Discrimination-aware data analysis for criminal intelligence

The growing use of Machine Learning (ML) algorithms in many application domains such as healthcare, business, education and criminal justice has evolved great promises as well challenges. ML pledges in proficiently analysing a large amount of data quickly and effectively by identifying patterns and providing insight into the data, which otherwise would have been impossible for a human to execute in this scale. However, the use of ML algorithms, in sensitive domains such as the Criminal Intelligence Analysis (CIA) system, demands extremely careful deployment. Data has an important impact in ML process. To understand the ethical and privacy issues related to data and ML, the VALCRI (Visual Analytics for sense-making in the CRiminal Intelligence analysis) system was used . VALCRI is a CIA system that integrated machine-learning techniques to improve the effectiveness of crime data analysis. At the most basic level, from our research, it was found that lack of harmonised interpretation of different privacy principles, trade-offs between competing ethical principles, and algorithmic opacity as concerning ethical and privacy issues among others. This research aims to alleviate these issues by investigating awareness of ethical and privacy issues related to data and ML. Document analysis and interviews were conducted to examine the way different privacy principles were understood in selected EU countries. The study takes a qualitative and quantitative research approach and is guided by various methods of analysis including interviews, observation, case study, experiment and legal document analysis. The findings of this research indicate that a lack of ethical awareness on data has an impact on ML outcome. Also, due to the opaque nature of the ML system, it is difficult to scrutinize and as a consequence, it leads to a lack of clarity in terms of how certain decisions were made. This thesis provides some novel solutions that can be used to tackle these issues

Algorithmic opacity: making algorithmic processes transparent through abstraction hierarchy

In this paper we introduce the problem of algorithmic opacity and the challenges it presents to ethical decision-making in criminal intelligence analysis. Machine learning algorithms have played important roles in the decision-making process over the past decades. Intelligence analysts are increasingly being presented with smart black box automation that use machine learning algorithms to find patterns or interesting and unusual occurrences in big data sets. Algorithmic opacity is the lack visibility of computational processes such that humans are not able to inspect its inner workings to ascertain for themselves how the results and conclusions were computed. This is a problem that leads to several ethical issues. In the VALCRI project, we developed an abstraction hierarchy and abstraction decomposition space to identify important functional relationships and system invariants in relation to ethical goals. Such explanatory relationships can be valuable for making algorithmic process transparent during the criminal intelligence analysis process

Algorithmic transparency of conversational agents

A lack of algorithmic transparency is a major barrier to the adoption of artificial intelligence technologies within contexts which require high risk and high consequence decision making. In this paper we present a framework for providing transparency of algorithmic processes. We include important considerations not identified in research to date for the high risk and high consequence context of defence intelligence analysis. To demonstrate the core concepts of our framework we explore an example application (a conversational agent for knowledge exploration) which demonstrates shared human-machine reasoning in a critical decision making scenario. We include new findings from interviews with a small number of analysts and recommendations for future research

Evaluation of work-related psychosocial factors and regional musculoskeletal pain: results from a EULAR Task Force

Objectives: to establish whether review articles provide consistent conclusions on associations between workplace psychosocial factors and musculoskeletal pain and, if differences exist, to explore whether this is related to the methods used.Methods: reviews, reported up to February 2007, that included consideration of workplace psychosocial factors and upper limb, back or knee pain were identified through searches of multiple databases. The specific work-related psychosocial factors considered were job demands, support, job autonomy and job satisfaction. The conclusions of each review on one or more of the psychosocial/musculoskeletal pain associations were extracted.Results: 15 review articles were identified that considered one or more of the regional pain syndromes included in the study. For back pain, the most consistent conclusions (four reviews positive out of six) were with high job demands and low job satisfaction. The studies of upper limb pain were exclusively related to shoulder and/or neck pain, and the most consistent positive conclusions were with high and low job demands (four reviews positive out of six and two reviews positive out of three, respectively). For knee pain, only a single review was identified. For individual reviews of back and upper limb pain, there were marked differences in the number of associations concluded to be positive between reviews.Conclusions: the reasons for reviews coming to different conclusions included that they were often evaluating different bodies of evidence (according to their search criteria, the year when the review was conducted, the role that quality assessment played in whether studies contributed to evidence, and the combination of risk factors addressed in individual studies), but more important was whether the review specified explicit criteria for making conclusions on strength of evidence. These conclusions emphasise the importance of developing standardised methods for conducting such evaluations of existing evidence and the importance of new longitudinal studies for clarifying the temporal relationship between psychosocial factors and musculoskeletal pain in the workplac

Maize in Nepal: Production Systems, Constraints, and Priorities for Research

Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies,