Artificial Intelligence And Criminal Justice - Opportunities And Ethical Concerns
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Category: Criminal Law
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ARTIFICIAL INTELLIGENCE AND CRIMINAL JUSTICE:
OPPORTUNITIES AND ETHICAL CONCERNS Shreya Goyal, Advocate at KK Malhotra & Co, Karkardooma District Court, Delhi, India
ABSTRACT
Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, profoundly influencing various sectors including healthcare, education, finance, and governance. The criminal justice system has also witnessed increasing integration of AI-based technologies in policing, investigation, adjudication, sentencing, and corrections. AI applications such as predictive policing, facial recognition systems, risk assessment algorithms, digital forensic tools, and automated surveillance have the potential to improve efficiency, accuracy, and resource allocation. However, the deployment of AI in criminal justice raises significant ethical and legal concerns relating to privacy, discrimination, accountability, transparency, due process, and human rights.
This paper examines the opportunities offered by artificial intelligence in enhancing criminal justice administration while critically analysing the ethical challenges associated with its use. The study adopts a socio-legal approach and explores international experiences along with the Indian legal framework. It argues that although AI can serve as an important tool in improving crime prevention and judicial efficiency, excessive reliance on algorithmic decision-making may undermine fundamental constitutional values and principles of natural justice. The paper concludes that the development of robust regulatory frameworks, human oversight mechanisms, and ethical guidelines is essential to ensure that AI serves as an instrument of justice rather than becoming a source of injustice.
Keywords: Artificial Intelligence, Criminal Justice, Predictive Policing, Algorithmic Bias, Ethics, Human Rights, India.
Introduction
Technological innovation has historically played a vital role in shaping legal institutions and mechanisms of governance. The emergence of artificial intelligence represents a paradigm shift that has transformed traditional approaches to criminal justice administration. Artificial intelligence refers to the capability of machines to perform tasks that typically require human intelligence, including learning, reasoning, problem-solving, and decision-making. The criminal justice system traditionally relies upon human discretion exercised by police officers, prosecutors, judges, and correctional authorities. However, increasing crime rates, judicial delays, and the growing complexity of criminal investigations have encouraged governments worldwide to adopt AI-based solutions.
Predictive policing systems, facial recognition technology, automated surveillance mechanisms, and risk assessment tools are increasingly being utilized to enhance efficiency and effectiveness. Despite these advantages, the integration of AI into criminal justice has generated intense debate. Concerns have been raised regarding algorithmic discrimination, lack of transparency, privacy violations, and threats to constitutional guarantees. Scholars argue that AI systems often replicate existing social biases embedded in datasets and may disproportionately affect vulnerable communities. Consequently, the use of AI in criminal justice necessitates careful examination from legal, ethical, and socio-political perspectives.
The present paper seeks to analyse the opportunities and ethical concerns associated with the use of artificial intelligence in criminal justice, with particular emphasis on India and comparative international experiences. Literature Review The growing integration of artificial intelligence into criminal justice has attracted significant scholarly attention. Stuart Russell and Peter Norvig have defined artificial intelligence as the science of creating intelligent machines capable of performing tasks that normally require human intelligence. 1 Their work provides the conceptual foundation for understanding the role of AI in modern governance and legal systems. 1 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (4th edn. , Pearson 2021).
Andrew Guthrie Ferguson, in The Rise of Big Data Policing, examines the use of predictive analytics in law enforcement and argues that technological innovations have fundamentally transformed traditional methods of policing. 2 He observes that predictive policing has the potential to improve efficiency and crime prevention, but simultaneously raises concerns regarding privacy and discriminatory practices. Bernard Harcourt, in Against Prediction: Profiling, Policing and Punishing in an Actuarial Age, critically analyses predictive technologies and warns that excessive reliance on statistical models may reinforce existing social inequalities and perpetuate biases against vulnerable groups. 3 His work highlights the tension between efficiency and fairness in criminal justice administration.
Frank Pasquale, in The Black Box Society, emphasizes the lack of transparency in algorithmic systems and argues that opaque decision-making processes threaten accountability and democratic values. 4 He contends that the inability to understand how algorithms reach conclusions undermines due process and public trust. Danielle Keats Citron and Frank Pasquale, in their article "The Scored Society: Due Process for Automated Predictions", argue that automated decision-making systems have significant implications for constitutional rights and procedural fairness. 5 They advocate for greater transparency and safeguards to prevent arbitrary outcomes. Kate Crawford, in Atlas of AI, highlights the social and ethical consequences of artificial intelligence and demonstrates how technological systems often reproduce existing inequalities embedded within society.
