Susceptibility to algorithmic recommendations in simulated jury decision making
Mario Álvarez, Helena MatuteAbstract
Introduction
AI decision‐support systems have become increasingly integrated into judicial contexts. This research asks about their influence on human judgements, particularly when they are erroneous or biased.
Method
Experiment 1 examined whether algorithmic recommendations conflicting with the evidence previously presented through standardized testimonies could change the judgements of participants acting as mock jurors. Experiment 2 replicated Experiment 1 and warned participants about potential AI errors.
Results
Experiment 1 showed that participants’ judgements shifted in the direction suggested by the algorithmic recommendation. Experiment 2 replicated the findings of Experiment 1 and further showed that warning participants about potential AI errors and biases reduced algorithmic influence, particularly when the algorithm suggested guilt and the testimonies suggested innocence.
Discussion
These findings highlight the importance of understanding how algorithmic recommendations influence legal decision‐making and how excessive trust in AI recommendations could be reduced.
Data
The data supporting the findings of this study are openly available in the Open Science Framework at