Abstract

Placing a human in the loop may abate the risks of deploying AI systems in safety-critical settings (e.g., a clinician working with a medical AI system). However, mitigating risks arising from human error and uncertainty within such human-AI interactions is an important and understudied issue. In this work, we study human uncertainty in the context of concept-based models, a family of AI systems that enable human feedback via concept interventions where an expert intervenes on human-interpretable concepts relevant to the task. Prior work in this space often assumes that humans are oracles who are always certain and correct. Yet, real-world decision-making by humans is prone to occasional mistakes and uncertainty. We study how existing concept-based models deal with uncertain interventions from humans using two novel datasets: UMNIST, a visual dataset with controlled simulated uncertainty based on the MNIST dataset, and CUB-S, a relabeling of the popular CUB concept dataset with rich, densely-annotated soft labels from humans. We show that training with uncertain concept labels may help mitigate weaknesses of concept-based systems when handling uncertain interventions. These results allow us to identify several open challenges, which we argue can be tackled through future multidisciplinary research on building interactive uncertainty-aware systems. To facilitate further research, we release a new elicitation platform, UElic, to collect uncertain feedback from humans in collaborative prediction tasks. 

Citing Us

Please cite us using the following BibTex entry if you use our data, code, or elicitation interface. Thank you :) 

@inproceedings{collins2023humanConceptUnc,

      title={Human Uncertainty in Concept-Based AI Systems}, 

      author={Katherine M. Collins and Matthew Barker and Mateo Espinosa Zarlenga and Naveen Raman and Umang Bhatt and Mateja Jamnik and Ilia Sucholutsky and Adrian Weller and Krishnamurthy Dvijotham},

      year={2023},

      archivePrefix={AIES},

}

Contact: kmc61@cam.ac.uk