AI in the judiciary: Ensuring Efficiency or Compromising Fairness

Abstract:

The incorporation of Artificial Intelligence (AI) within the judicial system has sparked an  international discourse focused on its dual effects—enhancing efficiency on one side, while  potentially undermining fairness on the other. This article delves into both perspectives: the  potential of AI to facilitate quicker, more economical legal procedures through automation and  predictive analytics, alongside significant concerns regarding transparency, accountability,  bias, and compliance with the rule of law. Current AI implementations in legal decision-making  are exemplified by tools such as COMPAS in the United States and the “Smart Court” system  in China. Furthermore, the paper explores the legal ramifications of introducing AI judges,  analyses various global strategies—particularly in the USA, China, and Europe—and  investigates whether the collaboration between human and machine intelligence can maintain  democratic values while rectifying existing inefficiencies in judicial processes.

Key words:

Artificial Intelligence (AI), Judiciary, Judicial efficiency, Judicial fairness, Transparency,  Accountability, Bias, Rule of Law, COMPAS, Smart Court, Legal technology, Decision support  systems, Algorithmic justice, Human oversight, Explainability, Contestability, Legal  implications Ethical concerns, Digital courts, Fair trial

Introduction:

The conversation regarding artificial intelligence in the legal system is shaped by two  conflicting principles: “Artificial Intelligence in the legal system ensures the efficiency of the  judicial process” and “Artificial Intelligence in the legal system jeopardizes the fairness of the  judicial process.” Both viewpoints strive for a “just legal system.” AI has the potential to  enhance judicial efficiency, which is understood as providing accurate responses, and judicial  effectiveness, which is defined by delivering answers with minimal time and resource use. The  first viewpoint emphasizes efficiency, while the second underscores the risk of AI undermining  the fairness of judicial processes, as evaluating fairness is inherently more intricate than  detecting machine errors. Both aspects are vital components of the sought-after fair legal  system. A.I. is distinct from other tools previously employed¹. It does not generate   predetermined or static responses. Rather, it has the capacity to think autonomously and  identify patterns to make informed decisions. This ability carries significant implications, as  the stakes are considerable and directly affect individuals’ lives. Instances of such AI  applications in judicial processes include COMPAS (Correctional Offender Management  Profiling for Alternative Sanctions), which is used in the United States to evaluate the  probability of a criminal reoffending. Furthermore, China has adopted the “Smart Court”  system, which leverages AI to automate routine tasks such as case management and even  drafting judgments. The advent of new AI technologies presents the legal system with an  opportunity to implement a broad range of procedural reforms, legal frameworks, or even  everyday administrative tasks to create a more effective and efficient justice system. The  integration of artificial intelligence (AI) into judicial systems is revolutionizing courts  worldwide. AI-driven tools promise enhanced efficiency, faster case resolution, and improved  legal research, yet they also provoke urgent concerns regarding fairness, transparency, and  accountability.

LEGAL Counteraccusations OF USING AN AI JUDGE:

Artificial Intelligence (AI) is a field concentrated on creating machines able of performing  tasks that generally bear mortal intelligence. It encompasses two main exploration areas one  grounded on rules, sense, and symbols, furnishing resolvable results but limited to scripts with  foreseeable issues, and the other grounded on exemplifications, data analysis, and correlation,  suitable for addressing ill- defined problems but taking vast quantities of data and offering  lower explainability with a small periphery of error. These approaches are decreasingly merged  to maximize their benefits and minimize their downsides. Recent advancements in AI, driven  by bettered algorithms, increased calculating power, and abundant data, have led to successful  operations in colourful disciplines, similar as speech- to- textbook, image interpretation, and  more, enabling AI systems to attack real- life scripts with query. Despite the current  proliferation of consumer- acquainted AI operations, the true eventuality of AI, known as  enterprise AI, lies in accelerating mortal capabilities and easing informed decision- making  through different professional fields, including healthcare, education, finance, and others, by  using vast quantities of data. The community between AI and mortal intelligence yields optimal results, with enterprise AI offering decision- support systems to professionals navigating  complex data- driven opinions².

In the perpetration of AI judges, a multitude of legal factors come under scrutiny, each playing a vital part in icing the integrity and fairness of judicial processes. translucency emerges as a  foundation, as the nebulosity of AI decision- making algorithms challenges the traditional  notion of open justice and public scrutiny. coincidently, responsibility becomes a pressing  concern, as the criterion of responsibility for AI- generated opinions becomes blurred,  potentially undermining the responsibility mechanisms essential in the rule of law. also, the  spectre of bias looms large, with the threat of AI systems immortalizing and indeed aggravating  being societal prejudices, thereby compromising the principle of equal justice under the law.  These developments raise profound questions about the comity of AI judges with popular  principles, the right to a fair trial, and the broader legal frame governing judicial proceedings.  As similar, a comprehensive examination of these legal factors is essential to insure that the  perpetration of AI judges upholds abecedarian rights, strengthens the rule of law, and preserves the integrity of the legal system.

