EXP-Crowd: A Gamified Crowdsourcing Framework for Explainability

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Abstract

The spread of AI and black-box machine learning models made it necessary to explain their behavior. Consequently, the research field of Explainable AI was born. The main objective of an Explainable AI system is to be understood by a human as the final beneficiary of the model. In our research, we frame the explainability problem from the crowds point of view and engage both users and AI researchers through a gamified crowdsourcing framework. We research whether it's possible to improve the crowds understanding of black-box models and the quality of the crowdsourced content by engaging users in a set of gamified activities through a gamified crowdsourcing framework named EXP-Crowd. While users engage in such activities, AI researchers organize and share AI- and explainability-related knowledge to educate users. We present the preliminary design of a game with a purpose (G.W.A.P.) to collect features describing real-world entities which can be used for explainability purposes. Future works will concretise and improve the current design of the framework to cover specific explainability-related needs.

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Tocchetti, A., Corti, L., Brambilla, M., & Celino, I. (2022). EXP-Crowd: A Gamified Crowdsourcing Framework for Explainability. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.826499

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