6 Similarly, Andrew Selbst argues that algorithmic systems may produce discriminatory outcomes due to biases inherent in historical data. 7 International organizations have also contributed to the discourse on ethical AI. The European Commission's Ethics Guidelines for Trustworthy Artificial Intelligence emphasize
2 Andrew Guthrie Ferguson, The Rise of Big Data Policing (New York University Press 2017).
3 Bernard E. Harcourt, Against Prediction
Profiling, Policing and Punishing in an Actuarial Age (University of Chicago Press 2007). 4 Frank Pasquale, The Black Box Society: The Secret Algorithms that Control Money and Information (Harvard University Press 2015). 5 Danielle Keats Citron and Frank Pasquale, "The Scored Society: Due Process for Automated Predictions" (2014) 89 Washington Law Review 1.
6 Kate Crawford, Atlas of AI (Yale University Press 2021).
7 Andrew D. Selbst, "Disparate Impact in Big Data Policing" (2017) 52 Georgia Law Review 109
transparency, accountability, fairness, and human oversight.8 UNESCO's Recommendation on the Ethics of Artificial Intelligence advocates a human rights-based approach to AI governance.9 Although existing scholarship has extensively examined algorithmic bias, privacy concerns, and ethical governance, limited attention has been paid to the implications of AI in criminal justice from the perspective of developing countries, particularly India. Furthermore, there remains a need to reconcile technological innovation with constitutional principles and human rights protections. The present study seeks to bridge this gap by analysing the opportunities and ethical concerns associated with AI in criminal justice with special reference to India. Research Questions The present study seeks to answer the following questions:
1. What is the role of artificial intelligence in the administration of criminal justice?
2. How can AI contribute to improving efficiency, accuracy, and crime prevention within
the criminal justice system?
3. What ethical and constitutional concerns arise from the use of AI-based technologies
in criminal justice?
4. To what extent do algorithmic bias and lack of transparency affect the principles of
fairness and due process?
5. Whether the existing legal framework in India is adequate to regulate the use of
artificial intelligence in criminal justice? 6. What measures can be adopted to ensure ethical, accountable, and human rightsoriented use of artificial intelligence? Hypotheses H1- Artificial intelligence enhances efficiency and effectiveness in criminal justice
8 European Commission, Ethics Guidelines for Trustworthy Artificial Intelligence (2019).
9 UNESCO, Recommendation on the Ethics of Artificial Intelligence (2021).
administration through improved investigation, predictive analysis, and resource allocation.10 H2- The use of AI-based technologies in criminal justice creates significant ethical concerns relating to privacy, transparency, accountability, and discrimination.11 H3- Algorithmic decision-making systems have the potential to perpetuate social and racial biases embedded within historical data.12 H4- The existing legal framework in India is insufficient to comprehensively regulate the application of artificial intelligence in criminal justice.13 H5- Human oversight, transparency, and regulatory safeguards are necessary to ensure that artificial intelligence remains consistent with constitutional values and human rights principles.14 Objectives of the Study
1. To examine the concept and scope of artificial intelligence in criminal justice.
2. To analyse the opportunities provided by AI in law enforcement and judicial
administration.
3. To identify ethical and legal challenges associated with AI-based decision-making.
4. To evaluate international experiences relating to AI regulation.
5. To study the Indian legal framework governing AI in criminal justice.
6. To suggest measures for ensuring ethical and responsible use of AI.
Research Methodology
The study adopts a doctrinal and socio-legal methodology. It relies primarily on secondary
10 OECD, Artificial Intelligence in Society (OECD Publishing 2019).
11 Frank Pasquale, The Black Box Society (Harvard University Press 2015).
12 Julia Angwin et al., "Machine Bias", ProPublica (23 May 2016).
13 NITI Aayog, National Strategy for Artificial Intelligence #AIForAll (2018).
14 UNESCO, Recommendation on the Ethics of Artificial Intelligence (2021).
sources including books, journal articles, reports, statutes, judicial decisions, and policy documents. Comparative analysis has been undertaken with reference to the experiences of the United States, the United Kingdom, China, and the European Union. The study also examines constitutional principles and legal developments in India concerning artificial intelligence and criminal justice. Understanding Artificial Intelligence Artificial intelligence refers to computer systems capable of performing functions that normally require human intelligence. AI systems rely on machine learning, neural networks, natural language processing, and data analytics to identify patterns and make predictions.