Rule of law

The conversation regarding artificial intelligence in the legal system is shaped by two  conflicting principles: “Artificial Intelligence in the legal system ensures the efficiency of the  judicial process” and “Artificial Intelligence in the legal system jeopardizes the fairness of the  judicial process.” Both viewpoints strive for a “just legal system.” AI has the potential to  enhance judicial efficiency, which is understood as providing accurate responses, and judicial  effectiveness, which is defined by delivering answers with minimal time and resource use. The  first viewpoint emphasizes efficiency, while the second underscores the risk of AI undermining  the fairness of judicial processes, as evaluating fairness is inherently more intricate than  detecting machine errors. Both aspects are vital components of the sought-after fair legal  system. A.I. is distinct from other tools previously employed. It does not generate  predetermined or static responses. Rather, it has the capacity to think autonomously and  identify patterns to make informed decisions. This ability carries significant implications, as  the stakes are considerable and directly affect individuals’ lives. Instances of such AI  applications in judicial processes include COMPAS (Correctional Offender Management  Profiling for Alternative Sanctions), which is used in the United States to evaluate the  probability of a criminal reoffending. Furthermore, China has adopted the “Smart Court”  system, which leverages AI to automate routine tasks such as case management and even  drafting judgments. The advent of new AI technologies presents the legal system with an  opportunity to implement a broad range of procedural reforms, legal frameworks, or even  everyday administrative tasks to create a more effective and efficient justice system. The  integration of artificial intelligence (AI) into judicial systems is revolutionizing courts  worldwide

Furthermore, the rule of law emphasises predictability and fairness, but AI systems can  inadvertently introduce biases and discrimination. Data sets used to train AI models often  reflect existing social biases, which can lead to discriminatory outcomes. The Loomis case  highlighted concerns about racial bias, as studies showed that COMPAS disproportionately  classified minority offenders as higher risk. Such biases undermine the principles of equality  before the law and non-discrimination, core elements of the rule of law as emphasised by the  Venice Commission. AI also poses a threat to the traditional legal protections and the balance  of power within the judicial system. The presumption of innocence and the right to a fair trial  are challenged when AI predicts recidivism or criminal behaviour, effectively judging  individuals on potential future actions rather than proven conduct. Additionally, the use of AI  by the judiciary raises questions about judicial independence. Judges may feel pressured to rely  on AI generated risk assessments, fearing to go against what is perceived as an objective,  scientific tool, thus compromising their autonomy. The right to contest decisions, a cornerstone  of the rule of law, is significantly weakened by the use of opaque AI systems. The complexity  and proprietary nature of AI technologies hinder individuals’ ability to understand, challenge,  or appeal decisions made by such systems. Proposals like creating a National Register of  Algorithmic Systems or incorporating contestability into the design of AI systems aim to  address these issues, but the implementation of these solutions remains complex and  challenging³.

Accountability:

There exists considerable uncertainty regarding who holds legal responsibility when a judicial  outcome generated by AI is flawed. Potential parties include the developer of the AI system,  the judge utilizing the AI, or the institution that approved the technology.

The lack of clarity in assigning liability may result in procedural gaps, which could compromise accountability and the rights of litigants who are seeking redress for judicial mistakes.

Responses to this issue vary by policy. For instance, certain jurisdictions, such as Kerala in  India, have explicitly prohibited judges from employing AI tools as replacements for legal  reasoning or for delivering judgments, advocating for a human-in-the-loop methodology to  ensure fairness and accountability⁴.

Evolution of AI in judicial system:

The integration of artificial intelligence into judicial systems has undergone a remarkable  transformation over the past three decades, evolving from basic automation to sophisticated  decision-support systems that are reshaping how justice is administered globally.

Early Foundation(1990S-2000):

The journey began in the 1990s with Basic Document Management System and Case Filing. Courts initially adopted simple automation tools to digitize records and streamline  administrative processes. The Logic Theorist. and early expert systems laid the groundwork for legal AI applications, focusing primarily on rule-based systems that could process  straightforward legal procedures.

Risk Assessment Era:(2000s-2010s)

The 2000s marked a pivotal shift toward predictive analysis in criminal justice. COMPAS  (Correctional offender management profiling for Alternative Sanctions), developed by  Northpointe in 1989 but widely adopted during this period, became one of the first AI systems  to influence judicial decisions. Virginia pioneered statewide use of risk assessment tools in  2002, demonstrating AI’s potential to guide sentencing decisions for non-violent offenders.

