Challenging the appearance of machine intelligence: Cognitive bias in LLMs and Best Practices for Adoption

Assessments of algorithmic bias in large language models (LLMs) are generally catered to uncovering systemic discrimination based on protected characteristics such as sex and ethnicity. However, there are over 180 documented cognitive biases that pervade human reasoning and decision making that are routinely ignored when discussing the ethical complexities of AI. We demonstrate the presence of these cognitive biases in LLMs and discuss the implications of using biased reasoning under the guise of expertise. We call for stronger education, risk management, and continued research as widespread adoption of this technology increases. Finally, we close with a set of best practices for when and how to employ this technology as widespread adoption continues to grow

Young adult perspectives on the selection of pharmaceuticals for mental health treatment

Shared decision making places an emphasis on patient understanding and engagement. However, when it comes to treatment selection, research tends to focus on how doctors select pharmaceutical treatments. The current study is a qualitative assessment of how patients choose among three common treatments that have varying degrees of scientific support and side effects. We used qualitative data from 157 undergraduates (44 males, 113 females; mean age = 21.89 years) that was collected as part of a larger correlational study of depression and critical thinking skills. Qualitative analysis revealed three major themes: shared versus independent decision making, confidence in the research and the drug, and cost and availability. Some participants preferred to rely on informal networks such as consumer testimonials while others expressed a false sense of security for over-the-counter treatments because they believe the drugs are regulated. Many indicated that they avoid seeking mental health services because of the time and money needed. The results indicate several factors influence selection of common depression treatments. Young adults indicate that when reading prescription information, they most often rely on perceptions including ease of access, price, and beliefs about drug regulations. General guidelines for treatment descriptions were created based on the qualitative analysis

Reference Dependence in Bayesian Reasoning

The purpose of this dissertation is to examine aspects of the representational and computational influences on Bayesian reasoning as they relate to reference dependence. Across three studies, I explored how dependence on the initial problem structure influences the ability to solve Bayesian reasoning tasks. Congruence between the problem and question of interest, response errors, and individual differences in numerical abilities was assessed. The most consistent and surprising finding in all three experiments was that people were much more likely to utilize the superordinate value as part of their solution rather than the anticipated reference class values. This resulted in a weakened effect of congruence, with relatively low accuracy even in congruent conditions, as well as a different pattern of response errors than what was anticipated. There was consistent and strong evidence of a value selection bias in that incorrect responses almost always conformed to values that were provided in the problem rather than errors related to computation. The one notable exception occurred when no organizing information was available in the problem, other than the instruction to consider a sample of the same size as that in the problem. In that case, participants were most apt to sum all of the subsets of the sample to yield the size of the original sample (N). In all three experiments, higher numerical skills were generally associated with higher accuracy, whether calculations were required or not

DS_10.1177_0272989X18758293 – Supplemental material for Improving Understanding of Diagnostic Test Outcomes

<p>Supplemental material, DS_10.1177_0272989X18758293 for Improving Understanding of Diagnostic Test Outcomes by Alaina N. Talboy and Sandra L. Schneider in Medical Decision Making</p