AI may be classified into: o Narrow AI- Narrow AI is designed to perform specific tasks such as facial recognition or language translation o General AI- General AI refers to systems capable of performing diverse intellectual functions similar to human beings. Such systems are still largely theoretical. o Machine Learning- Machine learning involves algorithms that improve performance through experience and data analysis. o Deep Learning- Deep learning employs neural networks to process complex datasets and identify intricate patterns. These technologies have increasingly become integrated into criminal justice systems worldwide.
1.1 Applications of AI in Criminal Justice
o Predictive Policing- Predictive policing uses algorithms to identify areas and individuals associated with higher risks of criminal activity. Law enforcement agencies analyse historical crime data to allocate resources efficiently. Programs such as PredPol in the United States have been used to forecast crime hotspots.15
15 Andrew Guthrie Ferguson, The Rise of Big Data Policing (New York University Press 2017).
o Facial Recognition Technology- Facial recognition systems compare images captured through surveillance cameras with existing databases to identify suspects. Countries such as China and the United States extensively use facial recognition technologies. o Digital Forensics- AI facilitates the analysis of large volumes of digital evidence including emails, call records, and social media communications. Automated systems assist investigators in detecting cybercrime and financial fraud. o Risk Assessment Algorithms- Algorithms evaluate the likelihood of recidivism and assist judges in bail and sentencing decisions. The COMPAS system in the United States represents one such example. o Surveillance and Monitoring- AI-enabled surveillance systems monitor public spaces and detect suspicious behaviour. Smart cameras and behavioural analytics enhance security and crime prevention.
o Judicial Assistance- AI tools support judges and lawyers through legal research, document analysis, and case management systems. Several countries have experimented with automated adjudication mechanisms for minor disputes.
1.2 Opportunities Offered by AI in Criminal Justice
o Enhancing Efficiency- Traditional criminal justice systems often suffer from delays and resource constraints. AI technologies can process large amounts of information rapidly, thereby increasing efficiency. Automated case management systems reduce administrative burdens and improve productivity. o Crime Prevention- Predictive analytics enable law enforcement agencies to anticipate criminal activities and adopt preventive measures. Early intervention strategies contribute to public safety. o Improved Investigation- AI-based forensic tools assist investigators in analysing digital evidence and identifying patterns that may otherwise remain undetected. This significantly enhances investigative accuracy. o Reduction of Human Error- Human decision-making may be influenced by fatigue, emotions, or cognitive biases. AI systems offer consistency and objectivity in analysing information.
16 o Better Allocation of Resources- Predictive models help authorities deploy police personnel strategically and optimize resource allocation. o Speedier Justice- Judicial delays constitute a major challenge in many countries, including India. AI-enabled technologies can streamline legal research, case management, and documentation processes. o Combating Cybercrime- Cybercrime investigations require sophisticated technological capabilities. AI assists in detecting malware, phishing attacks, and financial fraud.
1.3 AI and Criminal Investigation
Modern criminal investigations increasingly involve digital evidence. AI enables authorities to process extensive datasets efficiently. o Pattern Recognition- AI algorithms identify behavioural patterns and establish links between suspects and criminal networks. o Voice Recognition- Speech analysis technologies facilitate identification of individuals involved in criminal activities. o Image Processing- AI systems analyse photographs and surveillance footage to identify suspects and reconstruct events. o DNA Analysis- Machine learning techniques improve DNA profiling and forensic investigations. These advancements have significantly strengthened investigative capabilities.
1.4 Ethical Concerns Associated with AI
Despite numerous advantages, AI raises serious ethical concerns.
