Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?

Abstract

One realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society’s most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpetuate and exacerbate racial and gender biases. Yet, there is limited knowledge about the extent to which individuals might question AI-based recommendations when perceived as biased. To address this gap in knowledge, we investigate the effects of espoused national cultural values on AI questionability, by examining how individuals might question AI-based recommendations due to perceived racial or gender bias. Data collected from 387 survey respondents in the United States indicate that individuals with espoused national cultural values associated to collectivism, masculinity and uncertainty avoidance are more likely to question biased AI-based recommendations. This study advances understanding of how cultural values affect AI questionability due to perceived bias and it contributes to current academic discourse about the need to hold AI accountable.

AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context

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Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

“…XAI techniques can be used to address the emerging black box problem that characterizes modern AI models (Adadi & Berrada, 2018;Arrieta et al, 2020;Gunning & Aha, 2019;. We used XAI for justifying individual outcomes and for detecting potential errors or biases (Adadi & Berrada, 2018;Gupta et al, 2021). Research has shown that explainability features in DSSs can positively affect the users' satisfaction with the decision-making process (Li & Gregor, 2011).…”

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“…Confirmation bias occurs, when users encounter information that reinforces their pre-existing beliefs and attitudes (Bessi, 2016;Geschke et al, 2019;Zhao et al, 2020). Confirmation bias is defined as the tendency to interpret, search for, recall, and favour the information in a way that confirms one's pre-existing beliefs or hypotheses resulting into polarised views about an issue or event (Westerwick et al, 2017;Gupta et al, 2021). Confirmation biases indicate why a group of individuals with opposing views on a topic can view the same evidence.…”

“…In the context of this study, social media can induce polarisation in the form of echo chambers in global supply chains due to regional differences in terms of selecting, filtering, and sorting of information on social media (Flaxman et al, 2016;Miroudot, 2020). For example, echo chambers can occur as a global event unfolds (i.e., Covid-19, natural disaster) whereby supply chain participants are influenced by their professional and personal experiences (Bhagwat & Sharma, 2007;Muckstadt et al, 2001), as well as the information that is circulated and referred through social media (Gupta et al, 2021;Barberá et al, 2015;Toubiana & Zietsma, 2017). The impact of echo chambers on supply chain decision making can lead to excessive or insufficient stock levels in regional warehouses (Sharma et al, 2020).…”