Algorithms Of Injustice - Unmasking AI Bias And Human Rights Violations In India

Author: LawSphereX

Category: Constitutional & Human Rights

Published: 2026-08-23T17:47:48.470669+00:00

Updated: 2026-08-26T17:12:19.662+00:00

ALGORITHMS OF INJUSTICE: UNMASKING AI BIAS AND

HUMAN RIGHTS VIOLATIONS IN INDIA

Abhinav Raj, Central University of South Bihar Avichal Kumar, Central University of South Bihar

ABSTRACT

The integration of Artificial Intelligence (AI) into the Indian judicial system presents a transformative opportunity to enhance efficiency and accessibility. However, this technological advancement brings forth a significant challenge: the risk of algorithmic bias. This report provides a detailed analysis of algorithmic bias within the Indian legal context, examining its definition, various types, and potential sources. It explores the current landscape of AI applications in the judiciary, supported by empirical data and real-world examples, and analyzes how algorithmic bias can lead to violations of fundamental human rights as enshrined in the Indian Constitution. Through a comparative legal study with other jurisdictions like the US, UK, and EU, this paper highlights instances of AI bias and their regulatory responses.

It critically assesses the adequacy of India’s existing legal and regulatory framework to address this critical issue and concludes with actionable recommendations for ensuring fairness, accountability, and ethical AI adoption in the Indian judiciary.

Introduction: The Promise and Peril of AI in the Indian Judiciary The integration of Artificial Intelligence (AI) across industries is a rapidly growing global phenomenon, and the judicial system is no exception. In India, the judiciary, which is one of the biggest in the world, is faced with serious challenges like a massive pendency of cases, with more than 5 crore cases pending as of February 2025, and inbuilt inefficiencies in the dispensation of justice.1 Technology, and more specifically AI, is being considered as a powerful means to streamline processes, potentially lower costs, and ultimately increase efficiency in the system of delivery of justice.2 Initiatives like the e-Courts Project Phase

III are a sincere attempt towards the adoption of state-of-the-art AI solutions to improve case

management and administrative efficiency in different courts of India. This phase is proposed to bring about a regime of maximum ease of justice by shifting towards digital, online, and paperless courts.3 The government has sanctioned ₹7210 Crore for this project, out of which ₹53.57 Crore is for

and blockchain technology.4 Further, in consideration of the revolutionary nature of

AI,

the Supreme Court of India has set up an Artificial Intelligence Committee with the mandate to consider the scope and deployment of AI in the judicial ecosystem. This has resulted in the creation of AI-based systems like SUVAS (Supreme Court Vidhik Anuvaad Software), which facilitates the translation of judicial documents, and SUPACE (Supreme Court Portal for Assistance in Court Efficiency), to improve court efficiency. The Supreme Court made it clear that such tools are now being employed for case management and translation, not for decision-making. 5 While the promise of AI in revolutionizing the Indian judicial system is substantial, offering the potential for automating administrative tasks, improving the tracking of cases, and even enhancing crime prevention , a significant concern looms large: the risk of algorithmic bias.

6 The deployment of AI in such a critical domain has initiated important discussions among legal experts and the general public regarding the dependability of these systems, the potential for

1 Integrating AI in India's Judiciary and Law Enforcement - NEXT IAS, accessed on March 31, 2025,

https://www.nextias.com/ca/current-affairs/27-02-2025/integrating-ai-in-india-judiciary-and-law-enforcement

2 Ibid

3 E-Courts Mission Mode Project - Press Release: Press Information ..., accessed on March 31, 2025,

https://pib.gov.in/PressReleaseIframePage.aspx?PRID=2085127

4 Supra note 1

5 Use of AI in Supreme Court Case Management - Press Release ..., accessed on March 31, 2025,

https://pib.gov.in/PressReleasePage.aspx?PRID=2113224

6 Supra note 1

biases to be embedded within their algorithms, and the subsequent influence these biases could have on judicial rulings. 1 Globally, the issue of bias in AI is increasingly recognized as a significant challenge that needs careful consideration to ensure the achievement of justice goals. 7 There are valid concerns that AI could inadvertently introduce or even amplify existing biases and discrimination within social justice and adjudication processes. 8 This is particularly pertinent in the Indian context, a nation characterized by its rich diversity and a complex social hierarchy that has historically been marked by inequalities based on factors such as caste, religion, and socioeconomic status. 9 India, with its rich diversity and complex legal structure, requires AI models that are not only accurate but also equitable across different demographics to ensure justice for all citizens.

