Not so Autonomous, Very Human Decisions in Machine Learning. AAAI Spring Symposium on UX for ML: Designing the User Experience of Machine Learning Systems.


Until the machines are fully autonomous and generate themselves, human design decisions affect Machine Learning outcomes every step of the way. This position paper outlines multiple stages at which design decisions affect machine learning outcomes, and how they interact. This includes: dataset curation and data pipelines, selection of optimization targets, and the designed dialogue with end-users with its implicit and explicit feedback mechanisms. We specifically also call out another user group that appears somewhat overlooked in the research literature – the data curators and editors often involved in selecting and annotating the data that machines learns from.


May 2024 | The Web Conference

Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks

Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou

May 2024 | The Web Conference (GFM workshop)

Towards Graph Foundation Models for Personalization

Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas

April 2024 | ICLR

In-context Exploration-Exploitation for Reinforcement Learning

Zhenwen Dai, Federico Tomasi, Sina Ghiassian