  While artificial intelligence (AI) holds promise for addressing societal
challenges, issues of exactly which tasks to automate and to what extent to do
so remain understudied. We approach this problem of task delegability from a
human-centered perspective by developing a framework on human perception of
task delegation to AI. 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, and administer a survey based on our proposed framework. 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. Among the
four factors, trust is the most correlated with human preferences of optimal
human-machine delegation. This framework represents a first step towards
characterizing human preferences of AI automation across tasks. We hope this
work encourages 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.
