485 citations · 628 across the 9 of their papers we have counts for
10 papers
From Network Inequality to Network Fairness: A Perspective on Responsible Decision-Making
Lisette Espín-Noboa, Tina Eliassi-Rad, Pak-Hang Wong +5
Social networks shape how individuals make decisions and how opportunities are distributed. However, the mechanisms that generate these networks often reflect pre-existing inequali…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
Beyond Incompatibility: Trade-offs between Mutually Exclusive Fairness Criteria in Machine Learning and Law
Meike Zehlike, Alex Loosley, Håkan Jonsson +2
Fair and trustworthy AI is becoming ever more important in both machine learning and legal domains. One important consequence is that decision makers must seek to guarantee a 'fair…
Fair ranking: a critical review, challenges, and future directions
Gourab K Patro, Lorenzo Porcaro, Laura Mitchell +3
Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and…
Fairness in Ranking: A Survey
Meike Zehlike, Ke Yang, Julia Stoyanovich
In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, inf…
A Note on the Significance Adjustment for FA*IR with Two Protected Groups
Meike Zehlike, Tom Sühr, Carlos Castillo
In this report we provide an improvement of the significance adjustment from the FA*IR algorithm of Zehlike et al., which did not work for very short rankings in combination with a…
Towards a Flexible Framework for Algorithmic Fairness
Philip Hacker, Emil Wiedemann, Meike Zehlike
Increasingly, scholars seek to integrate legal and technological insights to combat bias in AI systems. In recent years, many different definitions for ensuring non-discrimination…
FairSearch: A Tool For Fairness in Ranked Search Results
Meike Zehlike, Tom Sühr, Carlos Castillo +1
Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dati…
Reducing Disparate Exposure in Ranking: A Learning To Rank Approach
Meike Zehlike, Carlos Castillo
Ranked search results have become the main mechanism by which we find content, products, places, and people online. Thus their ordering contributes not only to the satisfaction of…
FA*IR: A Fair Top-k Ranking Algorithm
Meike Zehlike, Francesco Bonchi, Carlos Castillo +3
In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n >> k candidates, maximizing utility…