Table 4 Allocation principles by Persad et al.39, with the addition of autonomy, and corresponding rewards and mean weights assigned by participants of our post-questionnaire

From: Psychological, economic, and ethical factors in human feedback for a chatbot-based smoking cessation intervention

Allocation principles

Reward

Weight

Prognosis

Base reward: Prioritize people who will see the largest increase in effort because of the feedback

30.82%

Treating people equally (lottery, first-come, first-served, least amount of human feedback so far1)

First-come, first-served: Prioritize people who have spent the longest time since the last feedback

22.18%

Sickest first

Sickest first: Prioritize people who would spend the lowest effort without feedback

25.34%

Youngest first, instrumental value, reciprocity

Priority: Prioritize people with a higher individual characteristic-based priority

13.04%

Autonomy

Autonomy: Prioritize people who appreciate feedback the most

8.62%

  1. Note: We use first-come, first-served to illustrate the effect of treating people equally.
  2. 1 We supplemented the principles for treating people equally by Persad et al.39 with the principle of prioritizing people with the least amount of human feedback so far. The reason is that while Persad et al.39 focus on medical resources that can be allocated to each person only once, human feedback can in our context also be allocated more than once.
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