The next meeting of the section will be held on 10 December 2018 from 14.00-17.00 at the Royal Statistical Society, 12 Errol Street, London EC1Y 8LX. The AGM of the section will be from 13.40-14.00, immediately preceding the meeting.
The meeting will be on policing and criminology. We will have four speakers:
- Professor Jim Smith (University of Warwick/ Alan Turing Institute)
Graphical Models to Help Investigate Violent Criminals
One serious challenge in providing support for policing various kinds of systematic violent crime is that cases can be very dynamic and idiosyncratic. In this talk I will outline the progress we are making in designing new graphical interfaces that help to separate the enduring structure of these processes from their more ephemeral features. Our Bayesian models have derived from earlier studies concerning the synthesis forensic activity level evidence but are now applied to resourcing models to support the prevention of crime. This reports on ongoing work undertaken by a team of researchers at the Alan Turing Institute.
- Dr Anjali Mazumder (Carnegie Mellon University/ Alan Turing Institute)
Algorithmic Tools in Justice – bias and fairness, a causal lens
There has been an increasing use of algorithmic tools to support decision-making across public sectors, including financial, human, health care, policing and criminal justice services. The use of such algorithms is not new. However, with growing recognition of the bias and potential for unfairness that such tools may possess and perpetuate, researchers have begun to develop methods to achieve algorithmic fairness. In this talk, we discuss the use of algorithmic tools in justice, their potential for and inherent bias and implications on fairness in such high stakes decision-making, and approaches to achieve algorithmic fairness. We will take a particular causal lens to explore the fairness of algorithmic tools in which forensic science plays a central role in both the investigative and evaluative stage of a criminal case. This latter work reports on ongoing work undertaken by researchers at the Alan Turing Institute and Carnegie Mellon University.
- Professor David Tuckett (Psychoanalysis Unit, UCL)
Making Decisions under Radical Uncertainty
How can academic study help business-leaders, policy-makers, regulators or those in in a courtroom, make better decisions? For a long time now, the answer has been by using tools such as game theory, expected utility theory, subjective utility theory to provide them with optimal choices – and often with major success. However, are these tools always – or even usually – appropriate? What role do specifically human qualities of imagination, feeling and intuition have to play? What is the role of analysing “data” properly and is the conclusion to draw from academic research that humans are poor decision-makers, influenced by bias and emotion, so that we would be better relying on AI as often as possible?
This talk will review these questions by introducing the work of the UKRI funded CRUISSE network and introducing Conviction Narrative Theory, as a model for human decision-making when there is deep uncertainty.
 Confronting Radical Uncertainty in Science, Society and the Environment.
- Dr Toby Davies (Jill Dando Institute of Security and Crime Science, UCL)
Understanding and predicting urban patterns of crime
One of the most crucial steps in preventing crime is understanding where and when it happens: as well as providing a basis for the deployment of police resources, such insight also provides a rationale for the application of place-based interventions. Traditionally, gaining such insight has been a particular challenge, given the complexity of behaviours involved, and its utility has been primarily descriptive. In recent years, however, improved data availability, coupled with the application of analytical techniques from other fields, has revealed a number of statistical regularities in crime data; most notably, its heterogeneous distribution in space and time and the prevalence of space-time clustering. In turn, the presence of these regularities has raised the possibility that they can be leveraged in order to predict the locations of future crimes by applying algorithms to past crime data. In this talk, I will briefly review this background, before discussing recent research which examines the role that urban structure – in particular the street network – plays in shaping these patterns. I will discuss the implications of this for crime prediction, and show how the adaptation of algorithms to account for this structure leads to improved predictive performance. In conclusion, I will describe a real-world implementation of predictive policing and identify opportunities for further exploitation of data in the field.
Attendance at the meeting is free of charge but registration is required. The registration link can be found here: