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

Police Knowledge Exchange: Summary Report

[Executive Summary] This report draws on research commissioned by the Association of Police and Crime Commissioners (APCC), the National Police Chiefs Council (NPCC) and the Home Office to investigate cultural aspects of knowledge sharing across the police service. The research reviews literature and police perceptions to identify the enablers and barriers to effective knowledge exchange and sharing within and between police forces and police partners, including the public. Data were collected from 11 police forces; 42 in-depth interviews/focus groups and 47 survey responses. The literature-guided analysis identified four core research themes: who, why, what and how we share. Detailed findings are presented in the full report; this summary report presents the core research findings. Recommendations from this study will inform the next phase of activity for the Board. The research identified that cross-force, cross-organisation, national and international sharing relies on a culture supporting individuals who have an independent and reflective sharing approach. A key enabler to police sharing is that, regardless of police rank and role, they all have a strong collaborative nature, through a deep motivation to share, that benefits the wider social community. This collaborative nature is driven by processes that reveal reciprocal benefit and safe sharing, as well as how to effectively ‘get the job done’ and foster professional learning. A key barrier to police sharing is a strong hierarchical culture that does not encourage the independent nature of sharing. Whilst police officers and staff act independently within the confines of their prescribed roles, they rarely independently share beyond this. This hierarchical culture means that innovations in sharing are often initiated or approved top-down and tied to leadership. Hierarchical structures are seen to support a competitive culture combining concepts of risk aversion and blame. The hierarchical culture is also perceived as providing poor clarity on what is of value to share and how to effectively share. There are two key recommendations to overcome this barrier: one long-term and one short-term. Long-term: ‘Become independent sharers’ by changing the nature and culture of the police to encourage this independent nature, so that specific sharing barriers are effectively solved by individuals. Professionalising the police and working collaboratively with academia are steps towards this long-term goal. Short-term: ‘Guide and authorise independent sharing’ by using the hierarchy to scaffold/support and direct police towards effective and approved sharing approaches. This will show the police, through the hierarchy, how and why this independent sharing nature is safe, effective and valued

Data-CASE: Grounding Data Regulations for Compliant Data Processing Systems

Data regulations, such as GDPR, are increasingly being adopted globally to protect against unsafe data management practices. Such regulations are, often ambiguous (with multiple valid interpretations) when it comes to defining the expected dynamic behavior of data processing systems. This paper argues that it is possible to represent regulations such as GDPR formally as invariants using a (small set of) data processing concepts that capture system behavior. When such concepts are grounded, i.e., they are provided with a single unambiguous interpretation, systems can achieve compliance by demonstrating that the system-actions they implement maintain the invariants (representing the regulations). To illustrate our vision, we propose Data-CASE, a simple yet powerful model that (a) captures key data processing concepts (b) a set of invariants that describe regulations in terms of these concepts. We further illustrate the concept of grounding using "deletion" as an example and highlight several ways in which end-users, companies, and software designers/engineers can use Data-CASE.Comment: To appear in EDBT '2

When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks

Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via do-calculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation. In our framework, CE is calculated using features in a latent space and perturbed prediction from a DNN-based model. We further provide the first look into the characteristics of discovered CE of adversarially perturbed images generated by gradient-based methods \footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}. Experimental results show that CE is a competitive and robust index for understanding DNNs when compared with conventional methods such as class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds promises for detecting adversarial examples as it possesses distinct characteristics in the presence of adversarial perturbations.Comment: Noted our camera-ready version has changed the title. "When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks" as the v3 official paper title in IEEE Proceeding. Please use it in your formal reference. Accepted at IEEE ICIP 2019. Pytorch code has released on https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvIm

“Right to erasure” and private blockchain in the European Union: legal requirements and technical possibilites

Blockchain, serving as one of the most complex networks used within an organization may be regarded as challenging for the applicability and realization of the General Data Protection Regulation Article 17, which gives the data subject right to erasure or a “right to be forgotten” to ones’ personal data. The immutability and decentralized character of the system does not prescribe the erasure of personal data on the chain, as well as poses problems in determining the competent authority responsible for data protection compliance, when the data subject needs to exercise its rights under the GDPR. The thesis examines whether the compliance with the General Data Protection Regulation’s Article 17 could be ensured while using private blockchain within an organization, by determining the authorities responsible for the compliance in decentralized system, and, examining the conditions when immutability may allow for data erasure. Thus, proposing possible solutions and developing the guidelines for businesses how to mitigate the enforcement of the Regulation regardless of technological pattern of private blockchain

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education