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When we approach a Machine Learning (ML) project where we want to solve a specific task with an existing or to-be-designed ML model, we immediately think about data: their availability always, their quantity often, their quality and mode of creation less frequently. If at all, it is only at the testing stage that we may attempt to better understand the dataset used to train the model, to identify the causes for the possible model failures and the improvements that can be made to the architecture or the training process. In so doing, we may come across biases in data representation, wrong labeling, uneven performance of the model. We may question whether the inductive biases the model exploits are indeed representative of the generalization capabilities we claim the model to have. We may think of failure, of data, of ethics. Let us unwrap and take a step back. To do so, in this series of three posts, we analyze and reflect on articles published recently in the ML research community.

This first post discusses some deployment failures of AI systems, and how these failures question the way we approach data to design ML systems. These failures have been analyzed in the article of Raji et al., The Fallacy of AI Functionality.

The second post will take a more holistic perspective on data creation and expectations we may place onto ML approaches, discussing the article of Paullada et al., Data and its (dis)contents: A survey of dataset development and use in machine learning research.

The third post will reflect on how we should question our ML education practices to contribute alleviating the current AI ethics crisis, analyzed in the article of Raji et al., You Can’t Sit With Us: Exclusionary Pedagogy in AI Ethics Education.