no code implementations • 25 Sep 2023 • Alessandro Fabris, Nina Baranowska, Matthew J. Dennis, Philipp Hacker, Jorge Saldivar, Frederik Zuiderveen Borgesius, Asia J. Biega
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
no code implementations • 15 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.
no code implementations • 14 Oct 2021 • Ruohan Li, Jianxiang Li, Bhaskar Mitra, Fernando Diaz, Asia J. Biega
Search systems control the exposure of ranked content to searchers.
no code implementations • 11 Aug 2021 • Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, Sergey Feldman, Sebastian Kohlmeier
This paper provides an overview of the NIST TREC 2020 Fair Ranking track.
no code implementations • 15 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.
no code implementations • 28 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')".
no code implementations • 27 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.
no code implementations • 4 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.
no code implementations • 25 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.
no code implementations • 4 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.