When Should a Decision Maker Ignore the Advice of a Decision Aid?

TL;DR

This paper explores when decision makers should ignore decision aids, highlighting that reliance on fallible algorithms can sometimes worsen performance unless specific conditions are met.

Contribution

It identifies conditions where decision aids improve performance and warns against uncritical reliance on fallible algorithms in decision support systems.

Findings

Decision aids can impair performance if misused.

Certain conditions are necessary for decision aids to be beneficial.

Reliance on fallible algorithms requires careful consideration.

Abstract

This paper argues that the principal difference between decision aids and most other types of information systems is the greater reliance of decision aids on fallible algorithms--algorithms that sometimes generate incorrect advice. It is shown that interactive problem solving with a decision aid that is based on a fallible algorithm can easily result in aided performance which is poorer than unaided performance, even if the algorithm, by itself, performs significantly better than the unaided decision maker. This suggests that unless certain conditions are satisfied, using a decision aid as an aid is counterproductive. Some conditions under which a decision aid is best used as an aid are derived.