human-judgment

The article argues that AI cannot compensate for an enterprise's lack of data understanding, emphasizing the critical role of human judgment and context in transforming data into informed decisions.

A conversation is highlighted where John Schulman discusses with Dwarkesh about the role of human judgment in advancing AI, focusing on handling real-world tasks and specifying desired outcomes.

The author reflects on whether human involvement in AI's 'last mile' has shifted since the 2019 book 'Ghost Work', exploring current unique roles of human judgment and its potential evolution.

This paper introduces UPHELD, a large benchmark for evaluating human-scale conversational ability in LLMs, and proposes a Mixture-of-Judges framework that improves correlation with human assessments by approximately 30%.

This paper investigates the role of citations in human and LLM preferences for scientific question answering, finding that humans prefer diverse citations but fewer overall, while LLMs exhibit stronger citation-related preferences despite lacking source access.

This paper evaluates traditional coherence metrics and LLM-based semantic similarity for dynamic topic models, finding that LLM-based metrics better align with human judgments by accounting for lexical changes. It advocates for a combined evaluation approach using both traditional and LLM-based measures.

Pascal Bornet discusses the paradox that as AI improves decision-making, humans may lose the opportunity to develop judgment and expertise, urging leaders to design deliberate practices for human decision-making.

A philosophical essay arguing that the critical question about AI usage is not whether AI was used, but who made the decisions and provided direction, emphasizing human judgment over tool use.

The article argues that the real shift from AI is not job replacement but a change in who makes decisions, emphasizing the growing importance of human judgment and supervision over AI systems.

The author argues that the key question about AI-generated content should not be 'did AI write this?' but rather what human judgment and taste were applied before output, emphasizing the need for context, evidence, and editorial decision-making.

This opinion article argues that AI is forcing education to shift from credential-based learning to developing human judgment, curiosity, and critical thinking, making timeless methods like Socratic questioning and the Feynman Technique newly relevant.

Engineers across database, iOS, frontend, data engineering, and backend domains discuss how AI code generation shifts the hard part to verification and integration, requiring human judgment for subtle risks and architectural fit.

Microsoft's 2026 Future of Work report indicates that generative AI is reshaping the workplace at an unprecedented pace, but the benefits are highly unevenly distributed, with junior roles hit hardest; AI is evolving from an acceleration tool to a collaboration partner, making human professional judgment even more crucial.

A commentary arguing that the focus should be on whether content contains original thinking rather than whether AI was used, emphasizing that tools do not replace human judgment.

An article exploring the difference between using AI as a tool to enhance thinking versus becoming overly dependent on AI, emphasizing the importance of maintaining human critical thinking and judgment.