16 OECD, Artificial Intelligence in Society (OECD Publishing 2019).
o Algorithmic Bias- AI systems learn from historical data. If the data contains racial, gender, or socioeconomic biases, the algorithm may perpetuate discriminatory outcomes. Studies have demonstrated that some risk assessment tools disproportionately classify minority populations as high-risk offenders. o Lack of Transparency- Many AI systems function as "black boxes," making it difficult to understand how decisions are reached. Opacity undermines accountability and public trust. o Privacy Violations- Mass surveillance and facial recognition technologies raise concerns regarding privacy rights. Continuous monitoring may result in excessive state intrusion. o Due Process Concerns- Individuals affected by algorithmic decisions may not have adequate opportunities to challenge such decisions. This threatens principles of natural justice.
o Accountability Problems- Determining responsibility for erroneous decisions generated by AI systems poses significant challenges. Questions arise regarding whether liability should be imposed upon programmers, governments, or institutions. o Dehumanization of Justice- Justice involves empathy, morality, and contextual understanding. Over-reliance on automated systems may undermine human values and ethical reasoning. o Algorithmic Bias and Discrimination- Algorithmic bias represents one of the most significant criticisms of AI. Bias may arise from:
1. Biased datasets.
2. Historical inequalities.
3. Faulty design.
4. Incomplete information.
The COMPAS17 algorithm used in the United States attracted criticism after reports indicated racial disparities in risk assessments. Similarly, facial recognition systems have exhibited higher error rates for women and minority populations. Such biases threaten the constitutional principles of equality and non-discrimination.
1.5 Privacy and Surveillance Concerns
The widespread deployment of surveillance technologies has intensified debates concerning privacy rights. AI-enabled cameras, biometric databases, and behavioural analytics facilitate extensive monitoring. Critics argue that excessive surveillance creates a "surveillance state" and undermines civil liberties. The right to privacy has been recognized as a fundamental right by the Supreme Court of India in Justice K.S. Puttaswamy v. Union of India (2017).18 Therefore, any use of AI-based surveillance must satisfy constitutional requirements of legality, necessity, and proportionality.
1.6 Theoretical Framework
Law, Technology and Criminal Justice The relationship between law and technology has historically been characterized by both cooperation and tension. Technological developments often provide new tools for governance and law enforcement, while simultaneously creating challenges for legal regulation and protection of individual rights. Artificial intelligence represents the latest stage in this evolving relationship. Unlike previous technologies, AI possesses the ability to learn from data, identify patterns, and generate predictions, thereby influencing decision-making processes that were traditionally reserved for human actors. From a legal perspective, the integration of AI into criminal justice raises important questions regarding the nature of justice itself. Traditional theories of criminal justice emphasize fairness, accountability, proportionality, and individual responsibility.
Decisions affecting liberty and punishment have historically been entrusted to human institutions because such decisions require moral judgment and contextual understanding. The theory of procedural justice developed by Tom Tyler emphasizes that public trust in legal institutions depends not only upon outcomes but also upon the fairness of decision-making
17 Loomis v. Wisconsin, 881 N.W.2d 749 (Wis. 2016).
18 Justice K.S. Puttaswamy (Retd.) v. Union of India, (2017) 10 SCC 1.
processes.19 When AI systems operate through opaque algorithms, individuals may perceive decisions as unfair even if the outcomes appear objectively reasonable. Thus, procedural fairness becomes an essential consideration in evaluating AI-based criminal justice systems. Similarly, Ronald Dworkin's rights-based theory argues that individual rights must act as constraints upon governmental power.20Consequently, the adoption of AI technologies must remain subject to constitutional protections and human rights principles. Efficiency alone cannot justify intrusions into privacy, equality, or due process.
1.7 Artificial Intelligence and Sentencing Decisions
One of the most controversial applications of artificial intelligence in criminal justice relates to sentencing decisions. Courts in several jurisdictions have experimented with risk assessment tools designed to estimate the probability of reoffending. Risk assessment algorithms analyse various factors, including criminal history, age, employment status, and social background. Based on these variables, the system generates a risk score that may influence judicial decisions regarding bail, probation, or sentencing. Proponents argue that such systems improve consistency and reduce subjective biases. Traditional sentencing often varies depending upon individual judges, resulting in disparities among similarly situated offenders. AI-based systems promise greater uniformity and predictability.
