<p>Artificial intelligence (AI) trained to predict psychiatric inpatient violence may overestimate risks for marginalized groups, making it critical to find ways to mitigate reliance on biased AI in this context. One potential solution is Cognitive forcing (CF), or interventions that delay AI information or slow the decision-making process. Benefits of CF may be modulated by traits, such as Need for Cognition (NFC), or the tendency to engage with complex, cognitive tasks. To examine how CF and NFC impact AI-assisted decision-making about violence risk, we conducted two experiments. In Experiment 1, participants (<i>n</i> = 281) made decisions about violence risk based on vignettes describing various patients experiencing mental health emergencies, and they were randomized to view biased or unbiased AI recommendations. In Experiment 2, participants (<i>n</i> = 373) made similar decisions, and they were randomized to view biased AI recommendations with one of three CF interventions or no CF. All participants completed measures of NFC. In both experiments, participants made biased decisions (overestimating violence risk for marginalized patients) when viewing biased AI recommendations. In Experiment 2, CF interventions did not mitigate this decision-making bias; however, participants reporting high NFC were less likely to make biased decisions when viewing biased AI recommendations, compared to those with low NFC. CF may not effectively safeguard against the impact of biased AI in high-stakes settings, like acute mental health care, or for decisions about violence risk prediction, which are fraught with social or racial stereotypes. However, trait NFC may mitigate reliance on biased AI information, highlighting a role of psychological factors. Further research is needed into various factors that promote equitable AI-assisted decision-making for mental health.</p>