consequences
- Access to Justice vs. The Digital Divide: AI tools and online systems might improve access to justice in theory, but only if litigants can access and use them effectively. Not all LiPs have reliable internet access or tech savvy. A move to AI-driven online courts could inadvertently exclude those who are less computer literate – often the more vulnerable in society. The UK’s drive for digitisation must consider “digital exclusion” as identified by studies
In light of these implications, what can be done to protect litigants in person? Ensuring transparency is one step: if courts use AI in any aspect that touches LiPs, they should be upfront about it and provide an explanation or human support. Another step is developing trustworthy AI tools for LiPs under public auspices – for example, the government or non-profits could offer vetted AI legal assistance tailored to UK law, which might be safer than LiPs relying on random internet chatbots. Additionally, judges and court staff could receive training specifically on handling cases involving self-represented parties who have used AI, so that merciful corrections can be made (for example, pointing out when a case cited doesn’t exist, without penalizing the LiP harshly for an honest mistake). Ultimately, the goal must be that AI augments a LiP’s ability to be heard, rather than creating new traps for the unwary. As the legal system integrates AI, it should do so in a way that “democratises access to legal services” for everyone, rather than only benefiting those who can afford the best technology.
Conclusion
The use of AI in UK civil litigation presents a double-edged sword. It holds significant promise to streamline justice – from faster document review and research to potentially reducing costs and delays – which could improve access to justice in an overburdened system. Yet, as this paper has detailed, those very tools can introduce serious pitfalls: embedded biases that discriminate against protected groups or perpetuate past injustices, automation-fueled confirmation bias that undermines independent judicial thinking, and opaque processes that threaten the transparency and fairness essential to due process. The regulatory framework in the UK is developing in response. Data protection law (GDPR) provides a baseline that insists on human oversight, fairness, and accountability in automated decisions. European initiatives like the AI Act are setting standards that will likely influence UK practices, even as the UK forges its own principles-led regulatory path. Crucially, the judiciary’s recent guidance on AI demonstrates a thoughtful, precautionary approach: welcoming innovation but with eyes wide open to the risks of inaccuracy, bias, and loss of human control.
For litigants in person, who often stand at the fragile front line of the justice system, the advent of AI could be either a boon or a bane. It is incumbent on the legal system to strive for the former – harnessing AI to empower individuals with information and tools, while rigorously safeguarding them from the technology’s failure modes. Access to justice, transparency, and procedural fairness must remain paramount. This requires not just formal regulation but also cultural change: lawyers and judges must maintain a healthy skepticism of AI, developers must work with ethicists and legal experts to produce fair and explainable AI tools, and support must be offered to litigants to navigate this new terrain. As one report put it, we should adopt a “rights-based framework” for AI in the justice system, ensuring that rule of law values are “embedded at each stage” of design and deployment
In conclusion, AI will undoubtedly become a fixture in civil litigation, but it need not be a threat to justice if handled wisely. The challenges of bias and due process are real, but they are surmountable with robust safeguards. The lesson from early cases and studies is clear: human accountability and oversight are irreplaceable. So long as AI remains a servant of human judges and not a surrogate, and so long as litigants’ rights to understand and challenge the process are respected, technology can be integrated without diluting the fairness of civil litigation. The road ahead demands vigilance, interdisciplinary collaboration, and possibly new legal doctrines, but with these in place, the justice system can innovate confidently. As Sir Geoffrey Vos aptly noted, “the judiciary must embrace … developing technologies in our justice system, whilst ensuring that AI is used safely and responsibly.” Balancing innovation with fundamental rights will be the key to unlocking AI’s benefits while avoiding its pitfalls in the courts of the future.
References (Cases, Legislation, and Reports)
- Pyrrho Investments Ltd v MWB Property Ltd [2016] EWHC 256 (Ch) – First English case approving the use of predictive coding in e-disclosure
- Harber v HMRC (2023) – First-tier Tax Tribunal case where a litigant in person submitted AI-generated fake case citations; Judge Redston’s decision (unreported) highlighted the risks of unverified AI outputs in litigation
- R (Bridges) v Chief Constable of South Wales Police [2020] EWCA Civ 1058 – Court of Appeal judgment on police use of facial recognition; held that lack of consideration of algorithmic bias breached the Public Sector Equality Duty
- General Data Protection Regulation (EU) 2016/679 (UK GDPR) – Particularly Article 22 and Recital 71, providing rights regarding automated decision-making and requiring safeguards against bias
- Equality Act 2010 (UK) – Section 149 (Public Sector Equality Duty) as applied in Bridges case, mandating public authorities to have due regard to eliminating discrimination in new technologies.
- Proposed EU Artificial Intelligence Act (Draft, 2023) – EU regulation identifying AI in justice as “high-risk” and imposing requirements of transparency, oversight, and accuracy
- UK Government, AI Regulation: A Pro-Innovation Approach White Paper (March 2023) – Policy document outlining five principles for AI governance (safety, transparency, fairness, accountability, contestability) to be applied in the UK
- Courts and Tribunals Judiciary, Artificial Intelligence (AI) Guidance for Judicial Office Holders (12 Dec 2023) – Official guidance note to judges on the responsible use of AI in litigation
- JUSTICE, AI in our Justice System (2025) – Report proposing a rights-based framework for AI in the UK justice system, focusing on access to justice, fair decision-making and transparency
- Fair Trials, Automating Injustice (2021) – Report examining AI in criminal justice and its impact on fair trial rights, highlighting risks of bias and lack of safeguards
- LSE Policy Blog, Trial by Artificial Intelligence? (2023) – Analysis by Dr. Giuffrida on how AI is reshaping the legal system, and the potential threats to judicial independence and rule of law
- Varun Magesh et al, “Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools” (2024) – Forthcoming study (arXiv preprint) on the accuracy of AI legal research systems, noted in JUSTICE report
- Article 29 Working Party, Guidelines on Automated decision-making and Profiling (WP251, 2018) – Interprets GDPR’s provisions, emphasising rights to explanation and non-discrimination in automated decisions (relevant to Recital 71)
Disclaimer:
This report is for informational and academic purposes only. It does not constitute legal advice and should not be relied upon as a substitute for professional legal counsel. While every effort has been made to ensure accuracy, AI technology and legal frameworks evolve rapidly, and readers should consult legal professionals for case-specific guidance. The views expressed are those of the author and do not necessarily reflect those of any regulatory or legal body.