Although artificial intelligence holds promise for addressing societ al challenges, issues of exactly which tasks to automate and the extent to do so remain understudied. We approach the problem of task delegability from a human-centered perspective by developing a framework on human perception of task delegation to artificial intelligence. We consider four high-level factors that can contribute to a delegation decision: motivation, difficulty, risk, and trust. To obtain an empirical understanding of human preferences in different tasks, we build a dataset of 100 tasks from academic papers, popular media portrayal of AI, and everyday life. For each task, we administer a survey to collect judgments of each factor and ask subjects to pick the extent to which they prefer AI involvement. We find little preference for full AI control and a strong preference for machine-in-the-loop designs, in which humans play the leading role. Our framework can effectively predict human preferences in degrees of AI assistance. Among the four factors, trust is the most predictive of human preferences of optimal human-machine delegation. This framework represents a first step towards characterizing human preferences of automation across tasks. We hope this work may encourage and aid in future efforts towards understanding such individual attitudes; our goal is to inform the public and the AI research community rather than dictating any direction in technology development.
sheridan-parasuraman2000model 's Levels of Automation is the closest to our work @cite_7 . However, their work is primarily concerned with performance-based criteria (e.g., capability, reliability, cost), while our primary interest involves human preferences.
@cite_7
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We outline a model for types and levels of automation that provides a framework and an objective basis for deciding which system functions should be automated and to what extent. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.