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Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI

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arxiv 2105.05424 v4 pith:ITG5L37U submitted 2021-05-12 cs.HC cs.AI

classification cs.HCcs.AI
keywords systemsprofessionalshcaiapproachdevelopinginteractiondevelopmenthuman
verification ladder T0 review T1 audit T2 compute T3 formal
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While AI has benefited humans, it may also harm humans if not appropriately developed. The focus of HCI work is transiting from conventional human interaction with non-AI computing systems to interaction with AI systems. We conducted a high-level literature review and a holistic analysis of current work in developing AI systems from an HCI perspective. Our review and analysis highlight the new changes introduced by AI technology and the new challenges that HCI professionals face when applying the human-centered AI (HCAI) approach in the development of AI systems. We also identified seven main issues in human interaction with AI systems, which HCI professionals did not encounter when developing non-AI computing systems. To further enable the implementation of the HCAI approach, we identified new HCI opportunities tied to specific HCAI-driven design goals to guide HCI professionals in addressing these new issues. Finally, our assessment of current HCI methods shows the limitations of these methods in support of developing AI systems. We propose alternative methods that can help overcome these limitations and effectively help HCI professionals apply the HCAI approach to the development of AI systems. We also offer strategic recommendations for HCI professionals to effectively influence the development of AI systems with the HCAI approach, eventually developing HCAI systems.

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  1. Advancing Responsible Innovation in Agentic AI: A study of Ethical Frameworks for Household Automation

    cs.AI 2025-07 conditional novelty 2.0 of 10

    A review that argues ethical principles for household agentic AI must be converted into concrete design patterns for tailored explainability, granular consent, and user override, especially for vulnerable groups.

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