Unbiased Identification of Broadly Appealing Content Using a Pure Exploration Infinitely-Armed Bandit Strategy
Maryam Aziz, Jesse Anderton, Kevin Jamieson, Alice Wang, Hugues Bouchard, Javed Aslam
Calls to action to implement evaluation of fairness and bias into industry systems are increasing at a rapid rate. The research community has attempted to meet these demands by producing ethical principles and guidelines for AI, but few of these documents provide guidance on how to implement these principles in real world settings. Without readily available standardized and practice-tested approaches for evaluating fairness in recommendation systems, industry practitioners, who are often not experts, may easily run into challenges or implement metrics that are potentially poorly suited to their specific applications. When evaluating recommendations, practitioners are well aware they should evaluate their systems for unintended algorithmic harms, but the most important, and unanswered question, is how? In this talk, we will present practical challenges we encountered in addressing algorithmic responsibility in recommendation systems, which also present research opportunities for the RecSys community. This talk will focus on the steps that need to happen before bias mitigation can even begin.
Maryam Aziz, Jesse Anderton, Kevin Jamieson, Alice Wang, Hugues Bouchard, Javed Aslam
Dmitrii Moor, Yi Yuan, Rishabh Mehrotra, Zhenwen Dai, Mounia Lalmas
Enrico Palumbo, Andreas Damianou, Alice Wang, Alva Liu, Ghazal Fazelnia, Francesco Fabbri, Rui Ferreira, Fabrizio Silvestri, Hugues Bouchard, Claudia Hauff, Mounia Lalmas, Ben Carterette, Praveen Chandar, David Nyhan