10 The presence of such deeply rooted societal structures implies that data reflecting these realities is highly susceptible to containing inherent biases. 11 If AI models are trained on this potentially biased data without adequate safeguards, they risk learning and subsequently perpetuating these biases within the judicial system, potentially leading to discriminatory outcomes for communities that are already marginalized.

12 This paper will delve into the definition and sources of algorithmic bias in legal contexts, provide empirical data on AI adoption in the Indian judiciary, incorporate global case studies of algorithmic bias, analyze how algorithmic bias can lead to violations of fundamental human rights as enshrined in the Indian Constitution, examine the ways in which it can erode the principles of fair justice, discuss strategies for the detection and mitigation of such biases, conduct a comparative legal study of AI and human rights in India with a focus on algorithmic bias, and finally, assess the adequacy of the existing legal and regulatory framework in India to address this critical challenge. II.

Defining and Understanding Algorithmic Bias in Legal Contexts: A Detailed Analysis Algorithmic bias, in the legal sense, means systematic and discriminatory differences in treatment or outcomes produced by an AI system that discriminatorily affect groups or 7 Bias in adjudication: Investigating the impact of artificial intelligence, media, financial and legal institutions in pursuit of social justice -

PMC

PubMed Central, accessed on March 31, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC11698438/

8 Ibid

9Data-driven justice: Survey on approaches to identify and mitigate biases to build fair artificial intelligent models for Indian justice system - Keerthi Lingam, Suresh Chittineni, 2025 - Sage Journals, accessed on March 31, 2025, https://journals.sagepub.com/doi/10.1177/18724981241301281

10 Ibid

11 Ibid

12 Ibid

individuals on the basis of protected characteristics like religion, race, caste, sex, or place of birth, which are grounds of discrimination under the Indian Constitution.13 It is a departure from fairness, whereby the decision or prediction of the algorithm disproportionately affects or favors some groups without a reasonable legal rationale. It can take many different forms at different points in the life cycle of an AI system, ranging from the data on which it is trained to the algorithm's design itself and its ultimate deployment.14 Although India does not have an express statutory definition of algorithmic bias, the phenomenon overlaps with settled legal concepts of discrimination and equality under the Constitution and laws.15 A number of different forms of algorithmic bias exist and may affect the Indian judicial system:

A. Data Bias

This is probably the most prevalent source of algorithmic bias and is a result of the training and test data employed to train AI models. Biased data used to develop algorithms end up heavily skewing the automated decision-making process, thus negating the efforts to counteract inherent disparities and social injustices. 16 a) Historical Bias: This happens when the training data itself contains historical biases and discriminatory practices in society. In India, historical judicial data might contain biases against marginalized groups. For example, if arrest data disproportionately highlights the policing of specific castes or religious groups because of ingrained biases in the police, an AI system trained on such data might incorrectly learn to correlate these traits with a greater likelihood of criminality.

Such an overestimation might result in biased outcomes in predictive policing or risk assessment systems. Legally, such bias can be interpreted as continuing historical injustices, which might contravene Article

14 (equality before the law) and Article 15 (prohibition of discrimination based on

religion, race, caste, sex, or place of birth) of the Constitution.

13 Supra note 9

14 LEGAL CHALLENGES OF AI BIAS IN INDIA: ADDRESSING THE ..., accessed on March 31, 2025,

https://www.legal500.com/developments/thought-leadership/legal-challenges-of-ai-bias-in-india-addressingthe-issue-of-fairness-and-discrimination/ 15 Artificial Intelligence in the Indian Criminal Justice System: Advancements, Challenges, and Ethical Implications | Journal of Lifestyle and SDGs Review, accessed on March 31, 2025, https://sdgsreview.org/LifestyleJournal/article/view/4877 16 Data-driven justice: Survey on approaches to identify and mitigate biases to build fair artificial intelligent models for Indian justice system - Keerthi Lingam, Suresh Chittineni, 2025 - Sage Journals, accessed on March 31, 2025, https://journals.sagepub.com/doi/10.1177/18724981241301281

b) Bias of Representation: This is a phenomenon where specific groups are overrepresented or underrepresented in the training set. If data employed to train an artificial intelligence system to examine the law predominantly comprises judicial rulings from specific geographic regions or entails specific categories of cases, to the detriment of those of significance to marginalized groups, the AI's understanding of legal rules can be skewed, leading to biased search results or analytical conclusions. This bias can exclude access to justice for underrepresented groups, possibly violating the spirit of Article 14. For example, research has found extensive underrepresentation of Muslim and Dalit communities in internet user data, which could negatively impact algorithmic performance.

