# Automating the Practice of Science -- Opportunities, Challenges, and Implications

arXiv: https://arxiv.org/abs/2409.05890 · HTML: https://mlsanity.com/paper/2409.05890/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2024.

## Built on by (most cited first, 18 of 18)

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- [Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist](https://mlsanity.com/paper/2606.26448/) (2026)
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- [Toward an automated science of the mind](https://mlsanity.com/paper/2603.20988/) (2026)
- [ATLAS: Active Theory Learning for Automated Science](https://mlsanity.com/paper/2606.12386/) (2026)
- [auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation](https://mlsanity.com/paper/2606.26460/) (2026)
- [From Understanding to Excelling: Template-Free Algorithm Design through Structural-Functional Co-Evolution](https://mlsanity.com/paper/2503.10721/) (2025)
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- [Active Inference AI Systems for Scientific Discovery](https://mlsanity.com/paper/2506.21329/) (2025)
- [Automated Adversarial Collaboration for Advancing Theory Building in the Cognitive Sciences](https://mlsanity.com/paper/2604.25521/) (2026)
- [TinyTim: A Family of Language Models for Divergent Generation](https://mlsanity.com/paper/2508.11607/) (2025)
- [MARBLE: Multi-Agent Reasoning for Bioinformatics Learning and Evolution](https://mlsanity.com/paper/2601.14349/) (2026)
- [Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components](https://mlsanity.com/paper/2605.24558/) (2026)
- [Rebuttals Move Peer-Review Scores, but Initial-Review Structure Bounds the Movement](https://mlsanity.com/paper/2606.22166/) (2026)
- [Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent](https://mlsanity.com/paper/2609.35400/) (2026)
- [Big-Big-SycoBench: A Benchmark to Detect Sycophancy Through Model Responses to Valid vs. Invalid Arguments](https://mlsanity.com/paper/2610.15634/) (2026)

## Abstract (arXiv)

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.