Cognitive Bias Detection Using Advanced Prompt Engineering

Cognitive biases, systematic deviations from rationality in judgment, pose significant challenges in generating objective content. This paper introduces a novel approach for real-time cognitive bias detection in user-generated text using large language models (LLMs) and advanced prompt engineering techniques. The proposed system analyzes textual data to identify common cognitive biases such as confirmation bias, circular reasoning, and hidden assumption. By designing tailored prompts, the system effectively leverages LLMs' capabilities to both recognize and mitigate these biases, improving the quality of human-generated content (e.g., news, media, reports). Experimental results demonstrate the high accuracy of our approach in identifying cognitive biases, offering a valuable tool for enhancing content objectivity and reducing the risks of biased decisionmaking. Introduction Cognitive biases are systematic patterns of deviation from rational judgment, affecting decision-making processes across various domains, including media, policy-making, and legal reasoning. With the rapid expansion of artificial intelligence (AI) applications, large language models (LLMs) have demonstrated significant potential in processing and evaluating vast amounts of textual information. However, existing research has largely focused on mitigating biases within AI-generated outputs rather than leveraging AI to detect biases in human-generated content. This gap presents a critical challenge in ensuring transparency and fairness in AI-assisted decision-making. This study explores the application of structured prompt engineering as a novel approach to improving LLM accuracy in detecting cognitive biases.