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

Manjul Gupta1, Carlos M Parra1, Denis Dennehy2

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National cultural values influence how people question biased AI recommendations. Collectivism, masculinity, and uncertainty avoidance increase AI questionability, highlighting the need for AI accountability.

Keywords:
Algorithmic biasArtificial intelligenceCultureEthical AIGender biasRacial biasRecommender systemsResponsible AI

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Area of Science:

  • Artificial Intelligence (AI)
  • Sociology
  • Human-Computer Interaction

Background:

  • AI recommender systems face scrutiny for perpetuating societal biases, particularly against vulnerable communities.
  • Existing research highlights AI's role in exacerbating racial and gender biases, yet understanding of user questioning behavior is limited.
  • Investigating the link between cultural values and AI bias perception is crucial for developing equitable AI.

Purpose of the Study:

  • To examine how espoused national cultural values impact individuals' propensity to question AI-based recommendations perceived as biased.
  • To explore the relationship between specific cultural values (collectivism, masculinity, uncertainty avoidance) and AI questionability regarding racial or gender bias.

Main Methods:

  • A survey was conducted with 387 respondents in the United States.
  • Data analysis focused on correlating espoused national cultural values with the likelihood of questioning biased AI recommendations.

Main Results:

  • Individuals endorsing cultural values of collectivism, masculinity, and uncertainty avoidance were more inclined to question AI recommendations flagged for bias.
  • Findings suggest a significant influence of national cultural values on user responses to perceived AI bias.

Conclusions:

  • Cultural values play a role in shaping AI questionability, particularly concerning perceived racial and gender biases.
  • This research contributes to the discourse on AI accountability by demonstrating how cultural dimensions affect user interaction with biased AI systems.