Executive Summary

Post-conviction analysis is the process of reviewing past cases and evidence to identify and correct wrongful convictions, which are a serious and pervasive problem in the criminal justice system. Wrongful convictions affect thousands of innocent people and undermine public confidence and legitimacy. AI driven post-conviction analysis can enhance public trust in the justice system, reduce the costs and harms of incarceration, prevent future miscarriages of justice and train large language models. However, post-conviction analysis also faces many challenges and barriers, such as lack of resources, data, standards, transparency, cooperation, incentives, and public awareness.

AI has the potential to address some of these challenges and enhance the opportunities of post-conviction analysis, by providing new tools and methods for collecting, analyzing, and presenting data and evidence. AI is a broad term that refers to the use of computer systems and algorithms to perform tasks that normally require human intelligence, such as reasoning, learning, decision making, and problem solving. AI can be applied to various aspects and stages of post-conviction analysis, such as identifying and prioritizing cases, extracting and organizing data and evidence, analyzing and evaluating data and evidence, and presenting and communicating the results and recommendations. AI can offer several advantages and benefits for post-conviction analysis, such as increasing the efficiency, accuracy, transparency, cooperation, incentives, and public awareness of the process.

However, AI also faces significant challenges and limitations, such as data quality and availability, ethical and legal issues, and social and organizational resistance, which need to be addressed and overcome. Therefore, there is a need for a balanced and nuanced approach to using AI for post-conviction analysis, that recognizes and addresses the challenges and limitations, while also leveraging and enhancing the opportunities and benefits. This approach should be based on the principles and guidelines of data quality and availability, ethical and legal issues, and social and organizational resistance, and should be implemented through the recommendations for policy and practice, that facilitate and foster the use of AI for post-conviction analysis.

One of the key recommendations for policy and practice is that police departments should lead the charge in post-conviction analysis, as a way to calibrate algorithms, provide transparency, and establish trust with the community in the capabilities of AI and the police. By initiating and supporting post-conviction analysis, police departments can demonstrate their commitment to justice, accountability, and innovation, and can revolutionize their relationships with the public and marginalized communities, who are most affected by wrongful convictions and over-policing.

This white paper aims to provide a comprehensive and coherent overview and analysis of the current state, the main challenges and opportunities, and the recommendations for policy and practice, of using AI for post-conviction analysis. This white paper also aims to stimulate and inform further research and action on this important and emerging topic, and to contribute to the advancement and improvement of post-conviction analysis and the criminal justice system.

Introduction

Wrongful convictions are a serious and pervasive problem in the criminal justice system, affecting thousands of innocent people and undermining public confidence and legitimacy. According to the National Registry of Exonerations, there have been more than 2,800 exonerations in the United States since 1989, involving more than 25,000 years of wrongful imprisonment[1]. However, these numbers are likely to be an underestimate, as many wrongful convictions remain undetected and uncorrected.

Post-conviction analysis is a crucial mechanism for identifying and rectifying wrongful convictions, as well as for learning from past errors and preventing future ones[2][3]. Post-conviction analysis involves reviewing past cases and evidence, such as DNA, fingerprints, eyewitness testimony, and forensic science, to determine whether the original verdict was based on reliable and valid information[4][5][6][7][8]. Post-conviction analysis can be initiated by various actors, such as defense attorneys, prosecutors, judges, innocence projects, or the convicted individuals themselves[9][10].

Post-conviction analysis can have multiple benefits, such as securing justice and compensation for the wrongfully convicted and their families[11], releasing the innocent from prison and reducing the costs and harms of incarceration[12], identifying and prosecuting the actual perpetrators of the crimes and enhancing public safety[13], exposing and correcting the sources and causes of wrongful convictions, such as faulty evidence, misconduct, bias, or error[14][15][16][17], and improving the quality and reliability of the criminal justice system and enhancing public trust and legitimacy[18][19][20][21][22].