21 However, critics argue that algorithmic sentencing undermines the principle of individualized justice. Criminal punishment should reflect the unique circumstances of each offender rather than statistical probabilities derived from group data. Moreover, if the underlying data reflects historical discrimination, the resulting risk scores may reinforce social inequalities. The controversy surrounding the COMPAS system in the United States illustrates these concerns. Studies suggested that the algorithm disproportionately classified minority defendants as high-risk offenders. 22 Although supporters disputed these findings, the debate
19 Tom R. Tyler, Why People Obey the Law (Princeton University Press 2006).
20 Ronald Dworkin, Taking Rights Seriously (Harvard University Press 1977).
21 Sonja B. Starr, "Evidence-Based Sentencing and the Scientific Rationalization of Discrimination" (2014) 66 Stanford Law Review 803.
22 Julia Angwin et al., "Machine Bias", ProPublica (23 May 2016).
highlighted broader questions regarding transparency, fairness, and accountability. In the Indian context, sentencing remains largely dependent upon judicial discretion. Nevertheless, increasing digitization of courts and growing interest in AI-based decisionsupport systems indicate that similar debates may emerge in the future.
1.8 AI and Correctional Administration
The application of artificial intelligence extends beyond policing and adjudication to correctional institutions. Prisons worldwide increasingly utilize AI technologies to improve security, inmate management, and rehabilitation programs. AI-powered surveillance systems can monitor inmate behaviour and identify potential security threats. Predictive models may assist prison administrators in allocating resources and preventing incidents of violence. Such technologies contribute to safer correctional environments and more efficient prison management. 23 Artificial intelligence may also support rehabilitation efforts. Data analytics can identify factors associated with recidivism and assist authorities in designing targeted intervention programs. Educational and vocational training initiatives may be customized according to individual needs, thereby improving reintegration outcomes.
However, concerns arise regarding excessive monitoring and the potential misuse of personal information. Continuous surveillance may infringe upon human dignity and privacy. Furthermore, predictive models used within correctional settings may stigmatize certain individuals by categorizing them as high-risk offenders based on historical patterns rather than present conduct. The challenge lies in ensuring that correctional technologies support rehabilitation while respecting fundamental human rights.
1.9 Comparative Analysis
AI Governance Models Different jurisdictions have adopted varying approaches to regulating artificial intelligence.
23 United Nations Office on Drugs and Crime, Artificial Intelligence and Criminal Justice (2021).
o European Union- The European Union has emerged as a global leader in AI regulation through its risk-based approach. The EU Artificial Intelligence Act classifies AI systems according to their level of risk and imposes strict obligations on high-risk applications. 24 Systems used in law enforcement and criminal justice are generally categorized as high-risk due to their potential impact on fundamental rights. Developers must ensure transparency, accountability, and human oversight. o United States- The United States has adopted a comparatively decentralized approach. Regulatory efforts are often sector-specific and rely upon existing constitutional safeguards. While innovation remains a priority, concerns regarding privacy and discrimination continue to shape policy discussions. 25 o China- China has invested heavily in AI-driven governance and surveillance infrastructure.
AI technologies play a significant role in public security and social management. While these initiatives have enhanced state capacity, critics argue that extensive surveillance may undermine civil liberties. 26 o India- India currently lacks comprehensive legislation specifically regulating AI. Existing legal protections derive from constitutional provisions, information technology laws, and data protection frameworks. Consequently, regulatory uncertainty remains a significant challenge.
1.10 Challenges
in Developing Countries The implications of AI in criminal justice differ considerably between developed and developing nations. Countries such as India face unique institutional, technological, and socioeconomic challenges. First, data quality remains a significant concern. AI systems depend upon accurate and representative datasets. In many developing countries, criminal justice records may be incomplete, inconsistent, or affected by reporting biases. Consequently, algorithmic predictions may be unreliable.
24 European Parliament, Artificial Intelligence Act (2024).
25 National Institute of Standards and Technology, AI Risk Management Framework (2023).
26 Rogier Creemers, "China's Emerging Data Protection Framework" (2022) 14 Journal of Cyber Policy 1.
Second, technological infrastructure varies substantially across regions. Rural areas often lack the resources necessary for effective implementation of advanced technologies. Unequal access may exacerbate existing disparities within the justice system. Third, digital literacy among law enforcement personnel and judicial officers remains limited. Effective utilization of AI requires specialized training and technical expertise. Fourth, concerns regarding accountability become particularly significant in jurisdictions where institutional oversight mechanisms are weak. Without robust safeguards, AI technologies may facilitate arbitrary decision-making rather than promoting justice. These challenges underscore the need for context-specific approaches to AI governance rather than simply adopting models developed in other jurisdictions.
