Search Results for author: Asia J. Biega

Found 10 papers, 0 papers with code

(Re)Politicizing Digital Well-Being: Beyond User Engagements

no code implementations15 Mar 2022 Niall Docherty, Asia J. Biega

The psychological costs of the attention economy are often considered through the binary of harmful design and healthy use, with digital well-being chiefly characterised as a matter of personal responsibility.

Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems

no code implementations15 Jan 2021 Asia J. Biega, Michèle Finck

This paper determines whether the two core data protection principles of data minimisation and purpose limitation can be meaningfully implemented in data-driven systems.

Decision Making Fairness

Operationalizing the Legal Principle of Data Minimization for Personalization

no code implementations28 May 2020 Asia J. Biega, Peter Potash, Hal Daumé III, Fernando Diaz, Michèle Finck

Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed (`data minimisation')".

Recommendation Systems

Evaluating Stochastic Rankings with Expected Exposure

no code implementations27 Apr 2020 Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette

We introduce the concept of \emph{expected exposure} as the average attention ranked items receive from users over repeated samples of the same query.

Information Retrieval Retrieval

Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions

no code implementations4 Apr 2020 Asia J. Biega, Jana Schmidt, Rishiraj Saha Roy

Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood.

Community Question Answering

Overview of the TREC 2019 Fair Ranking Track

no code implementations25 Mar 2020 Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, Sebastian Kohlmeier

The goal of the TREC Fair Ranking track was to develop a benchmark for evaluating retrieval systems in terms of fairness to different content providers in addition to classic notions of relevance.

Fairness Retrieval

Equity of Attention: Amortizing Individual Fairness in Rankings

no code implementations4 May 2018 Asia J. Biega, Krishna P. Gummadi, Gerhard Weikum

We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program.

Fairness Recommendation Systems

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