We are living in a world where the use of various technological tools has become almost omnipresent and all institutions including judiciary have embraced it in a manner which was never seen before. History is replete with examples that most of the scientific inventions and advancements have brought ease, satisfaction and even happiness to mankind but at the same time it also needs to be admitted that as and when any new scientific invention or advancement came, it came with inhibitions, unknown fear and threat of automation leading to resistance. However, a reading of the history of sciences would show that if change was really beautiful, it was welcomed and happily embraced sooner or later.
Undoubtedly, the use of artificial intelligence and machine learning have brought immense beneficial implications for judiciary in many parts of the world. In fact, the computational era is bringing changes to this system. The increasing use of artificial intelligence and machine learning will place judges in a better position to decide matters of fact, better equipped to make predictions about the consequences of their decisions, improving the quality of judicial decision- making.
However, adopting computation methods into judiciary will create various challenges, like concerns about fairness and propriety in decision-making, but the methods themselves will help resolve some of those concerns. The result, of course, will be that the judges will be able to do legal research faster. There will be more certainty in how most cases will be decided on both the facts and the law, leading to higher number of disposals (Abdi Aidid & Benjamin Alarie, Legal Singularity, 2023).
Many countries across the world are consistently using AI in their judicial system. But we are yet to make use of AI in Indian judicial system in a way it is used in United States and other countries as presently in India it is used for translation of judgements and disposal of traffic challans. When we look towards the use of AI in sentencing process, we find that it is used in sentencing decisions through the application of algorithms and predictive analytics. These systems analyze vast amounts of data, including criminal histories, demographic information, and other factors, to predict the likelihood of an individual re-offending or the severity of their crime. AI systems can identify patterns and trends that humans may overlook, providing judges with additional information to be used in their decisions. However, there are concerns about bias and fairness in AI sentencing, as algorithms may reflect and perpetuate existing inequalities in the criminal justice system. Virtual data streams and algorithms are visible, depicting the influence of AI on legal decisions. The setting is a blend of traditional and modern elements, highlighting the intersection of law and technology.
Let us now turn our attention to some of the specific cases in United States relating to the use of AI software in sentencing process. The first reference may be made to a specific case decided by the Supreme Court of Wisconsin (State of Wisconsin v. Eric L. Loomis, 2015 AP 157-CR) where the use of Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), an AI software, influenced a sentencing decision. The software predicted a high risk of reoffending for a defendant, which the judge considered in determining the length of the sentence. This case sparked controversy as critics raised concerns about the fairness and accuracy of using AI algorithms in sentencing decisions. The debate highlighted the ethical implications of relying on AI in the criminal justice system.
In another instance, the state of Utah implemented the Risk and Needs Assessment Tool (RANAT) to assist judges in sentencing decisions. This AI system analyzes factors such as criminal history, substance abuse, and employment status to predict the likelihood of reoffending. Judges in U.S., use these risk assessments to tailor sentencing and rehabilitation plans for individuals, aiming to reduce recidivism rates and promote successful reintegration into society.
Similarly, in Pennsylvania, COMPAS tool is utilized to assess the risk levels of individuals awaiting trial or sentencing. By analyzing various data points, including criminal history and social factors, COMPAS generates risk scores that help judges make informed decisions about bail, probation, and other sentencing options. In California, the Public Safety Assessment (PSA) tool uses AI to evaluate factors like prior arrests, age, and offense history to predict the likelihood of future criminal activity. Judges use these risk assessments to determine appropriate conditions for pre-trial release and sentencing, aiming to improve fairness and efficiency in the justice system.
These examples demonstrate how AI technologies are being increasingly integrated into sentencing decisions to provide judges with data-driven insights and improve the effectiveness of criminal justice outcomes.
Judges can take several measures to ensure that AI algorithms used in sentencing decisions are unbiased and fair. They should be cautious of any potential biases in the training data used to develop the AI algorithms. By examining the sources and quality of the data, judges can identify and address any inherent biases that may impact the algorithm’s results. It is essential for judges to consider the ethical implications of using AI in sentencing decisions and to prioritize fairness and justice in their use of technology.
Furthermore, judges can incorporate human oversight and discretion in the decision-making process to complement the information provided by AI algorithms. By balancing the insights of AI technology with their expertise and judgment, judges can ensure that sentencing decisions are informed, fair, and in line with legal standards. Ultimately, judges play a crucial role in overseeing the use of AI in sentencing decisions, advocating for transparency, accountability, and fairness to uphold the principles of justice in the criminal justice system. As a suggestive measure, judges can address potential biases in AI algorithms used for sentencing by implementing the following strategies:
1. Regular Bias Testing: Judges can conduct regular bias testing on the AI algorithms to identify any disparities in outcomes based on factors such as race, gender, or socio-economic status. By analyzing the impact of these variables on sentencing decisions, judges can take corrective measures to mitigate bias.
2. Data Transparency: Judges can request transparency from developers regarding the data sources and criteria used to train the AI algorithms. Understanding the data inputs and processes can help judges assess the potential for bias in the algorithms and make informed decisions about their use.
3. Diverse Stakeholder Engagement: Judges can involve a diverse group of stakeholders, including legal experts, ethicists, and community representatives, in discussions about the use of AI in sentencing. By considering diverse perspectives, judges can identify and address potential biases in the algorithms more effectively.
4. Bias Mitigation Strategies: Judges can work with AI developers to implement bias mitigation strategies, such as reweighing data inputs or adjusting algorithms to reduce disparities in sentencing outcomes. By actively addressing bias in the development and deployment of AI algorithms, judges can promote fair and equitable sentencing practices.
5. Ethical Guidelines: Judges can establish and adhere to ethical guidelines for the use of AI in sentencing decisions. By setting clear standards for fairness, transparency, and accountability, judges can ensure that AI algorithms align with legal and ethical principles in the criminal justice system.
6. Continued Monitoring: Judges should continuously monitor the use of AI algorithms in sentencing decisions to assess their impact on fairness and equity. By tracking outcomes and evaluating the effectiveness of bias mitigation measures, judges can proactively address any issues that arise and uphold the integrity of the justice system.
To conclude, it may be said that the AI algorithms may be very useful in deciding quantum of sentence in criminal cases but it should be done only after taking all possible precautionary measures lest fairness, transparency and justice will become a casualty. In short, by adopting the above mentioned strategies and approaches, judges can address potential biases in AI algorithms used for sentencing and promote fairness, transparency, and justice in the criminal justice system.
Article By: Dr. Humayun Rasheed Khan
Professor, National Judicial Academy India, Bhopal
