Every year an estimated 200,000 people go missing in the UK alone. Missing persons investigations involve challenging time-critical sensemaking tasks based on fragmented data sources. This paper describes a mixed-methods participatory study evaluating data science and AI-driven techniques (summarisation, fact extraction, and data visualisation) for supporting these investigations as part of a human-centered workflow. A series of human-AI interfaces were iteratively designed and tested with search officers and domain experts at Police Scotland. Based on findings, we describe: (1) user and information needs for missing persons investigations; (2) insights on the benefits and challenges of applying LLM-based techniques in high-risk contexts; and (3) lessons for integrating AI for sensemaking tasks in policing more broadly. We highlight that in high-stakes contexts, where accuracy and context-sensitivity are paramount, AI techniques must be balanced with other approaches and designed in close partnership with end-users.

The growing popularity of social media raises concerns about children’s online safety. Of particular concern are interactions between minors and adults with predatory intentions. Unfortunately, previous research on online sexual grooming has relied on time-intensive manual annotation by domain experts, limiting both the scale and scope of possible interventions. This work explores the possibility of detecting predatory behaviours with accuracy comparable to expert annotators using machine learning (ML). Using a dataset of 6771 chat messages sent by child sex offenders, labelled by two of the authors who are forensic psychology experts, we study how well can deep learning algorithms identify eleven known predatory behaviours. We find that the best-performing ML models are consistent but not on par with expert annotation. We therefore consider a system where an expert annotator validates the ML algorithms outputs. The combination of human decision-making and computer efficiency yields precision—but not recall—comparable to manual annotation, while taking only a fraction of the time needed by a human annotator. Our findings underscore the promise of ML as a tool for assisting researchers in this area, but also highlight the current limitations in reliably detecting online sexual exploitation using ML.

Racial disparities in US drug arrest rates have been observed for decades, but their causes and policy implications are still contested. Some have argued that the disparities largely reflect differences in drug use between racial groups, while others have hypothesized that discriminatory enforcement policies and police practices play a significant role. In this work, we analyze racial disparities in the enforcement of marijuana violations in the US. Using data from the National Incident-Based Reporting System (NIBRS) and the National Survey on Drug Use and Health (NSDUH) programs, we investigate whether marijuana usage and purchasing behaviors can explain the racial composition of offenders in police records. We examine potential driving mechanisms behind these disparities and the extent to which county-level socioeconomic factors are associated with corresponding disparities. Our results indicate that the significant racial disparities in reported incidents and arrests cannot be explained by differences in marijuana days-of-use alone. Variations in the location where marijuana is purchased and in the frequency of these purchases partially explain the observed disparities. We observe an increase in racial disparities across most counties over the last decade, with the greatest increases in states that legalized the use of marijuana within this timeframe. Income, high school graduation rate, and rate of employment positively correlate with larger racial disparities, while the rate of incarceration is negatively correlated. We conclude with a discussion of the implications of the observed racial disparities in the context of algorithmic fairness.

We present a survey of tools used in the criminal justice system in the UK in three categories: data infrastructure, data analysis, and risk prediction. Many tools are currently in deployment, offering potential benefits, including improved efficiency and consistency. However, there are also important concerns. Transparent information about these tools, their purpose, how they are used, and by whom is difficult to obtain. Even when information is available, it is often insufficient to enable a satisfactory evaluation. More work is needed to establish governance mechanisms to ensure that tools are deployed in a transparent, safe and ethical way. We call for more engagement with stakeholders and greater documentation of the intended goal of a tool, how it will achieve this goal compared to other options, and how it will be monitored in deployment. We highlight additional points to consider when evaluating the trustworthiness of deployed tools and make concrete proposals for policy.

Criminal justice is an increasingly important application domain for machine learning and algorithmic fairness, as predictive tools are becoming widely used in police, courts, and prison systems worldwide. A few relevant benchmarks have received significant attention, e.g., the COMPAS dataset, often without proper consideration of the domain context. To raise awareness of publicly available criminal justice datasets and encourage their responsible use, we conduct a survey, consider contexts, highlight potential uses, and identify gaps and limitations. We provide datasheets for 15 datasets and upload them to a public repository. We compare the datasets across several dimensions, including size, coverage of the population, and potential use, highlighting concerns. We hope that this work can provide a useful starting point for researchers looking for appropriate datasets related to criminal justice, and that the repository will continue to grow as a community effort.

Can an interviewer influence the cooperativeness of an interviewee? The role of an interviewer in actualising a successful interview is an active field of social psychological research. A large-scale analysis of interviews, however, typically involves time-exorbitant manual tasks and considerable human effort. Despite recent advances in computational fields, many automated methods continue to rely on manually labelled training data to establish ground-truth. This reliance obscures explainability and hinders the mobility of analysis between applications. In this work, we introduce a cross-disciplinary approach to analysing interviewer efficacy. We suggest computational success measures as a transparent, automated, and reproducible alternative for pre-labelled data. We validate these measures with a small-scale study with human-responders. To study the interviewer’s influence on the interviewee we utilise features informed by social psychological theory to predict interview quality based on the interviewer’s linguistic behaviour. Our psychologically informed model significantly outperforms a bag-of-words model, demonstrating the strength of a cross-disciplinary approach toward the analysis of conversational data at scale.