Towards Ethical AI: Empirically Investigating Dimensions of AI Ethics, Trust, and Performance in Human-AI Teaming

Abstract

Objective: Determining the efficacy of two trust repair strategies (apology and denial) for trust violations of an ethical nature by an autonomous teammate. Background: While ethics in human-AI interaction is extensively studied, little research has investigated how decisions with ethical implications impact trust and performance within human-AI teams and their subsequent repair. Method: Forty teams of two participants and one autonomous teammate completed three team missions within a synthetic task environment. The autonomous teammate made an ethical or unethical action during each mission, followed by an apology or denial. Measures of individual team trust, autonomous teammate trust, human teammate trust, perceived autonomous teammate ethicality, and team performance were taken. Results: Teams with unethical autonomous teammates had significantly lower trust in the team and trust in the autonomous teammate. Unethical autonomous teammates were also perceived as substantially more unethical. Neither trust repair strategy effectively restored trust after an ethical violation, and autonomous teammate ethicality was not related to the team score, but unethical autonomous teammates did have shorter times. Conclusion: Ethical violations significantly harm trust in the overall team and autonomous teammate but do not negatively impact team score. However, current trust repair strategies like apologies and denials appear ineffective in restoring trust after this type of violation. Application: This research highlights the need to develop trust repair strategies specific to human-AI teams and trust violations of an ethical nature.