Supporting Intelligence Analysts with a Trust-Based Question-Answering System

Intelligence analysts have to work in highly demanding circumstances. This causes mistakes with severe consequences, which is the reason that support systems for intelligence analysts have been developed. The support system proposed in this paper assists humans by offering support that improves their performance, without reducing them in their freedom. This is done with a trust-based question answering system (T-QAS). An important part of T-QAS are trust models which keep track of trust in each of the agents gathering information. Using these trust models, the system can support the intelligence analyst by: 1) helping to decide which agents are trusted enough to receive questions, 2) providing information about the reliability of each of the sources used, and 3) advising in making decisions based on information from possibly unreliable sources. An implementation of last two capabilities of T-QAS is evaluated in an experiment in which participants perform a decision making task with information from possibly unreliable sources. Results show that the proposed T-QAS support indeed helps participants to improve their performance. We therefore expect that future intelligence analyst support systems can benefit from the inclusion of T-QAS

A survey of intelligence analysts’ perceptions of analytic tools

This article presents a survey of 278 intelligence analysts’ views of fully operational analytic technologies and their newly developed replacements. It was found that usability was an important concept in analysts’ reasons for and against using analytic tools. The perceived usability of a tool was not necessarily indicative of its perceived usefulness. Analysts’ decisions to recommend an analytic tool to others were best predicted by how usable analysts perceived the tool to be rather than how useful they considered the tool to be. These findings have implications for the development and implementation of new analytic technologies in the intelligence community

Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

Developing conversational agents for use in criminal investigations

The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision-making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency relates to (i) the explainability of results and (ii) the ability of a user to inspect and verify system goals and constraints; and brittleness, (iii) the ability of a system to adapt to new user demands. Transparency is a particular concern for criminal intelligence analysis, where there are significant ethical and trust issues that arise when algorithmic and system processes are not adequately understood by a user. This prevents adoption of potentially useful technologies in policing environments. In this article, we present a novel approach to designing a conversational agent (CA) AI system for intelligence analysis that tackles these issues. We discuss the results and implications of three different studies; a Cognitive Task Analysis to understand analyst thinking when retrieving information in an investigation, Emergent Themes Analysis to understand the explanation needs of different system components, and an interactive experiment with a prototype conversational agent. Our prototype conversational agent, named Pan, demonstrates transparency provision and mitigates brittleness by evolving new CA intentions. We encode interactions with the CA with human factors principles for situation recognition and use interactive visual analytics to support analyst reasoning. Our approach enables complex AI systems, such as Pan, to be used in sensitive environments, and our research has broader application than the use case discussed

Police Knowledge Exchange: Full Report 2018

[Executive Summary] This report was commissioned to explore the enablers and barriers to sharing within and between police forces and between police forces and partners, including the public. This was completed from an interdisciplinary review of international literature covering sharing, knowledge exchange, learning and organisational learning. The literature broke down into four main factors; who, why, what and how. An introduction to the literature is presented with ‘Who’ is sharing which considers both personal identity and different institutional issues. The ‘Why’ literature covers issues of cultural and community motivators and barriers. The ‘What’ segment reviews concepts of data, information and knowledge and related legislative issues. Finally, the ‘how’ section spans face to face sharing approaches to technologies that produce both enablers and barriers. A series of 42 in-depth interviews and focus groups were completed and combined with 47 survey responses . The aim of the interviews, focus groups and survey was to show perceptions and beliefs around knowledge sharing from a small sample across policing in order to complement the findings from the literature review. The survey was adapted from a standardised questionnaire (Biggs, 1987). The Biggs questionnaire focused on what motivated students to learn and how they approached their learning. Our adapted survey looked at what motivated police to share, and how they approached sharing. The responses showed a trend, across the police, towards a motivation for sharing to develop a deeper understanding of issues. However, the approaches and the strategies they used to share with others, which were primarily driven by achieving and surface approaches (to get promoted and get the job done). According to Biggs (1987) this could leave them discontented as they never progress to a deeper understanding of issues. Scaffolding sharing within the police through processes that are clearly defined, effective and valued could help to overcome these issues. Within the interviews and focus group findings a similar structured approach to sharing was adopted. Within the ‘who’ section some key aspects around personal relationships, reciprocity and reputation were identified. The ‘why’ the police share was one of the largest discussion points. Not only was there a deep motivation to solve key policing issues there was an approach of reciprocity. Police sharing was deeply motivated to support ‘good practice’ in the prevention and detection of crime. However, a sharing barrier was identified in the parity of value given to different types of knowledge for example between professional judgement and research evidence knowledge. Sharing was achieved when there were reciprocal benefits, in particular with personal networks or face to face sharing which was noted as ‘safe’. Again, this was inhibited by misunderstandings around the ‘risks’ of sharing, frequently attributed to data protection legislation; producing cautious reactions and as an avoidance tactic to save time and effort sharing. However, a divide was noted between technical users and those who avoided any online systems for sharing; often due to poorly designed systems and a lack of confidence in how to use systems. The police culture was identified as being risk-adverse, and competitive due to multiple factors, a lack of supported time to share, Her Majesty’s Inspectorate of Constabulary (HMIC) reviews and promotion criteria. The result was perceived to be a poor cultural ability to learn from mistakes and a likelihood to repeat errors. A set of strategic recommendations are given and include the use of a sharing authorised professional practice for HMIC reviews, sharing networks and training. A further set of operational recommendations are given such as; sharing impact cases for evidence based practice, data sharing officers and evaluating mechanisms for sharing. This full report is supported by the Police Knowledge Exchange Summary Report 2018 which gives an overview of the findings and recommendations

Continuity and change in the history of police technology: The case of contemporary crime analysis

A series of police practices and technology make up what today is known as crime analysis. Crime analysis can broadly be defined as the use of police knowledge and data to combat and solve crime. The current study seeks to illuminate the current status of crime analysis, and the measures being taken to gain legitimacy and recognition in the field of law enforcement. First, the historical backdrop of technology and police history will be established. Next, three inter-related research projects are used to frame patterns and practices of contemporary crime analysis. The first project examines police organizations’ adoption of community problem analysis. The second explores themes emerging from a list serve used by crime analysts for professional assistance and queries. Third, a survey of analysts from across New York State is used to describe the experience and training needs among contemporary crime analysts. The research findings are used to evaluate crime analysis as an emerging profession and suggest questions and avenues for future research

Developing conversational agents for use in criminal investigations

The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency relates to (i) the explainability of results and (ii) the ability of a user to inspect and verify system goals and constraints, and brittleness (iii) the ability of a system to adapt to new user demands. Transparency is a particular concern for criminal intelligence analysis, where there are significant ethical and trust issues that arise when algorithmic and system processes are not adequately understood by a user. This prevents adoption of potentially useful technologies in policing environments. In this paper, we present a novel approach to designing a conversational agent (CA) AI system for intelligence analysis that tackles these issues.We discuss the results and implications of three different studies; a Cognitive Task Analysis to understand analyst thinking when retrieving information in an investigation, Emergent Themes Analysis to understand the explanation needs of different system components, and an interactive experiment with a prototype conversational agent. Our prototype conversational agent, named Pan, demonstrates transparency provision and mitigates brittleness by evolving new CA intentions. We encode interactions with the CA with human factors principles for situation recognition and use interactive visual analytics to support analyst reasoning. Our approach enables complex AI systems, such as Pan, to be used in sensitive environments and our research has broader application than the use case discussed