Game With A Purpose Research Articles

Eye into AI: Evaluating the Interpretability of Explainable AI Techniques through a Game with a Purpose

Recent developments in explainable AI (XAI) aim to improve the transparency of black-box models. However, empirically evaluating the interpretability of these XAI techniques is still an open challenge. The most common evaluation method is algorithmic performance, but such an approach may not accurately represent how interpretable these techniques are to people. A less common but growing evaluation strategy is to leverage crowd-workers to provide feedback on multiple XAI techniques to compare them. However, these tasks often feel like work and may limit participation. We propose a novel, playful, human-centered method for evaluating XAI techniques: a Game With a Purpose (GWAP), Eye into AI, that allows researchers to collect human evaluations of XAI at scale. We provide an empirical study demonstrating how our GWAP supports evaluating and comparing the agreement between three popular XAI techniques (LIME, Grad-CAM, and Feature Visualization) and humans, as well as evaluating and comparing the interpretability of those three XAI techniques applied to a deep learning model for image classification. The data collected from Eye into AI offers convincing evidence that GWAPs can be used to evaluate and compare XAI techniques. Eye into AI is available to the public: https://dig.cmu.edu/eyeintoai/.

EXP-Crowd: A Gamified Crowdsourcing Framework for Explainability.

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.