Potential Ways to Detect Unfairness in HRI and to Re-establish Positive Group Dynamics

TL;DR

This paper explores methods to identify and mitigate biases in human-robot interactions by detecting microaggressions and social exclusion, aiming to promote fairness and positive group dynamics.

Contribution

It introduces computational models to detect biases and microaggressions in HRI, and proposes regulatory mechanisms to enhance fairness and social inclusion.

Findings

Proposed models for detecting microaggressions in HRI

Framework for promoting fairness and social inclusion

Insights into bias detection through human coping behaviors

Abstract

This paper focuses on the identification of different algorithm-based biases in robotic behaviour and their consequences in human-robot mixed groups. We propose to develop computational models to detect episodes of microaggression, discrimination, and social exclusion informed by a) observing human coping behaviours that are used to regain social inclusion and b) using system inherent information that reveal unequal treatment of human interactants. Based on this information we can start to develop regulatory mechanisms to promote fairness and social inclusion in HRI.