c) Measurement Bias: This occurs when the attributes used to train the AI are defective or do not optimally represent the underlying event in each group. As an example, if socioeconomic status is used as a factor in a bail risk assessment tool, and the methods used to determine it are discriminatory against communities, the resulting risk assessments will be discriminatory and inaccurate. These results could lead to discriminatory detention decisions, which would be against Article 21 (protection of life and personal liberty).

B. Algorithm Bias

Bias can also be introduced in designing and implementing the algorithm itself. This happens when the algorithm unintentionally favors some groups over others. 17 a) Bias of Selection: This may occur in data or feature selection for the algorithm. If variables linked with protected characteristics are included in the model without proper scrutiny, they may serve as surrogates for discrimination. For instance, the use of a seemingly neutral feature such as "area of residence" in a crime prediction algorithm may potentially introduce bias if specific neighborhoods have historical links with particular marginalized communities and a history of over-policing. This indirectly infringes Article 15.

b) Omitted Variable Bias: This is where important variables that may have the potential to explain the outcome are left out of the model, and the left-out variables correlate with the included variables and outcome in a way that differs between groups. In a 17 Data-driven justice: Survey on approaches to identify and mitigate biases to build fair artificial intelligent models for Indian justice system - Keerthi Lingam, Suresh Chittineni, 2025 - Sage Journals, accessed on March 31, 2025, https://journals. sagepub. com/doi/10. 1177/18724981241301281

sentencing algorithm, if counsel access or socioeconomic status are not controlled well, and if these variables affect some groups disproportionately, the output sentencing recommendations will be biased. This can subsequently lead to breaches of Article 14 and Article 21.

C. Interpretation Bias

It is caused by the way humans interpret and use the results generated by an AI system. 18 Even if an algorithm is judged to be fair according to certain parameters, its interpretation and the judgments passed by judges or lawyers based on its predictions or suggestions may introduce bias. 19 Suppose a judge lays too much importance on an AI's risk assessment score without fully considering its limitations or inherent bias, this might lead to unfair judgments. 20 This highlights the necessity of human intervention and critical review of AI outputs in the judicial system. 21 While there are no specific statutory definitions of such forms of algorithmic bias in Indian law as of now, the present legal regime does offer principles which can be applied to address them.

22 The principle of general equality before law under Article 14 requires the use of AI in the judicial process not to result in discriminatory judgments. 23 Likewise, the ban on discrimination on certain grounds under Article 15 suggests that algorithmic regimes should not embed bias on such protected grounds. The right to life and liberty under Article 21 requires the orders on the basis of AI impacting an individual's liberty to be just, fair, and reasonable, and that this would include remedying potential algorithmic biases which would result in unjust outcomes. 24 Additionally, the principles of natural justice, like right to fair hearing and reasoned orders, can be invoked to challenge decisions on the basis of opaque or biased algorithmic regimes.

25 As AI is increasingly finding its place in the Indian judiciary, there is increasingly a need legislative intervention to define and address the various forms of algorithmic bias specifically so that the aspiration for technologically efficient justice is not at the expense of constitutional rights and just justice. 26

18 Supra note 17

19 Ibid

20 Ibid

21 Ibid

22 Supra note 15

23 Implications of the Use of AI Algorithms in the Criminal Justice System from a Constitutional Perspective - Mahant P A, Jus Corpus Law Journals, accessed on March 31st , 2025, https://www.juscorpus.com/wpcontent/uploads/2024/02/26.-Mahanth-P-A.pdf

24 Ibid

25 Supra note 23

26 Supra note 15

III.

The Landscape of AI Applications in the Indian Judicial System: A Double-Edged Sword AI is driving a revolutionary transformation in India's judiciary, improving efficiency, accessibility, and decision-making. The e-Courts Project Phase III incorporates cutting-edge AI solutions to improve case management and administrative efficiency in Indian courts. 27 AI technologies such as Machine Learning (ML), Natural Language Processing (NLP), and Predictive Analytics are being used to automate administrative processes, improve case tracking, and improve crime prevention. Empirical Evidence on AI Adoption: Until early 2025, the e-Courts project has made considerable progress in AI implementation. ₹53. 57 Crore has been invested directly in AI and blockchain technologies in High Courts.

