Algorithms that Remember: Model Inversion Attacks and Data Protection Law

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.Comment: 15 pages, 1 figur

Fortifying the algorithmic management provisions in the proposed Platform Work Directive

The European Commission proposed a Directive on Platform Work at the end of 2021. While much attention has been placed on its effort to address misclassification of the employed as self-employed, it also contains ambitious provisions for the regulation of the algorithmic management prevalent on these platforms. Overall, these provisions are well-drafted, yet they require extra scrutiny in light of the fierce lobbying and resistance they will likely encounter in the legislative process, in implementation and in enforcement. In this article, we place the proposal in its sociotechnical context, drawing upon wide cross-disciplinary scholarship to identify a range of tensions, potential misinterpretations, and perversions that should be pre-empted and guarded against at the earliest possible stage. These include improvements to ex ante and ex post algorithmic transparency; identifying and strengthening the standard against which human reviewers of algorithmic decisions review; anticipating challenges of representation and organising in complex platform contexts; creating realistic ambitions for digital worker communication channels; and accountably monitoring and evaluating impacts on workers while limiting data collection. We encourage legislators and regulators at both European and national levels to act to fortify these provisions in the negotiation of the Directive, its potential transposition, and in its enforcement

Some HCI Priorities for GDPR-Compliant Machine Learning

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.Comment: 8 pages, 0 figures, The General Data Protection Regulation: An Opportunity for the CHI Community? (CHI-GDPR 2018), Workshop at ACM CHI'18, 22 April 2018, Montreal, Canad

Fortifying the Algorithmic Management Provisions in the Proposed Platform Work Directive

The European Commission proposed a Directive on Platform Work at the end of 2021. While much attention has been placed on its effort to address misclassification of the employed as self-employed, it also contains ambitious provisions for the regulation of the algorithmic management prevalent on these platforms. Overall, these provisions are well-drafted, yet they require extra scrutiny in light of the fierce lobbying and resistance they will likely encounter in the legislative process, in implementation and in enforcement. In this article, we place the proposal in its sociotechnical context, drawing upon wide cross-disciplinary scholarship to identify a range of tensions, potential misinterpretations, and perversions that should be pre-empted and guarded against at the earliest possible stage. These include improvements to ex ante and ex post algorithmic transparency; identifying and strengthening the standard against which human reviewers of algorithmic decisions review; anticipating challenges of representation and organising in complex platform contexts; creating realistic ambitions for digital worker communication channels; and accountably monitoring and evaluating impacts on workers while limiting data collection. We encourage legislators and regulators at both European and national levels to act to fortify these provisions in the negotiation of the Directive, its potential transposition, and in its enforcement

Accurate Determination of Phenotypic Information from Historic Thoroughbred Horses by Single Base Extension

Historic DNA have the potential to identify phenotypic information otherwise invisible in the historical, archaeological and palaeontological record. In order to determine whether a single nucleotide polymorphism typing protocol based on single based extension (SNaPshot™) could produce reliable phenotypic data from historic samples, we genotyped three coat colour markers for a sample of historic Thoroughbred horses for which both phenotypic and correct geotypic information were known from pedigree information in the General Stud Book. Experimental results were consistent with the pedigrees in all cases. Thus we demonstrate that historic DNA techniques can produce reliable phenotypic information from museum specimens.© 2010 Campana et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Using short dietary questions to develop indicators of dietary behaviour for use in surveys exploring attitudinal and/or behavioural aspects of dietary choices

For countries where nutrition surveys are infrequent, there is a need to have some measure of healthful eating to plan and evaluate interventions. This study shows how it is possible to develop healthful eating indicators based on dietary guidelines from a cross sectional population survey. Adults 18 to 64 years answered questions about the type and amount of foods eaten the previous day, including fruit, vegetables, cereals, dairy, fish or meat and fluids. Scores were based on serves and types of food according to an established method. Factor analysis indicated two factors, confirmed by structural equation modeling: a recommended food healthful eating indicator (RF_HEI) and a discretionary food healthful eating indicator (DF_HEI). Both yield mean scores similar to an established dietary index validated against nutrient intake. Significant associations for the RF_HEI were education, income, ability to save, and attitude toward diet; and for the DF_HEI, gender, not living alone, living in a socially disadvantaged area, and attitude toward diet. The results confirm that short dietary questions can be used to develop healthful eating indicators against dietary recommendations. This will enable the exploration of dietary behaviours for “at risk” groups, such as those with excess weight, leading to more relevant interventions for populations

Energy Spectra of Elements with 18 ≤ Z ≤ 28 Between 10 and 300 GeV/amu

The HEAO-3 Heavy Nuclei Experiment (Binns, et al., 1981) is composed of ionization chambers above and below a plastic Cherenkov counter. We have measured the energy dependence of the abundances of elements with atomic number, Z, between 18 and 28 at very high energies where they are rare and thus need the large area x time of this experiment. We extend the measurements of the Danish French HEAO-3 experiment (Englemann, et al., 19S3) to higher energies, using the relativistic rise of ionization signal as a measure of energy, and determine source abundances for Ar and Ca