Blame attribution in human-AI and human-only systems: Crowdsourcing judgments from Twitter

Matija Franklin, Trisevgeni Papakonstantinou, Tianshu Chen, Carlos Fernandez-Basso, David A. Lagnado

Abstract: Author(s): Franklin, Matija; Papakonstantinou, Trisevgeni; Chen, Tianshu; Fernandez-Basso, Carlos; Lagnado, David | Abstract: We introduce a novel methodology to scrutinize blame attributions in 'Tweets', focusing on Artificial Intelligence (AI) incidents - a contemporary issue that provokes regular discourse. The method identifies the agents that get blamed and the factors that are associated with blame attributions. The proposed methodology replicates and contextualizes findings from experimental settings, revealing AI entities are often held accountable for adverse outcomes, while human agents are judged based on intentions. It also identifies unexplored factors, such as blaming data for perceived biases or AI for replacing humans. This method offers a robust tool for mitigating measurement bias in specific fields, enabling the continual rejuvenation of theoretical frameworks with emerging variables.