However, post-conviction analysis also faces many challenges and barriers, such as lack of resources and capacity to conduct thorough and timely reviews of past cases and evidence[23][24][25], lack of access and availability of relevant and reliable data and evidence, such as DNA samples, court records, or police reports[26][27][28], lack of standards and guidelines for conducting and evaluating post-conviction analysis and ensuring its quality and consistency[29][30][31], lack of transparency and accountability for the outcomes and impacts of post-conviction analysis and ensuring its fairness and impartiality[32][33][34], lack of cooperation and collaboration among the various stakeholders involved in post-conviction analysis, such as defense attorneys, prosecutors, judges, police, forensic experts, and innocence projects[35][36][37], lack of incentives and motivation for initiating and supporting post-conviction analysis, especially for prosecutors and police, who may face reputational, legal, or professional risks or costs[38][39][40], and lack of public awareness and education about the problem of wrongful convictions and the importance of post-conviction analysis[41][42].

AI has the potential to address some of these challenges and enhance the opportunities of post-conviction analysis, by providing new tools and methods for collecting, analyzing, and presenting data and evidence[43][44][45][46]. AI is a broad term that refers to the use of computer systems and algorithms to perform tasks that normally require human intelligence, such as reasoning, learning, decision making, and problem solving[47][48][49][50]. AI can be applied to various aspects and stages of post-conviction analysis, such as identifying and prioritizing cases that are most likely to involve wrongful convictions, based on criteria such as the type and quality of evidence, the severity of the sentence, or the availability of new information[51][52][53][54], extracting and organizing relevant data and evidence from various sources, such as court documents, police reports, forensic reports, or media articles, using techniques such as natural language processing, optical character recognition, or image analysis[55][56][57][58], analyzing and evaluating the data and evidence, using techniques such as machine learning, statistical analysis, or probabilistic reasoning, to detect and quantify the sources and causes of wrongful convictions, such as false confessions, eyewitness misidentification, or forensic error[59][60][61][62], and presenting and communicating the results and recommendations of post-conviction analysis, using techniques such as data visualization, report generation, or argumentation, to support and persuade the relevant decision makers, such as judges, prosecutors, or parole boards.

AI can offer several advantages and benefits for post-conviction analysis, such as increasing the efficiency and scalability of post-conviction analysis, by reducing the time and labor required to review and process large amounts of data and evidence[63][64][65], increasing the accuracy and reliability of post-conviction analysis, by reducing the errors and biases that may affect human judgment and decision making[66][67][68], increasing the transparency and accountability of post-conviction analysis, by providing clear and consistent criteria and methods for conducting and evaluating post-conviction analysis and ensuring its quality and consistency[69][70][71], increasing the cooperation and collaboration of post-conviction analysis, by facilitating the sharing and integration of data and evidence among the various stakeholders and providing a common platform and language for communication and negotiation[72][73][74], increasing the incentives and motivation for post-conviction analysis, by providing evidence-based and data-driven arguments and recommendations that can overcome the resistance and reluctance of some actors and demonstrate the benefits and impacts of post-conviction analysis, and increasing the public awareness and education of post-conviction analysis[75][76][77], by providing accessible and engaging ways of informing and educating the public about the problem of wrongful convictions and the importance of post-conviction analysis[78][79][80].

Challenges and Opportunities

Despite the potential benefits of AI for post-conviction analysis, there are also significant challenges and limitations that need to be addressed and overcome, such as data quality and availability, ethical and legal issues, and social and organizational resistance.

Data quality and availability: AI depends on the availability and quality of data and evidence, which may be scarce, incomplete, inconsistent, or unreliable, especially for older cases or cases involving marginalized groups[81][82]. AI may also introduce new errors or biases in the data and evidence, such as noise, outliers, or misclassification, which may affect the validity and reliability of the analysis. Therefore, AI should be used with caution and care, and only when there is sufficient and reliable data and evidence to support the analysis. AI should also be subject to rigorous and regular testing and validation, to ensure the accuracy and reliability of the analysis. AI should also be complemented and supplemented by human expertise and judgment, to verify and interpret the results and recommendations of the analysis[83].

Ethical and legal issues: AI raises various ethical and legal issues, such as privacy, consent, ownership, accountability, and explainability, which may affect the rights and interests of the individuals and groups involved in post-conviction analysis[84][85][86][87][88], such as the wrongfully convicted, the victims, the witnesses, or the professionals. AI may also pose new risks or harms, such as discrimination, manipulation, or coercion, which may affect the fairness and impartiality of the analysis. Therefore, AI should be used with respect and responsibility, and only when there is clear and informed consent and agreement from the individuals and groups involved in the analysis. AI should also be subject to ethical and legal standards and regulations, to ensure the protection and promotion of the rights and interests of the individuals and groups involved in the analysis. AI should also be transparent and accountable, and provide clear and understandable explanations and justifications for the results and recommendations of the analysis[89].