This era saw the emergence of algorithmic risk assessment across multiple jurisdictions, with  states like Arizona, Colorado, and Wisconsin implementing AI-driven tools to evaluate defendants’ likelihood of reoffending, flight risk, and rehabilitation needs⁵.

Global Perspective:

Summary Table: USA vs CHINA vs EUROPIA Global Approaches to AI in Judicial  System⁶:

Key Concerns: The Ethical and Legal Dilemmas

Key Ethical Challenges:

Bias And Fairness:

Data-Driven Discrimination: AI systems learn from historical case data. If past  judicial data contain biases—racial, socioeconomic, or otherwise—AI models can  perpetuate and even amplify these inequities, disproportionately affecting marginalized  groups.

Lack of Contextual Understanding: AI cannot fully grasp the nuances of individual  cases or extenuating circumstances, potentially reducing complex human realities to  quantifiable metrics, which may compromise individualized justice.⁷

Judicial Independence and Discretion

Automation Bias: Judges may become overly reliant on AI recommendations, risking  undue influence and loss of independent judgment, known as “automation bias.” This  may erode judges’ critical assessment and diminish judicial discretion in interpreting  the law on a case-by-case basis.

Homogenization Risk: Uniform, AI-generated outcomes risk minimizing the diversity and  adaptability of human judicial reasoning necessary for just and creative interpretations of law.

Key Legal Dilemmas:

Due Processes and Right to Fair Trial:

Procedural Fairness: The right to an impartial and fair trial requires clarity in how decisions are made. Opaque AI systems may violate due process standards and reduce opportunities for  meaningful appeals.

Access to Legal Recourse: Challenging decisions made or influenced by algorithms is difficult  when the rationale is obscure or proprietary.⁸

Data Privacy and Security: 

Handling Sensitive Information: AI systems often process sensitive personal data, raising  concerns about privacy, data protection, and compliance with legal frameworks.

Standardization vs. Individual Justice:

Potential for Uniformity Over Justice: AI’s tendency to standardize can conflict with the  need for individualized rulings, particularly when unique circumstances demand flexible legal  reasoning⁹.

AI and The Indian Legal System: A special focus:

▪ The Pendency Crisis:

India’s court system confronts an extraordinary challenge, with more than 5 crore (50 million)  legal cases awaiting resolution throughout all court tiers—a figure that grows annually. Lower  courts alone handle over 85% of this enormous accumulation of cases. In regions such as Uttar  Pradesh and Maharashtra, lower courts are dealing with millions of unresolved cases, with  more than 25% of these remaining unaddressed for over five years. Operating with merely 21  judges for every million citizens—significantly below the recommended 50 per million— India’s legal framework struggles under massive caseloads, inadequate infrastructure, and  ongoing judicial position shortages.

▪ Can AI Help Clear the Backlog?

Artificial Intelligence (AI) is gaining recognition as a powerful solution for India’s  overwhelmed judicial system. The incorporation of AI technology within Indian courts has  already started, featuring uses in translating documents, transcribing spoken arguments,  conducting legal research, and automating administrative processes. AI-enabled systems are  capable of:

• Handling repetitive court operations including appointment scheduling, case  monitoring, and file organization
• Accelerating legal research and creating judgment summaries for judicial officers and  legal practitioners
• Supporting forecasting analysis to determine which cases are appropriate for quick  resolution or negotiated settlements
• Enhancing case organization, resource distribution, and minimizing mistakes made by  staff

Sophisticated AI systems, exemplified by China’s “Smart Courts,” manage case submissions,  examine evidence, and conduct initial case evaluations—frameworks that India could modify  for its own circumstances. Nevertheless, AI should be viewed primarily as a supportive  instrument; while it can reduce the burden on judicial personnel, it cannot and must not  substitute the critical thinking and judgment that human judges provide.

Is India Ready? Challenges for Technology and Digital Literacy Gaps:

Despite its promise, AI’s adoption in India faces significant hurdles, especially in rural  and marginalized settings:

Digital Skills Gap: Numerous rural citizens involved in legal proceedings, along with  certain court personnel, do not possess the technological competencies required to  navigate AI-powered systems or participate in virtual court proceedings. The  technology gap continues to be substantial, particularly affecting elderly individuals  and those residing in isolated areas.

Inadequate Infrastructure: Dependable internet access and computing resources are  not consistently accessible across rural judicial facilities. Insufficient technological  foundations restrict the successful implementation of remote court sessions, electronic  filing systems, and digital dispute resolution services.

