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Author Information
Hannah Korevaar (Meta Platforms)
Manish Raghavan (MIT)
Ashudeep Singh (Pinterest)
Fernando Diaz (Google)
Fernando Diaz is a research scientist at Google Brain Montréal. His research focuses on the design of information access systems, including search engines, music recommendation services and crisis response platforms is particularly interested in understanding and addressing the societal implications of artificial intelligence more generally. Previously, Fernando was the assistant managing director of Microsoft Research Montréal and a director of research at Spotify, where he helped establish its research organization on recommendation, search, and personalization. Fernando’s work has received awards at SIGIR, WSDM, ISCRAM, and ECIR. He is the recipient of the 2017 British Computer Society Karen Spärck Jones Award. Fernando has co-organized workshops and tutorials at SIGIR, WSDM, and WWW. He has also co-organized several NIST TREC initiatives, WSDM (2013), Strategic Workshop on Information Retrieval (2018), FAT* (2019), SIGIR (2021), and the CIFAR Workshop on Artificial Intelligence and the Curation of Culture (2019)
Chloé Bakalar
Alana Shine (Meta)
Alana is a research scientist at Meta working within the Responsible AI organization on fairness in recommender systems. Prior to coming to Responsible AI, she completed a Computer Science PhD under David Kempe at the University of Southern California working on generative graph models.
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2021 : Artsheets for Art Datasets »
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2022 : Exposure Fairness in Music Recommendation »
Rebecca Salganik · Fernando Diaz · Golnoosh Farnadi -
2022 : Striving for data-model efficiency: Identifying data externalities on group performance »
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2022 Workshop: Cultures of AI and AI for Culture »
Alex Hanna · Rida Qadri · Fernando Diaz · Nick Seaver · Morgan Scheuerman -
2022 : Q & A »
Hannah Korevaar · Manish Raghavan · Ashudeep Singh -
2022 Tutorial: Fair and Socially Responsible ML for Recommendations: Challenges and Perspectives »
Ashudeep Singh · Manish Raghavan · Hannah Korevaar -
2022 : Tutorial part 1 »
Hannah Korevaar · Manish Raghavan · Ashudeep Singh -
2021 Poster: Fairness in Ranking under Uncertainty »
Ashudeep Singh · David Kempe · Thorsten Joachims -
2020 Workshop: Algorithmic Fairness through the Lens of Causality and Interpretability »
Awa Dieng · Jessica Schrouff · Matt Kusner · Golnoosh Farnadi · Fernando Diaz -
2020 Tutorial: (Track2) Beyond Accuracy: Grounding Evaluation Metrics for Human-Machine Learning Systems Q&A »
Praveen Chandar · Fernando Diaz · Brian St. Thomas -
2020 Tutorial: (Track2) Beyond Accuracy: Grounding Evaluation Metrics for Human-Machine Learning Systems »
Praveen Chandar · Fernando Diaz · Brian St. Thomas -
2019 Poster: Policy Learning for Fairness in Ranking »
Ashudeep Singh · Thorsten Joachims -
2017 : Equality of Opportunity in Rankings »
Thorsten Joachims · Ashudeep Singh -
2016 Demonstration: Project Malmo - Minecraft for AI Research »
Katja Hofmann · Matthew A Johnson · Fernando Diaz · Alekh Agarwal · Tim Hutton · David Bignell · Evelyne Viegas