91 citations · 105 across the 8 of their papers we have counts for
9 papers
Reinforcement Learning for Durable Algorithmic Recourse
Marina Ceccon, Alessandro Fabris, Goran Radanović +2
Algorithmic recourse seeks to provide individuals with actionable recommendations that increase their chances of receiving favorable outcomes from automated decision systems (e.g.,…
Value Sensitive Design for Fair Online Recruitment: A Conceptual Framework Informed by Job Seekers' Fairness Concerns
Changyang He, Yue Deng, Alessandro Fabris +2
The susceptibility to biases and discrimination is a pressing issue in today's labor markets. While digital recruitment systems play an increasingly significant role in human resou…
Data Repurposing through Compatibility: A Computational Perspective
Asia J. Biega
Reuse of data in new contexts beyond the purposes for which it was originally collected has contributed to technological innovation and reducing the consent burden on data subjects…
Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
Alessandro Fabris, Nina Baranowska, Matthew J. Dennis +5
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and st…
Explainability as a Requirement for Hardware: Introducing Explainable Hardware (XHW)
Timo Speith, Julian Speith, Steffen Becker +3
In today's age of digital technology, ethical concerns regarding computing systems are increasing. While the focus of such concerns currently is on requirements for software, this…
Exposing Query Identification for Search Transparency
Ruohan Li, Jianxiang Li, Bhaskar Mitra +2
Search systems control the exposure of ranked content to searchers. In many cases, creators value not only the exposure of their content but, moreover, an understanding of the spec…
Overview of the TREC 2020 Fair Ranking Track
Asia J. Biega, Fernando Diaz, Michael D. Ekstrand +2
This paper provides an overview of the NIST TREC 2020 Fair Ranking track. For 2020, we again adopted an academic search task, where we have a corpus of academic article abstracts a…
Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems
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. While cont…
Overview of the TREC 2019 Fair Ranking Track
Asia J. Biega, Fernando Diaz, Michael D. Ekstrand +1
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 notio…