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Vedder, Anton

doi:10.1023/a:1010016102284

Cited by 74 publications

(21 citation statements)

References 4 publications

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“…Models basing patterns on single data records are generally considered to have been undesirably overfitted-memorizing the training data rather than finding generalizable patterns. 8 Such a right becomes more useful when used collectively. Whether this is possible will depend on both the phenomena at hand and the level of co-ordination between the data subjects.…”

Section: (Ii) Erasure Rightsmentioning

confidence: 99%

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Algorithms that remember: model inversion attacks and data protection law

Veale

Binns

Edwards

2018

Phil. Trans. R. Soc. A.

130

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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 indicates 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.This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.

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Section: (Ii) Erasure Rightsmentioning

confidence: 99%

“…In data rather than finding generalizable patterns. 8 Such a right becomes more useful when used collectively. Whether this is possible will depend on both the phenomena at hand and the level of co-ordination between the data subjects.…”

Section: (Ii) Erasure Rightsmentioning

confidence: 99%

Algorithms that remember: model inversion attacks and data protection law

Veale

Binns

Edwards

2018

Phil. Trans. R. Soc. A.

130

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“…It becomes important for companies, governments, and individuals to differentiate information from noise to detect useful and interesting data. Therefore, we need to use technologies to analyze the collected data and explore the knowledge in that data [36]. The processes that are used for profiling include the following steps: − Initial stage: the profiling process starts with the specification of a problem domain and the identification of the purpose of analysis.…”

Section: Profiling and User Behaviormentioning

confidence: 99%

Cosine similarity-based algorithm for social networking recommendation

Al-Otaibi

Altwoijry

Alqahtani

et al. 2022

IJECE

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Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities.

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“…Human decisions can also be arbitrary, biased and subject to institutional processes that lead to variations and inequities in decision outcomes (Keddell 2014). Humans can also rely on invisible heuristics and pattern matching that can lead to decisions based on a cognitive categorisation process, rather than a humane, relational approach that considers each individual person (Vedder 1999). Heuristics can become similarly biased when based on experiences that reflect a 'skewed sample' of particular kinds of cases compared to the general population.…”

Section: Considering the Counter-argument: Problems In Human Child Prmentioning

confidence: 99%

Algorithmic Justice in Child Protection: Statistical Fairness, Social Justice and the Implications for Practice

Keddell¹

2019

Social Sciences

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Algorithmic tools are increasingly used in child protection decision-making. Fairness considerations of algorithmic tools usually focus on statistical fairness, but there are broader justice implications relating to the data used to construct source databases, and how algorithms are incorporated into complex sociotechnical decision-making contexts. This article explores how data that inform child protection algorithms are produced and relates this production to both traditional notions of statistical fairness and broader justice concepts. Predictive tools have a number of challenging problems in the child protection context, as the data that predictive tools draw on do not represent child abuse incidence across the population and child abuse itself is difficult to define, making key decisions that become data variable and subjective. Algorithms using these data have distorted feedback loops and can contain inequalities and biases. The challenge to justice concepts is that individual and group rights to non-discrimination become threatened as the algorithm itself becomes skewed, leading to inaccurate risk predictions drawing on spurious correlations. The right to be treated as an individual is threatened when statistical risk is based on a group categorisation, and the rights of families to understand and participate in the decisions made about them is difficult when they have not consented to data linkage, and the function of the algorithm is obscured by its complexity. The use of uninterpretable algorithmic tools may create ‘moral crumple zones’, where practitioners are held responsible for decisions even when they are partially determined by an algorithm. Many of these criticisms can also be levelled at human decision makers in the child protection system, but the reification of these processes within algorithms render their articulation even more difficult, and can diminish other important relational and ethical aims of social work practice.

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Untitled

Cited by 74 publications

References 4 publications

Algorithms that remember: model inversion attacks and data protection law

Algorithms that remember: model inversion attacks and data protection law

Cosine similarity-based algorithm for social networking recommendation

Algorithmic Justice in Child Protection: Statistical Fairness, Social Justice and the Implications for Practice

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