Evaluation of Explainable Artificial Intelligence (EXplAIn) tackles the challenge of evaluating users interacting with ML-systems, aiming at developing a generic and integrative framework for evaluating human ML-system collaboration.
Funded by The Swedish Research Council (Etableringsbidrag, Vetenskapsrådet).
PI: Maria Riveiro
We live in a fascinating big data world, full of challenges, but also possibilities. Over the course of the next 20 years more will change around the way we do our daily activities than it has happened in the last 2000; we are entering an augmented age, where our natural capabilities are being augmented by AI technologies that help us think, make and be connected.
However, understanding how people interact with Machine Learning (ML) technologies is critical to design and evaluate systems that people can use effectively. Unfortunately, ML is often conceived in an impersonal way and ML algorithms are often perceived as black-boxes, which hinders their use and their full exploitation in our daily activities.
EXPLAIN tackles the challenge of evaluating users interacting with ML-systems. We argue that to be able to evaluate these interactive processes, we need to include theoretical principles from Cognitive Science that account for human preconceptions about systems' inner workings and behavior.
We develop a generic and integrative framework for evaluating human ML-system collaboration, combining traditional methods from ML and HCI with principles from Cognitive theories rarely considered in this interdisciplinary field.
The overall goal is to contribute to explaining our interactions with AI-technologies, moving forward towards more usable AI for augmented intelligence.
Project duration and funding
If you would like to know more about the project, please contact Maria Riveiro, firstname.lastname@example.org.
- Maria Riveiro wil join the Dagstuhl Seminar "Interactive Visualization for Fostering Trust in AI" in September 2020, Schloss Dagstuhl, Wadern, Germany.
- Maria Riveiro participated in the Dagstuhl Seminar “Machine Learning Meets Visualization to make AI Interpretable” in November 2019, Schloss Dagstuhl, Wadern, Germany. Summary report.
Riveiro, M., & Thill, S. (2021). “That's (not) the output I expected!” On the role of end user expectations in creating explanations of AI systems. Artificial Intelligence, Volume 298, 103507.
Riveiro, M. (2020). Explainable AI for maritime anomaly detection and autonomous driving. Dagstuhl Reports, 9(11), 29-30.
Thill, S., Riveiro, M. (2019). Memento hominibus: on the fundamental role of end users in real-world interactions with neuromorphic systems. Robust Artificial Intelligence for Neurorobotics, 26 – 28 August 2019, Edinburgh, Scotland.