Autonomous driving paper index
How Personal and Cognitive Traits Influence AI Attitudes, Ethics, and Well-being: The Role of AI Autonomy and Criticality in Design
One-line summary
Abstract As artificial intelligence (AI) systems become increasingly prevalent in decision-making, it’s essential to understand public perceptions in various contexts and cultures for responsible use.
Engineering notes
Key topics: autonomous driving, perception, control. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Abstract As artificial intelligence (AI) systems become increasingly prevalent in decision-making, it’s essential to understand public perceptions in various contexts and cultures for responsible use. This study examines the relationship between acceptance and fear of AI, considering factors such as AI autonomy, task criticality, personality traits, need for cognition (NFC), faith in intuition (FI), locus of control (LOC), and cultural background (UK vs. Arab countries). Using the IMPACT framework, we examined the interplay between AI design modalities (automation and criticality) and individual differences in relation to perceived well-being and ethical concerns. A total of 639 participants (316 UK and 323 Arab) took part in an online survey that used scenarios to describe four types of AI: low automation/low criticality (LL), high automation/low criticality (HL), low automation/high criticality (LH), and high automation/high criticality (HH). In both cultural groups, higher perceived well-being was consistently associated with greater acceptance of AI, while ethical concerns were associated with increased fear of AI. UK participants reported the lowest well-being in the LH design, while Arab participants rated their well-being higher in both LH and HH, indicating a greater acceptance of AI autonomy in important situations. Ethical concerns were highest in the HH design for both groups. Psychological traits had some effects that varied by design. Agreeableness predicted higher well-being in both samples, while other traits, such as extraversion, openness, neuroticism, NFC, FI, and LOC, had smaller or situation-dependent effects. In the LH design, NFC and internal LOC were important predictors of well-being and ethical evaluations, especially among Arab participants. Overall, attitudes toward AI were mainly influenced by design and cultural context, while cognitive and control-related traits showed more specific influences.
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