2.0 Future Prospects of AI in Criminal Justice
Artificial intelligence is expected to play an increasingly important role in criminal justice systems worldwide. Future developments may include advanced forensic technologies, intelligent legal research systems, automated evidence analysis, and enhanced cybercrime investigation tools. Generative AI technologies may assist legal professionals in drafting documents, summarizing case records, and conducting legal research. Courts may increasingly rely upon AI-assisted case management systems to reduce delays and improve efficiency. Nevertheless, future innovation must be accompanied by ethical safeguards. The objective should not be to replace human decision-makers but rather to augment their capabilities. Human judgment remains indispensable in matters involving liberty, punishment, and constitutional rights.
The future of criminal justice therefore depends upon achieving an appropriate balance between technological innovation and legal accountability. Responsible governance will determine whether AI becomes a tool for advancing justice or a mechanism for reinforcing existing inequalities.
2.1 AI and Criminal Justice in India
India has gradually embraced artificial intelligence in governance and law enforcement. o ICJS Project- The Interoperable Criminal Justice System integrates police, prisons, forensic laboratories, prosecution agencies, and courts. o CCTNS- Crime and Criminal Tracking Network and Systems facilitate efficient information sharing. o Facial Recognition Systems- Various states have experimented with facial recognition technologies.
2.2 AI-Based Translation Tools
The Supreme Court of India has promoted AI-enabled translation and legal research mechanisms. o SUVAAS- The Supreme Court Vidhik Anuvaad Software assists in translating judicial documents.
2.3 Constitutional Concerns in India
o Article 14- Algorithmic discrimination may violate the principle of equality. o Article 19- Surveillance mechanisms may affect freedom of speech and expression. o Article 21- Privacy and personal liberty may be compromised by intrusive AI technologies. Thus, constitutional safeguards assume central importance.
2.4 Human Rights Implications
AI technologies affect several internationally recognized human rights:
1. Right to privacy.
2. Right to equality.
3. Right to fair trial.
4. Right against arbitrary interference.
5. Human dignity.
International organizations including the United Nations and UNESCO have emphasized ethical AI principles.
2.5 Need for Ethical Governance
Responsible AI requires: o Transparency- Algorithms should be explainable and understandable. o Accountability- Clear mechanisms should exist for assigning responsibility. o Human Oversight- Humans should retain ultimate decision-making authority. o Fairness- Systems should avoid discriminatory outcomes. o Data Protection- Personal information must be adequately safeguarded. o Proportionality- State intervention should be necessary and proportionate.
2.6 Recommendations
o Enact Comprehensive AI Legislation- India should formulate specific legislation governing AI applications. o Establish Independent Regulatory Bodies- Oversight mechanisms should monitor AI systems. o Mandatory Algorithmic Audits- Regular audits should identify biases and inaccuracies. o Human-in-the-Loop Principle- Final decisions affecting liberty should remain subject to human review. o Strengthen Data Protection Framework- Robust safeguards are necessary to protect privacy. o Promote Transparency- Authorities should disclose the functioning of AI systems. o Capacity Building- Judges, lawyers, and police personnel should receive training concerning AI technologies. o International Cooperation- Global collaboration is essential to address transnational challenges.
Conclusion Artificial intelligence represents a transformative force capable of revolutionizing criminal justice administration. Its applications in policing, investigation, adjudication, and corrections offer significant opportunities for enhancing efficiency, accuracy, and crime prevention. However, these benefits are accompanied by profound ethical and legal concerns. Algorithmic bias, privacy violations, lack of transparency, and accountability deficits threaten constitutional values and human rights. Excessive dependence upon automated systems risks undermining fairness and due process. Therefore, AI should function as an assistive mechanism rather than a substitute for human judgment.
The future of criminal justice lies not in replacing human decision-makers with machines but in creating a balanced framework that combines technological innovation with constitutional morality and ethical principles. Appropriate regulation, transparency, and human oversight are indispensable to ensuring that artificial intelligence strengthens rather than weakens the administration of justice. As societies increasingly embrace digital technologies, the challenge is not whether AI should be used in criminal justice, but how it should be used in a manner that preserves liberty, equality, and human dignity.
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