Social and organizational resistance: AI may face social and organizational resistance, such as distrust, fear, or hostility, from some of the stakeholders and actors involved in post-conviction analysis, such as prosecutors, police, judges, or forensic experts, who may perceive AI as a threat or a challenge to their authority, expertise, or reputation. AI may also encounter cultural and institutional barriers, such as norms, values, or traditions, that may hinder or prevent the adoption and acceptance of AI for post-conviction analysis. Therefore, AI should be used with collaboration and cooperation, and only when there is trust and support from the stakeholders and actors involved in the analysis. AI should also be subject to social and organizational dialogue and engagement, to ensure the participation and representation of the stakeholders and actors involved in the analysis. AI should also be adaptive and responsive, and provide flexible and customizable options and solutions for the results and recommendations of the analysis[90][91][92][93][94].

Therefore, there is a need for a balanced and nuanced approach to using AI for post-conviction analysis, that recognizes and addresses the challenges and limitations, while also leveraging and enhancing the opportunities and benefits. This approach should be based on the principles and guidelines of data quality and availability, ethical and legal issues, and social and organizational resistance, and should be implemented through the recommendations for policy and practice, that facilitate and foster the use of AI for post-conviction analysis.

Recommendations

Based on the above principles and guidelines, this white paper provides the following recommendations for policy and practice, to facilitate and foster the use of AI for post-conviction analysis:

Policy recommendations:

• Establish and enforce legal and ethical frameworks and standards for the use of AI for post-conviction analysis, that ensure the quality, reliability, fairness, and accountability of the analysis.

• Provide and allocate adequate and sustainable resources and funding for the development and implementation of AI for post-conviction analysis, that ensure the efficiency, scalability, and accessibility of the analysis.

• Promote and support public awareness and education campaigns and initiatives for the use of AI for post-conviction analysis, that ensure the understanding, acceptance, and engagement of the public and the media.

Practical recommendations:

• Develop and adopt best practices and guidelines for the use of AI for post-conviction analysis, that ensure the quality, reliability, fairness, and accountability of the analysis.

• Establish and maintain data and evidence repositories and platforms for the use of AI for post-conviction analysis, that ensure the availability, consistency, and integration of the data and evidence.

• Build and strengthen partnerships and networks for the use of AI for post-conviction analysis, that ensure the cooperation, collaboration, and communication of the stakeholders and actors.

• Encourage and empower police departments to lead the charge in post-conviction analysis, as a way to calibrate algorithms, provide transparency, and establish trust with the community in the capabilities of AI and the police. By initiating and supporting post-conviction analysis, police departments can demonstrate their commitment to justice, accountability, and innovation, and can revolutionize their relationships with the public and marginalized communities, who are most affected by wrongful convictions and over-policing.

Conclusion

AI has the potential to improve the accuracy and efficiency of post-conviction analysis, which is a crucial mechanism for identifying and correcting wrongful convictions, as well as for improving the quality and reliability of the criminal justice system. However, AI also faces significant challenges and limitations, such as data quality and availability, ethical and legal issues, and social and organizational resistance, which need to be addressed and overcome. Therefore, there is a need for a balanced and nuanced approach to using AI for post-conviction analysis, that recognizes and addresses the challenges and limitations, while also leveraging and enhancing the opportunities and benefits. This approach should be based on the principles and guidelines of data quality and availability, ethical and legal issues, and social and organizational resistance, and should be implemented through the recommendations for policy and practice, that facilitate and foster the use of AI for post-conviction analysis. One of the key recommendations is that police departments should lead the charge in post-conviction analysis, as a way to calibrate algorithms, provide transparency, and establish trust with the community in the capabilities of AI and the police. This white paper aims to provide a comprehensive and coherent overview and analysis of the current state, the main challenges and opportunities, and the recommendations for policy and practice, of using AI for post-conviction analysis. This white paper also aims to stimulate and inform further research and action on this important and emerging topic, and to contribute to the advancement and improvement of post-conviction analysis and the criminal justice system.

About the author

 FPI Fellow Philip Lukens, is a retired police chief and a policing consultant who writes extensively about policing and artificial intelligence and about many other police-related issues. Click here to read his full bio. To read more of his commentary on AI in policing visit his Substack.

Endnotes

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