Human-Machine Collaboration for Smart Decision Making: Current Trends and Future Opportunities

Geng, Baocheng; Varshney, Pramod K.

doi:10.1109/cic56439.2022.00019

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2024

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Cited by 5 publications

References 66 publications

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A Model of Two-Parameter Competitive Assessment of the Effectiveness of a Complex Sensorimotor Reaction of a Computer Operator

Kovalenko,

Bondarenko,

Kupriyanov

et al. 2024

Lecture Notes in Networks and Systems

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A Model of Two-Parameter Competitive Assessment of the Effectiveness of a Complex Sensorimotor Reaction of a Computer Operator

Kovalenko,

Bondarenko,

Kupriyanov

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Human–machine collaboration for enhanced decision-making in governance

Van Rooy

2024

Data & Policy

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A detailed exploration is presented of the integration of human–machine collaboration in governance and policy decision-making, against the backdrop of increasing reliance on artificial intelligence (AI) and automation. This exploration focuses on the transformative potential of combining human cognitive strengths with machine computational capabilities, particularly emphasizing the varying levels of automation within this collaboration and their interaction with human cognitive biases. Central to the discussion is the concept of dual-process models, namely Type I and II thinking, and how these cognitive processes are influenced by the integration of AI systems in decision-making. An examination of the implications of these biases at different levels of automation is conducted, ranging from systems offering decision support to those operating fully autonomously. Challenges and opportunities presented by human–machine collaboration in governance are reviewed, with a focus on developing strategies to mitigate cognitive biases. Ultimately, a balanced approach to human–machine collaboration in governance is advocated, leveraging the strengths of both humans and machines while consciously addressing their respective limitations. This approach is vital for the development of governance systems that are both technologically advanced and cognitively attuned, leading to more informed and responsible decision-making.

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