This paper investigates the application of artificial intelligence (AI) language models in writing policy briefing notes within the context of public administration by juxtaposing the technologies’ performance against the traditional reliance on human expertise. Briefing notes are pivotal in informing decision-making processes in government contexts, which generally require high accuracy, clarity, and issue-relevance. Given the increasing integration of AI across various sectors, this study aims to evaluate the effectiveness and acceptability of AI-generated policy briefing notes. Using a structured evaluation-by-experts methodology, (...) the research scrutinizes and compares the output of three leading AI language models—OpenAI’s ChatGPT, Google’s Gemini, and Mistral’s Le Chat—across ten critical dimensions reflective of policy briefing notes quality. These dimensions encompass both structural and content-related aspects, ranging from linguistic precision to the depth and applicability of the generated information. The discussion is anchored in the technology acceptance model (TAM) theory and its extensions, which offer a theoretical framework for understanding the factors influencing the adoption and usefulness of technology in public administration. Our comparative analysis reveals that while AI models exhibit notable competencies in meeting some structural and linguistic benchmarks, they fail to address the nuances and depth required by policy experts for undertaking informed decision-making adequately. This discrepancy underscores the enduring value of human expertise in synthesizing complex information and navigating ethical considerations, even as AI enhances efficiency in certain aspects of the policy briefing note crafting process. (shrink)

Artificial intelligence (AI) is often described as crucial for making healthcare safer and more efficient. However, some studies point in the opposite direction, demonstrating how biases in AI cause inequalities and discrimination. As a result, a growing body of research suggests mitigation measures to avoid gender bias. Typically, mitigation measures address various stakeholders such as the industry, academia, and policy-makers. To the author’s knowledge, these have not undergone sociological analysis. The article fills this gap and explores five examples of mitigation (...) measures designed to counteract gender bias in AI within the healthcare sector. The rapid development of AI in healthcare plays a crucial role globally and must refrain from creating or reinforcing inequality and discrimination. In this effort, mitigation measures to avoid gender bias in AI in healthcare are central tools and, therefore, essential to explore from a social science perspective, including sociology. Sociologists have made valuable contributions to studying inequalities and disparities in AI. However, research has pointed out that more engagement is needed, specifically regarding bias in AI. While acknowledging the importance of these measures, the article suggests that they lack accountable agents for implementation and overlook potential implementation barriers such as resistance, power relations, and knowledge hierarchies. Recognizing the conditions where the mitigation measures are to be implemented is essential for understanding the potential challenges that may arise. Consequently, more studies are needed to explore the practical implementation of mitigation measures from a social science perspective and a systematic review of mitigation measures. (shrink)