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Found 7 papers, 3 papers with code
Fair ranking: a critical review, challenges, and future directions
Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring.
Towards Fair Recommendation in Two-Sided Platforms
Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services.
Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrieval
Online platforms like Google and Yelp allow location-based search in the form of nearby feature to query for hotels or restaurants in the vicinity.
Two-Sided Fairness in Non-Personalised Recommendations
Analysing the results obtained from voting rule-based recommendation, we find that while the well-known voting rules are better from the user side, they show high bias values and clearly not suitable for organisational requirements of the platforms.
On Fair Virtual Conference Scheduling: Achieving Equitable Participant and Speaker Satisfaction
We show that the welfare and fairness objectives can be in conflict with each other, and there is a need to maintain a balance between these objective while caring for them simultaneously.
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Decision-making systems increasingly orchestrate our world: how to intervene on the algorithmic components to build fair and equitable systems is therefore a question of utmost importance; one that is substantially complicated by the context-dependent nature of fairness and discrimination.
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other.
