Privacy impact assessment in the UK

Privacy impact assessment in the U

Machine Decisions and Human Consequences

As we increasingly delegate decision-making to algorithms, whether directly or indirectly, important questions emerge in circumstances where those decisions have direct consequences for individual rights and personal opportunities, as well as for the collective good. A key problem for policymakers is that the social implications of these new methods can only be grasped if there is an adequate comprehension of their general technical underpinnings. The discussion here focuses primarily on the case of enforcement decisions in the criminal justice system, but draws on similar situations emerging from other algorithms utilised in controlling access to opportunities, to explain how machine learning works and, as a result, how decisions are made by modern intelligent algorithms or 'classifiers'. It examines the key aspects of the performance of classifiers, including how classifiers learn, the fact that they operate on the basis of correlation rather than causation, and that the term 'bias' in machine learning has a different meaning to common usage. An example of a real world 'classifier', the Harm Assessment Risk Tool (HART), is examined, through identification of its technical features: the classification method, the training data and the test data, the features and the labels, validation and performance measures. Four normative benchmarks are then considered by reference to HART: (a) prediction accuracy (b) fairness and equality before the law (c) transparency and accountability (d) informational privacy and freedom of expression, in order to demonstrate how its technical features have important normative dimensions that bear directly on the extent to which the system can be regarded as a viable and legitimate support for, or even alternative to, existing human decision-makers

Digital Curation, Copyright, and Academic Research

A defining characteristic of contemporary copyright law is the willingness of governments to accept the argument that the impact of digital technologies requires copyright owners to be given ever greater control over the use of their works, regardless of the detriment to the copyright regime's 'public interest' elements. Yet a one-size-fits-all 'all rights reserved' copyright regime clearly fails to meet the requirements of many rightsholders. One response has been the Creative Commons movement which seeks, through licences based on existing copyright laws, to provide a simple mechanism for rightsholders to disseminate their works under less restrictive conditions. The Creative Commons' initial success has led to suggestions that its principles could be equally applied to scientific research outputs, such as publications, licensing of research materials, and datasets. This article argues that the Science Commons approach, if based on the Creative Commons model, and premised at its root on utilitarian copyright law, will both fail to address contemporary policy drivers in research, or to provide researchers with the type of rights that they actually want. It suggests that constructing an appropriate set of rights for the Science Commons, particularly for datasets, will require a willingness to step outside the utilitarian model and look to the Continental copyright tradition, which sets less store in economic rights and gives greater weight to moral rights

Australian Capital Territory Economic, Social and Cultural Rights Research Project Report

This Report presents the findings and recommendations of a research project established to examine whether the ACT Human Rights Act 2004 (HRA) should be amended to include explicit guarantees of economic, social and cultural rights (ESCR) and, if so, what impact this was likely to have on governance in the ACT. The project was funded under the Australian Research Council Linkage Project Scheme; the academic project partners were the Regulatory Institutions Network (RegNet) in the College of Asia and the Pacific of The Australian National University and the Australian Human Rights Centre, Faculty of Law, The University of New South Wales, while the Partner Organisation was the ACT Department of Justice and Community Safety

Quantitative differences in developmental profiles of spontaneous activity in cortical and hippocampal cultures.

BACKGROUND: Neural circuits can spontaneously generate complex spatiotemporal firing patterns during development. This spontaneous activity is thought to help guide development of the nervous system. In this study, we had two aims. First, to characterise the changes in spontaneous activity in cultures of developing networks of either hippocampal or cortical neurons dissociated from mouse. Second, to assess whether there are any functional differences in the patterns of activity in hippocampal and cortical networks. RESULTS: We used multielectrode arrays to record the development of spontaneous activity in cultured networks of either hippocampal or cortical neurons every 2 or 3 days for the first month after plating. Within a few days of culturing, networks exhibited spontaneous activity. This activity strengthened and then stabilised typically around 21 days in vitro. We quantified the activity patterns in hippocampal and cortical networks using 11 features. Three out of 11 features showed striking differences in activity between hippocampal and cortical networks: (1) interburst intervals are less variable in spike trains from hippocampal cultures; (2) hippocampal networks have higher correlations and (3) hippocampal networks generate more robust theta-bursting patterns. Machine-learning techniques confirmed that these differences in patterning are sufficient to classify recordings reliably at any given age as either hippocampal or cortical networks. CONCLUSIONS: Although cultured networks of hippocampal and cortical networks both generate spontaneous activity that changes over time, at any given time we can reliably detect differences in the activity patterns. We anticipate that this quantitative framework could have applications in many areas, including neurotoxicity testing and for characterising the phenotype of different mutant mice. All code and data relating to this report are freely available for others to use.PC and AM were supported by the Wellcome Trust Genes to Cognition programme. PC received additional support from the Biotechnology and Biological Sciences Research Council (BB/H008608/1). EC was supported by a Wellcome Trust PhD studentship and Cambridge Biomedical Research Centre studentship. SJE was supported by an Engineering and Physical Sciences Research Council grant (EP/E002331/1).This is the final published version. It first appeared at http://link.springer.com/article/10.1186%2Fs13064-014-0028-0

Privacy Impact Assessment in the UK

Privacy impact assessment in the U