28 AI software such as SUVAS is utilized by the Supreme Court for judgment translation into 18 Indian languages to enhance accessibility. 29 SUPACE is helping judges with case research and legal analysis. 8 AI is also being utilized for intelligent scheduling, case timeline prediction, and automated filing using OCR and NLP. 30 Some states have utilized AI-based police chatbots for online FIR filing and case status. 31 Specific uses of AI in the Indian judicial system are case management for intelligent scheduling, case prioritization, and backlog management. AI is also applied to legal research and documentation by making it easier and identifying relevant precedents. 32 AI-based filing and court hearings are being adopted through document automation and OCR methods. AIbased virtual legal assistants and chatbots offer real-time information and procedural advice to litigants.

AI-based predictive outcome of cases and risk analysis is another use, based on past data. 33 AI is also used for transcription of oral arguments in the Supreme Court. 34 AI for law enforcement and crime prevention is predictive policing, surveillance, investigation, and

27 Supra note 1

28 Supra note 1

29 (PDF) Employing Artificial Intelligence for Adjudication in Indian Courts - ResearchGate, accessed on March 31, 2025, https://www.researchgate.net/publication/387264813_Employing_Artificial_Intelligence_for_Adjudication_in_I ndian_Courts

30 Supra note 1

31 Ibid

32 Ibid

33 Supra note 1

34 Ibid

forensic analysis.35 The application of such programs is promising more efficiency, less delay, and better accessibility in the Indian judiciary. But they are also prone to inherent risks of ethical concerns, data confidentiality, and susceptibility to bias.36 The Supreme Court has made it clear that although artificial intelligence programs are applied in the case handling, they are not employed in any decision-making.37 IV.

Algorithmic Bias: An Immediate Threat to Basic Rights in India Algorithmic bias poses the biggest threat to the fundamental rights enshrined in the Indian Constitution. 38 • Article 14 (Equality before the law) is negated when AI systems replicate historical biases, and discriminatory outcomes for marginalized groups are produced. 39 Discriminatory use of AI-based tools can lead to unequal treatment on the basis of caste, religion, gender, and economic status. 40 Investigations reveal that marginalized groups, Adivasis and Dalits, are disproportionately likely to be accused of more serious crimes, and many death row prisoners belong to backward classes and religious minorities.

41 • Article 15 (Prohibition of discrimination on grounds of religion, race, caste, sex or place of birth) is under the threat because AI systems trained on discriminatory data may learn and reinforce discrimination against certain groups. 42 Facial recognition technology has shown increased error rates for certain racial and ethnic groups, which has raised racial profiling concerns. 43 • Article 21 (Protection of life and personal liberty) may be violated by discriminatory bail risk assessment decisions that are made unfairly. 44 Facial recognition technology used for surveillance poses a challenge to the right to privacy by discriminating. AIdriven decisions lack transparency and explainability, which may affect due process.

35 Ibid

36 Ibid

37 Use of AI in Supreme Court Case Management - Press Release ..., accessed on March 31, 2025,

https://pib.gov.in/PressReleasePage.aspx?PRID=2113224

38 Supra note 23

39 Ibid

40 Ibid

41 Supra note 16

42 Supra note 23

43 Supra note 16

44 Supra note 23

Algorithmic use in pre-trial detention, bail hearings, and sentencing has been argued to violate the presumption of innocence.45 V.

Global Case Studies: Examples of Prejudice in Artificial Intelligence within the Legal Sector Several international case studies illustrate the dangers of algorithmic bias to legal frameworks: • COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), USA: The US court's in-house risk assessment algorithm to determine whether a defendant is likely to reoffend has been found to be racially biased. Apparently, Black defendants were nearly twice as likely as white defendants to be flagged as higher risk but not re-offend. 46 On the other hand, white defendants who did re-offend were more likely to be incorrectly classified as low risk. Apparently, a 2024 Broward County study found that COMPAS lowered confinement rates for all groups but increased racial disparities in confinement.

47 • Algorithmic Tenant Screening Systems, USA: SafeRent Solutions, among other companies, has been sued on allegations that its algorithms systematically provide lower ratings for Black and Hispanic rental applicants compared to white applicants. 48 The algorithms have been accused of discriminating against voucher holders by failing to consider the value of their vouchers. • AI Sentencing in Virginia, USA: Research by Tulane University on over 50,000 drug, fraud, and larceny convictions in Virginia found that while AI-recommended sentencing recommendations led to shorter jail terms for low-risk offenders, judges disproportionately declined to offer non-incarceration alternatives to Black defendants when AI recommended probation. 49 This resulted in similar Black offenders receiving longer average jail terms compared to whites.

• Dutch Child Benefits Scandal, Netherlands: Although not a court AI system, the scandal involved an algorithm used by the Dutch tax authorities that incorrectly accused

45 Ibid

46 Algorithms Were Supposed to Reduce Bias in Criminal Justice—Do ..., accessed on March 31, 2025,

https://www.bu.edu/articles/2023/do-algorithms-reduce-bias-in-criminal-justice/

COMPAS

Recidivism Racial Bias - Kaggle, accessed on March 31, 2025, https://www.kaggle.com/datasets/danofer/compass

48 Ghosts in the Machine: How Past and Present Biases Haunt ..., accessed on March 31, 2025,

https://www.americanbar.org/groups/crsj/resources/human-rights/2024-june/how-past-present-biases-hauntalgorithmic-tenant-screening-systems/ 49 AI sentencing cut jail time for low-risk offenders, but study finds racial ..., accessed on March 31, 2025, https://news.tulane.edu/pr/ai-sentencing-cut-jail-time-low-risk-offenders-study-finds-racial-bias-persisted

thousands of families, including many of immigrant background, of child benefit fraud. 50 The case demonstrates how discriminatory algorithms can lead to serious injustices unless they are checked. • UK Post Office Horizon Scandal, UK: This was another case where an IT system whose defects resulted in the wrongful prosecution of various post office workers for theft and false accounting. 51 This reminds us of the risk of relying on computerized systems without proper precautions and human checks.

• Police Automated Facial Recognition, UK: In R (Bridges) v Chief Constable of South Wales Police EWCA Civ 1058, the UK Court of Appeal held that the police deployment of automated facial recognition technology was unlawful, in part because of the lack of evaluation of the possible gender and racist bias of the technology, in contravention of the Public Sector Equality Duty under the Equality Act 2010. 52 The above case studies illustrate the ability of artificial intelligence systems to amplify and even perpetuate existing societal biases in the legal profession, leading to discriminatory outcomes and human rights abuses. VI.

Strategies for Identifying and Reducing Algorithmic Bias in Legal Technology There are various methods of detecting and countering algorithmic bias in legal technology.14 Detection methods include mandatory algorithmic testing 53, the deployment of bias discovery tools and methods 54, continuous testing and monitoring of AI systems 55, testing training data sets for biases 56, testing AI models on diverse data sets 57, and the establishment of back-end feedback mechanisms. Fairness metrics, such as demographic parity and equal opportunity, are employed to quantify and examine fairness.58 Explainable AI (XAI) techniques are important

50 The Pitfalls of Using AI in UK Civil Litigation – Legal Lens, accessed on March 31, 2025,

https://legallens.org.uk/the-pitfalls-of-using-ai-in-uk-civil-litigation/

51 Ibid

52 Ibid

53 Bharat's eCourts Mission: Transforming Judiciary through Digital Innovation - Organiser, accessed on March 31, 2025, https://organiser.org/2024/12/22/270020/bharat/bharats-ecourts-mission-transforming-judiciarythrough-digital-innovation/

54 Supra note 16

55 Ibid

56 Ibid

57 Ibid

58 Supra note 16

to enhance transparency in AI decision-making.59 Mitigation strategies involve bias mitigation techniques at different stages of the machine learning pipeline 60, including pre-processing techniques like data balancing and re-sampling, in-processing techniques that enforce fairness constraints during training, and post-processing techniques that scale model outputs for improved fairness.61 Ensuring diversity in data and development teams 62, developing ethical frameworks 63, mandating transparency measures64, including human oversight 30, and continuous monitoring and AI model updating are also required.65 VII.

Comparative Legal Analysis of AI and Human Rights: India, US, UK, and EU India's AI law is yet in its infancy. While the current provisions of equality and nondiscrimination under the Constitution (Articles 14 and 15) and the right to privacy (Article 21) are applicable66, there is no direct law regarding algorithmic bias. 67 The Information Technology Act, 2000, and the Digital Personal Data Protection Act, 2023, address data privacy but not judicial AI bias comprehensively. 68 In contrast, the European Union (EU) has been more engaged with the EU AI Act, seeking to ensure that AI development supports human rights protections, such as the reduction of discriminatory biases. 69 The Act is risk-based, with high-risk uses toward law enforcement and judicial decision-making subject to more stringent regulation.

