Epistemic Virtues for Science in the Age of Automation

2026-04-14 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Research Methodology & Innovation, Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

A new initiative, inspired by Italo Calvino's "Six Memos for the Next Millennium," seeks to identify critical "epistemic virtues" for science in an era of increasing automation and AI. Jessica, Andrew, and Berna are collecting input from practicing scientists to determine which qualities of scientific character and practice should be preserved as AI advances. They have compiled a list of 27 candidate virtues, including Accountability, Curiosity, Reproducibility Seeking, and Transparency, each with a short description. Researchers are invited to rank their top six virtues and suggest any missing ones via a Google Forms survey. The goal is to gather broad participation from faculty, research scientists, postdocs, and senior Ph.D. students across all scientific disciplines, with results to be shared widely.

Key takeaway

For research scientists, postdocs, and senior Ph.D. students concerned about the impact of AI on scientific practice, your participation in this survey is vital. By contributing your perspective on critical epistemic virtues, you directly influence the discourse on maintaining scientific integrity and quality as automation advances. Take five minutes to complete the survey and help shape the future principles guiding scientific inquiry.

Key insights

Identifying core epistemic virtues is crucial for preserving scientific integrity amidst increasing automation.

Principles

• Epistemic virtues shape inquiry and evidence evaluation.
• Scientific character and practice are durable qualities.

Method

A survey solicits practicing scientists to rank 27 candidate epistemic virtues and suggest new ones, aiming for broad participation.

In practice

• Rank your top six epistemic virtues.
• Suggest additional virtues for consideration.

Topics

• Epistemic Virtues
• Science Automation
• Scientific Practice
• Researcher Survey
• AI Impact

Best for: AI Scientist, Research Scientist, AI Ethicist

Related on AIssential

• When Science Was Still Philosophy: The Questions Technical Answers Leave Behind — Technical scientific success often outpaces conceptual clarity, necessitating philosophical inquiry to examine underlying meanings and assumptions.
• How Far Are We From True Auto-Research? — Current auto-research agents generate polished papers but critically lack experimental rigor and result integrity.
• The Good Robot podcast: the role of designers in AI ethics with Tomasz Hollanek — Design is central to AI ethics, embedding values and requiring critical approaches for just human-AI interaction.
• The Relic Condition: When Published Scholarship Becomes Material for Its Own Replacement — Published scholarly work can be distilled into AI systems capable of performing complex academic functions at expert-level quality.
• The social sciences need tools for the 21st century | Letters — Social science needs new tools and methods to improve replicability and advance understanding of human behavior.
• AI ‘scientists’ promise to accelerate research — how do they work? — AI agents accelerate research, yet human involvement is essential for sound scientific practice.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.