31,349 research outputs found

    Disclosive ethics and information technology: disclosing facial recognition systems

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    This paper is an attempt to present disclosive ethics as a framework for computer and information ethics � in line with the suggestions by Brey, but also in quite a different manner. The potential of such an approach is demonstrated through a disclosive analysis of facial recognition systems. The paper argues that the politics of information technology is a particularly powerful politics since information technology is an opaque technology � i.e. relatively closed to scrutiny. It presents the design of technology as a process of closure in which design and use decisions become black-boxed and progressively enclosed in increasingly complex sociotechnical networks. It further argues for a disclosive ethics that aims to disclose the nondisclosure of politics by claiming a place for ethics in every actual operation of power � as manifested in actual design and use decisions and practices. It also proposes that disclosive ethics would aim to trace and disclose the intentional and emerging enclosure of politics from the very minute technical detail through to social practices and complex social-technical networks. The paper then proceeds to do a disclosive analysis of facial recognition systems. This analysis discloses that seemingly trivial biases in recognition rates of FRSs can emerge as very significant political acts when these systems become used in practice

    Eavesdropping Whilst You're Shopping: Balancing Personalisation and Privacy in Connected Retail Spaces

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    Physical retailers, who once led the way in tracking with loyalty cards and `reverse appends', now lag behind online competitors. Yet we might be seeing these tables turn, as many increasingly deploy technologies ranging from simple sensors to advanced emotion detection systems, even enabling them to tailor prices and shopping experiences on a per-customer basis. Here, we examine these in-store tracking technologies in the retail context, and evaluate them from both technical and regulatory standpoints. We first introduce the relevant technologies in context, before considering privacy impacts, the current remedies individuals might seek through technology and the law, and those remedies' limitations. To illustrate challenging tensions in this space we consider the feasibility of technical and legal approaches to both a) the recent `Go' store concept from Amazon which requires fine-grained, multi-modal tracking to function as a shop, and b) current challenges in opting in or out of increasingly pervasive passive Wi-Fi tracking. The `Go' store presents significant challenges with its legality in Europe significantly unclear and unilateral, technical measures to avoid biometric tracking likely ineffective. In the case of MAC addresses, we see a difficult-to-reconcile clash between privacy-as-confidentiality and privacy-as-control, and suggest a technical framework which might help balance the two. Significant challenges exist when seeking to balance personalisation with privacy, and researchers must work together, including across the boundaries of preferred privacy definitions, to come up with solutions that draw on both technology and the legal frameworks to provide effective and proportionate protection. Retailers, simultaneously, must ensure that their tracking is not just legal, but worthy of the trust of concerned data subjects.Comment: 10 pages, 1 figure, Proceedings of the PETRAS/IoTUK/IET Living in the Internet of Things Conference, London, United Kingdom, 28-29 March 201

    Longitudinal Study of Child Face Recognition

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    We present a longitudinal study of face recognition performance on Children Longitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects, in the age group [2, 18] years. Each subject has at least four face images acquired over a time span of up to six years. Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A and FaceNet matchers. To improve the performance of the open-source FaceNet matcher for child face recognition, we were able to fine-tune it on an independent training set of 3,294 face images of 1,119 children in the age group [3, 18] years. Multilevel statistical models are fit to genuine comparison scores from the CLF dataset to determine the decrease in face recognition accuracy over time. Additionally, we analyze both the verification and open-set identification accuracies in order to evaluate state-of-the-art face recognition technology for tracing and identifying children lost at a young age as victims of child trafficking or abduction
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