70 It allows for processing sensitive personal data for bias detection and correction under certain conditions, offsetting this against GDPR restrictions. 71

59 Ibid

60 Ibid

61 Ibid

62 Ibid

63 Supra note 53

64 Ibid

65 Supra note 16

66 Supra note 23

67 Supra note 15

68 Legal And Ethical Issues In Regulation Of Artificial Intelligence In India - Lawful Legal, accessed on March 31, 2025, https://lawfullegal.in/legal-and-ethical-issues-in-regulation-of-artificial-intelligence-in-india/

69 EU Grapples with Algorithmic Discrimination Under AI Act and ..., accessed on March 31, 2025,

https://babl.ai/eu-grapples-with-algorithmic-discrimination-under-ai-act-and-gdpr/ 70 European Parliament Adopts the World's First Comprehensive AI Law | Littler, accessed on March 31, 2025, https://www.littler.com/news-analysis/asap/european-parliament-adopts-worlds-first-comprehensive-ai-law

71 Supra note 69

The United States has no single overarching federal law that governs artificial intelligence. Regulation is patchy across federal and state schemes. 72 Despite executive orders and guidelines, the response has been far less centralized than that of the European Union. 73 Various state-specific legislations, such as the Colorado AI Act and the Illinois Supreme Court's AI Policy, are in the making. 74 Also, there has been extensive research and discussion in the United States on algorithmic bias, with special focus on the COMPAS algorithm75 and its applications in predictive policing. 76 The United Kingdom (UK) has taken a "pro-innovation" stance to AI regulation, encouraging a principles-based policy rather than hard law at this point.

77 Current regulators will be responsible for interpreting and applying five AI principles: safety, transparency, fairness, accountability, and contestability. 78 The UK government is drafting an AI Bill for high-end AI models, with the goal of making voluntary commitments legally enforceable. 79 The UK has also seen cases that acknowledge the dangers of AI bias, including the Bridges case involving facial recognition. 28 Table 2: Comparative Analysis of AI Regulation in Judiciary Jurisdiction Regulatory Key Features Addressing Algorithmic Approach Bias

Comprehensive, EU AI Act, GDPR Explicit focus on Risk-Based mitigating discriminatory biases

Fragmented, Various federal and Focus on specific Sector-Specific state initiatives, applications (e.g., guidelines

COMPAS),

no overarching law

Principles-Based, Five AI principles Emphasis on fairness, Pro-Innovation for regulators, case law on bias (e.g., upcoming AI Bill facial recognition) This comparative analysis reveals that India lags behind the EU in establishing a comprehensive legal framework to address algorithmic bias in the judiciary. While the US and UK are grappling with the issue through various initiatives and legal challenges, the EU's AI Act represents a more structured and rights-centric approach. VIII.

Evaluating the Sufficiency of the Existing Legal and Regulatory Framework in India to Address this Gravest Problem India's current legal and regulatory environment is overall insufficient to counter the imperative threat of algorithmic bias in the judiciary.80 The lack of AI-specific specialized legislation in India is a pertinent lacuna.81 Although enactments such as the Information Technology Act, 2000, and the Digital Personal Data Protection Act, 2023, are indirectly germane to the data privacy issue in AI systems, they specifically do not address algorithmic bias and its consequences on fundamental rights in the context of the judiciary.82 The Supreme Court AI Committee is also mainly concerned with the use of AI for translation and process efficiency,

80 Supra note 15

81 Ibid

82 Supra note 68

rather than specifically bias elimination.83 The e-Courts Project Phase III, as much as it hopes for digitalization, falls short of clear measures to eliminate algorithmic bias.84 The application of settled legal rules, for example, the rule of equality and non-discrimination, to the new problem of algorithmic bias is daunting.85 Uncertainty about basic legal notions such as "algorithmic bias" and "automated decision-making" adds to the difficulty of applying settled legal frameworks.86 The intrinsic complexity of some AI algorithms also makes it hard to detect and contest biased outcomes, which can interfere with individuals' access to justice and due process.87 In addition, the absence of mandatory algorithmic examination and transparency obligations further weakens the efficacy of the existing legal system.88 IX.

