Ain’t blaming you: Delegation of financial decisions to humans and algorithms

This article investigates the tendency to prioritize outcomes when evaluating decision-making processes, particularly in situations where choices are assigned to either a human or an algorithm. In our experiment, a Principal delegates a risky financial decision to an Agent, who can choose to act independently or to use an algorithm. The Principal then rewards or penalizes the Agent based on investment performance, while we manipulate the Principal’s knowledge of the outcome during the evaluation. Our results confirm a significant outcome bias, indicating that the assessment of decision effectiveness remains heavily influenced by results, whether the decision is made by the Agent or delegated to an algorithm. Furthermore, the Agent’s reliance on the algorithm and the level of investment risk do not change depending on whether rewards or penalties are decided before or after the outcome is known. • Outcome bias in financial decisions persists whether made by humans or algorithms. • Delegation to an algorithm does not significantly affect the outcome bias. • Delegation to an algorithm is as likely as firsthand choices. • Agents making firsthand decisions choose minimal risk and remain uninfluenced by the principal’s outcome knowledge. • The outcome bias is driven by rewards for successful outcomes and punishments for unsuccessful outcomes.