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