1,721,009 research outputs found
Some HCI Priorities for GDPR-Compliant Machine Learning
No full textCite as Michael Veale, Reuben Binns and Max Van Kleek (2018) Some HCI Priorities for GDPR-Compliant Machine Learning. The General Data Protection Regulation: An Opportunity for the CHI Community? (CHI-GDPR 2018), Workshop at ACM CHI'18, 22 April 2018, Montreal, Canada.
In this short paper, we consider the roles of HCI in enabling the better governance of consequential machine learning systems using the rights and obligations laid out in the recent 2016 EU General Data Protection Regulation (GDPR)---a law which involves heavy interaction with people and systems. Focussing on those areas that relate to algorithmic systems in society, we propose roles for HCI in legal contexts in relation to fairness, bias and discrimination; data protection by design; data protection impact assessments; transparency and explanations; the mitigation and understanding of automation bias; and the communication of envisaged consequences of processing
Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
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Michael Veale, Max Van Kleek and Reuben Binns (2018) Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making. ACM Conference on Human Factors in Computing Systems (CHI'18). doi: 10.1145/3173574.3174014
Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions—like taxation, justice, and child protection—are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning—absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the `street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications
Algorithms that Remember: Model Inversion Attacks and Data Protection Law
No full textCite as: Michael Veale, Reuben Binns and Lilian Edwards (2018) Algorithms That Remember: Model Inversion Attacks and Data Protection Law. Philosophical Transactions A, forthcoming. doi:10.1098/rsta.2018.0083
Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EU's recent General Data Protection Regulation (GDPR) has been seen as a core tool for achieving better governance of this area. While the GDPR does apply to the use of models in some limited situations, most of its provisions relate to the governance of personal data, while models have traditionally been seen as intellectual property. We present recent work from the information security literature around `model inversion' and `membership inference' attacks, which indicate that the process of turning training data into machine learned systems is not one-way, and demonstrate how this could lead some models to be legally classified as personal data. Taking this as a probing experiment, we explore the different rights and obligations this would trigger and their utility, and posit future directions for algorithmic governance and regulation
Some HCI Priorities for GDPR-Compliant Machine Learning
Get PDFCite as Michael Veale, Reuben Binns and Max Van Kleek (2018) Some HCI Priorities for GDPR-Compliant Machine Learning. The General Data Protection Regulation: An Opportunity for the CHI Community? (CHI-GDPR 2018), Workshop at ACM CHI'18, 22 April 2018, Montreal, Canada.
In this short paper, we consider the roles of HCI in enabling the better governance of consequential machine learning systems using the rights and obligations laid out in the recent 2016 EU General Data Protection Regulation (GDPR)---a law which involves heavy interaction with people and systems. Focussing on those areas that relate to algorithmic systems in society, we propose roles for HCI in legal contexts in relation to fairness, bias and discrimination; data protection by design; data protection impact assessments; transparency and explanations; the mitigation and understanding of automation bias; and the communication of envisaged consequences of processing
When Data Protection by Design and Data Subject Rights Clash
No full textCite as: Michael Veale, Reuben Binns and Jef Ausloos (2018) When Data Protection by Design and Data Subject Rights Clash. International Data Privacy Law (2018) doi:10.1093/idpl/ipy002.
[Note: An earlier draft was entitled "We Can't Find Your Data, But A Hacker Could: How 'Privacy by Design' Trades-Off Data Protection Rights"]
Abstract
➔ Data Protection by Design (DPbD), a holistic approach to embedding principles in technical and organisational measures undertaken by data controllers, building on the notion of Privacy by Design, is now a qualified duty in the GDPR.
➔ Practitioners have seen DPbD less holistically, instead framing it through the confidentiality-focussed lens of Privacy Enhancing Technologies (PETs).
➔ While focussing primarily on confidentiality risk, we show that some DPbD strategies deployed by large data controllers result in personal data which, despite remaining clearly reidentifiable by a capable adversary, make it difficult for the controller to grant data subjects rights (eg access, erasure, objection) over for the purposes of managing this risk.
➔ Informed by case studies of Apple's Siri voice assistant and Transport for London's Wi-Fi analytics, we suggest three main ways to make deployed DPbD more accountable and data subject-centric: building parallel systems to fulfil rights, including dealing with volunteered data; making inevitable trade-offs more explicit and transparent through Data Protection Impact Assessments; and through ex ante and ex post information rights (arts 13-15), which we argue may require the provision of information concerning DPbD trade-offs.
➔ Despite steep technical hurdles, we call both for researchers in PETs to develop rigorous techniques to balance privacy-as-control with privacy-as-confidentiality, and for DPAs to consider tailoring guidance and future frameworks to better oversee the trade-offs being made by primarily well-intentioned data controllers employing DPbD
When Data Protection by Design and Data Subject Rights Clash
No full textCite as: Michael Veale, Reuben Binns and Jef Ausloos (2018) When Data Protection by Design and Data Subject Rights Clash. International Data Privacy Law (2018) doi:10.1093/idpl/ipy002.
[Note: An earlier draft was entitled "We Can't Find Your Data, But A Hacker Could: How 'Privacy by Design' Trades-Off Data Protection Rights"]
Abstract
➔ Data Protection by Design (DPbD), a holistic approach to embedding principles in technical and organisational measures undertaken by data controllers, building on the notion of Privacy by Design, is now a qualified duty in the GDPR.
➔ Practitioners have seen DPbD less holistically, instead framing it through the confidentiality-focussed lens of Privacy Enhancing Technologies (PETs).
➔ While focussing primarily on confidentiality risk, we show that some DPbD strategies deployed by large data controllers result in personal data which, despite remaining clearly reidentifiable by a capable adversary, make it difficult for the controller to grant data subjects rights (eg access, erasure, objection) over for the purposes of managing this risk.
➔ Informed by case studies of Apple's Siri voice assistant and Transport for London's Wi-Fi analytics, we suggest three main ways to make deployed DPbD more accountable and data subject-centric: building parallel systems to fulfil rights, including dealing with volunteered data; making inevitable trade-offs more explicit and transparent through Data Protection Impact Assessments; and through ex ante and ex post information rights (arts 13-15), which we argue may require the provision of information concerning DPbD trade-offs.
➔ Despite steep technical hurdles, we call both for researchers in PETs to develop rigorous techniques to balance privacy-as-control with privacy-as-confidentiality, and for DPAs to consider tailoring guidance and future frameworks to better oversee the trade-offs being made by primarily well-intentioned data controllers employing DPbD
'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions
No full textCite as:
Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao and Nigel Shadbolt (2018) 'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions. ACM Conference on Human Factors in Computing Systems (CHI'18), April 21–26, Montreal, Canada. doi: 10.1145/3173574.3173951
Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
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only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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