Communication Obstacles: Most AI-based legal applications function primarily in  English or Hindi, creating accessibility barriers for legal practitioners and clients in  areas where other local languages are predominant. Achieving genuine accessibility  necessitates advanced, multi-language AI capabilities.

Government programs like the e-Courts Project Phase III and Tele-Law have established initial  progress, yet comprehensive and effective implementation requires additional investment in  technology education, infrastructure development, and multilingual technology solutions¹⁰.

Conclusion:

The integration of Artificial Intelligence into judicial systems creates a multifaceted challenge:  while it holds potential for enhanced efficiency, reduced costs, and improved access to justice,  it also generates serious concerns about equity, responsibility, and openness. AI applications— including risk evaluation systems in the United States, “Smart Courts” in China, and ethics centred tools in Europe—illustrate the varied worldwide strategies for incorporating  technology into legal frameworks. Substantial worries remain, particularly about the potential  for magnifying current prejudices embedded in past data, the clarity of automated judgments,  and the weakening of public confidence. Although AI can assist judicial systems in optimizing  procedures and handling case volumes, equity and proper legal process must not be sacrificed.  A measured strategy—maintaining essential human supervision, enforcing regulations that  ensure responsibility, and allowing ethical principles to guide advancement—is crucial to  guarantee that the quest for judicial effectiveness does not undermine justice and personal  freedoms. Only through careful protective measures can judicial systems utilize AI’s  advantages while preserving the core values of laws.

Reference(S):

1. 1.Press Information Bureau, ‘Use of AI in Supreme Court Case Management’ (20 March 2025)  https://www.pib.gov.in/PressReleasePage.aspx?PRID=2113224 accessed 22 July 2025.
2. Abhijith Balakrishnan, ‘Ethical and Legal Implications of AI Judges: Balancing Efficiency and the Right to Fair Trial’ (Master’s thesis, Utrecht University 2024) https://studenttheses.uu.nl/handle/20.500.12932/48242 accessed 22 July 2025
3. Unknown author, ‘Untitled’ (Google Drive shared file,2025) https://share.google/sZWWwSA3iliU0yMp0 accessed 22 July 2025.

4. Ott Velsberg and Estonian Ministry of Justice, ‘Estonian AI judge pilot project’(2019) https://www.weforum.org/stories/2019/03/estonia-is-building-a-robot-judge-to-help-clear-legal backlog/ accessed 22 July 2025

5. Using AI and ChatGPT in legal cases: What Indian courts have said’ (Indian Express, 28 May 2024) https://indianexpress.com/article/explained/explained-law/ai-chatgpt-high-courts-judiciary 9356510/ accessed 22 July 2025.

^{1 Press Information Bureau, ‘Use of AI in Supreme Court Case Management’ (20 March 2025)  https://www.pib.gov.in/PressReleasePage.aspx?PRID=2113224 accessed 22 July 2025.}

² Abhijith Balakrishnan, ‘Ethical and Legal Implications of AI Judges: Balancing Efficiency and the Right to Fair  Trial’ (Master’s thesis, Utrecht University 2024) https://studenttheses.uu.nl/handle/20.500.12932/48242 accessed 22 July 2025

³ Abhijith Balakrishnan, ‘Ethical and Legal Implications of AI Judges: Balancing Efficiency and the Right to Fair  Trial’ (Master’s thesis, Utrecht University 2024) https://studenttheses.uu.nl/handle/20.500.12932/48242 accessed 22 July 2025

⁴ Abhijith Balakrishnan, ‘Ethical and Legal Implications of AI Judges: Balancing Efficiency and the Right to Fair  Trial’ (Master’s thesis, Utrecht University 2024) https://studenttheses.uu.nl/handle/20.500.12932/48242 accessed 22 July 2025

⁵ Unknown author, ‘Untitled’ (Google Drive shared file, 2025) https://share.google/sZWWwSA3iliU0yMp0 accessed 22 July 2025.

⁶ Unknown author, ‘Untitled’ (Google Drive shared file, 2025) https://share.google/sZWWwSA3iliU0yMp0 accessed 22 July 2025.

⁹ Ott Velsberg and Estonian Ministry of Justice, ‘Estonian AI judge pilot project’  (2019) https://www.weforum.org/stories/2019/03/estonia-is-building-a-robot-judge-to-help-clear-legal backlog/ accessed 22 July 2025

¹⁰ Using AI and ChatGPT in legal cases: What Indian courts have said’ (Indian Express, 28 May  2024) https://indianexpress.com/article/explained/explained-law/ai-chatgpt-high-courts-judiciary 9356510/ accessed 22 July 2025.