Recommendations to Promote Fairness and Accountability in AI-Based Justice To support greater equity and accountability in the use of artificial intelligence in the Indian judiciary, the following are proposed recommendations89: • Implement Specific AI Legislation: India requires a full legal regime specifically for AI in the judiciary, i. e. , definitions of algorithmic bias, fairness and non-discrimination requirements, and accountability. 90 It should learn from international models like the EU AI Act. 91 • Have an Independent Monitoring Body: An independent body or a mandate of the e- Courts initiative must be instituted to ensure that AI is being implemented and innovated ethically, for instance, conducting continuous bias audits.

92 • Implement Mandatory Algorithmic Audits: Regular, transparent, and compulsory checking of AI employed in the justice system to find and reduce bias is required. 93 The audit protocols and findings should be made available to the public to ensure maximum confidence.

83 Supra note 37

84 Supra note 1

85 Supra note 68

86 Ibid

87 Ibid

88 Supra note 68

89 Responsible AI for the Indian Justice System – A Strategy Paper, accessed on March 31, 2025,

https://vidhilegalpolicy.in/research/responsible-ai-for-the-indian-justice-system-a-strategy-paper/

90 Supra note 53

91 Legal Challenges of Artificial Intelligence in India's Cyber Law Framework - IJFMR, accessed on March 31, 2025, https://www.ijfmr.com/papers/2024/6/31347.pdf

92 Supra note 53

93 Ibid

•Establish and Enforce Ethical Guidelines: Creating and enforcing precise ethical guidelines for the design, development, and deployment of AI in the justice system, focusing on fairness, transparency, and accountability, must be done and enforced rigorously. 94 Such guidelines must also take into account the specific social and cultural context of India. 95 • Create Representative and Diverse Data: There has to be a move to ensure training data for AI systems is representative of the Indian population's diversity, actively combating historical and societal biases. 96 • Invest in Bias Detection and Mitigation Research: The government must invest in research and development of bias detection and mitigation techniques specific to the Indian context, such as caste, religion, and linguistic diversity.

97 • Maintain Human Supervision: Human supervision and the potential for human examination need to be preserved for all significant AI-driven decisions in the judicial process to prevent biased results and provide accountability. 98 AI should support, rather than substitute, human judgment. 99 • Enhance Data Protection and Privacy Controls: Effective data protection and privacy controls must be provided for the protection of sensitive legal data used in AI systems, as per the Digital Personal Data Protection Act, 2023. 100 • Enhance Public Awareness and Education: Public education and awareness regarding the application of AI in the judiciary and the hazards of algorithmic bias are vital to facilitate well-informed engagement and trustworthiness.

101 • Foster Multi-Stakeholder Collaboration: An inter-disciplinary collaboration among legal professionals, experts in technology, policymakers, and civil society is needed to meet this challenge fully and ensure that AI serves the interest of justice for everyone. 102

94 Ibid

95 Supra note 9

96 Ibid

97 Ibid

98 Supra note 91

99 impact of artificial intelligence and automation on legal practice and the indian judicial system - M&J Services, accessed on March 31, 2025, https://mandjservice.com/blogs/blog/impact-of-artificial-intelligence-andautomation-on-legal-practice-and-the-indian-judicial-system

100 Supra note 68

101 Supra note 53

102 Responsible AI for the Indian Justice System – A Strategy Paper, accessed on March 31, 2025,

https://vidhilegalpolicy.in/research/responsible-ai-for-the-indian-justice-system-a-strategy-paper/

Conclusion: Navigating the Algorithmic Frontier of Indian Justice The use of artificial intelligence in the Indian judiciary presents a major chance to enhance efficiency and accessibility. However, the significant risks associated with algorithmic bias cannot be overlooked. Such biases pose a direct threat to the fundamental rights protected by the Indian Constitution and can undermine the principles of fair justice. Addressing this problem necessitates a proactive and multi-faceted approach involving legislative changes, efficient oversight processes, strict technical safeguards, and a commitment to ethical principles. By fostering transparency, guaranteeing accountability, and promoting cooperation, India can develop the algorithmic aspect of its judiciary while upholding the constitutional ideals of equality, non-discrimination, and freedom for all citizens.

The path forward calls for an even-handed approach that promotes innovation while highlighting the protection of human rights and the fundamental principles of a fair legal system.