The spread of AI and black-box machine learning models makes 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 just published on Frontiers in Artificial Intelligence, we frame the explainability problem from the crowd’s point of view and engage both users and AI researchers through a gamified crowdsourcing framework. We research whether it’s possible to improve the crowd’s understanding of black-box models and the quality of the crowdsourced content by engaging users in gamified activities through a crowdsourcing framework called EXP-Crowd. While users engage in such activities, AI researchers organize and share AI- and explainability-related knowledge to educate users.
The next diagram shows the interaction flows of researchers (dashed cyan arrows) and users (orange plain arrows) with the activities devised within our framework. Researchers organize users’ knowledge and set up activities to collect data. As users engage with such activities, they provide Content to researchers. In turn, researchers give the user feedback about the activity they performed. Such feedback aims to improve users’ understanding of the activity itself, the knowledge, and the context provided within it.
In our recent paper published on Frontiers in Artificial Intelligence, 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.
One of the crucial steps in the process is the questions and annotation challenge, where Player 1 asks yes/no questions about the entity to be explained. Player 2 answers such questions, and then is asked to complete a series of simple tasks to identify the guessed feature by answering questions and potentially annotating the picture as shown below.
If you are interested in more details, you can read the full EXP-Crowd paper on the journal site (full open access):
You can cite the paper as:
Tocchetti A., Corti L., Brambilla M., and Celino I. (2022). EXP-Crowd: A Gamified Crowdsourcing Framework for Explainability. Frontiers in Artificial Intelligence 5:826499. doi: 10.3389/frai.2022.826499