# Accelerating science with human-aware artificial intelligence

arXiv: https://arxiv.org/abs/2306.01495 · HTML: https://mlsanity.com/paper/2306.01495/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://mlsanity.com/paper/2005.03675/) (2020)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [Random Walks on Hypergraphs with Edge-Dependent Vertex Weights](https://mlsanity.com/paper/1905.08287/) (2019)
- [Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19](https://mlsanity.com/paper/2004.07229/) (2020)
- [Building a PubMed knowledge graph](https://mlsanity.com/paper/2005.04308/) (2020)
- [Detecting signal from science:The structure of research communities and prior knowledge improves prediction of genetic regulatory experiments](https://mlsanity.com/paper/2008.09985/) (2020)
- [Complementary artificial intelligence designed to augment human discovery](https://mlsanity.com/paper/2207.00902/) (2022)

## Built on by (most cited first, 28 of 28)

- [LLM Voting: Human Choices and AI Collective Decision Making](https://mlsanity.com/paper/2402.01766/) (2024)
- [Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents](https://mlsanity.com/paper/2502.16069/) (2025)
- [How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment](https://mlsanity.com/paper/2401.13481/) (2024)
- [Forecasting high-impact research topics via machine learning on evolving knowledge graphs](https://mlsanity.com/paper/2402.08640/) (2024)
- [Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders](https://mlsanity.com/paper/2405.17044/) (2024)
- [OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists](https://mlsanity.com/paper/2511.16931/) (2025)
- [Empowering Scientific Workflows with Federated Agents](https://mlsanity.com/paper/2505.05428/) (2025)
- [Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation](https://mlsanity.com/paper/2402.12590/) (2024)
- [Scientific Hypothesis Generation by a Large Language Model: Laboratory Validation in Breast Cancer Treatment](https://mlsanity.com/paper/2405.12258/) (2024)
- [From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models](https://mlsanity.com/paper/2407.09502/) (2024)
- [Deep Ideation: Designing LLM Agents to Generate Novel Research Ideas on Scientific Concept Network](https://mlsanity.com/paper/2511.02238/) (2025)
- [A Multi-Task Evaluation of LLMs' Processing of Academic Text Input](https://mlsanity.com/paper/2508.11779/) (2025)
- [Static network structure cannot stabilize cooperation among Large Language Model agents](https://mlsanity.com/paper/2411.10294/) (2024)
- [Discovering emergent connections in quantum physics research via dynamic word embeddings](https://mlsanity.com/paper/2411.06577/) (2024)
- [Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures](https://mlsanity.com/paper/2501.09025/) (2025)
- [Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing](https://mlsanity.com/paper/2511.12529/) (2025)
- [Alien Recombination: Exploring Concept Blends Beyond Human Cognitive Availability in Visual Art](https://mlsanity.com/paper/2411.11494/) (2024)
- [Big Data and the Computational Social Science of Entrepreneurship and Innovation](https://mlsanity.com/paper/2505.08706/) (2025)
- [Topic Modeling and Link-Prediction for Material Property Discovery](https://mlsanity.com/paper/2507.06139/) (2025)
- [Discovery Learning accelerates battery design evaluation](https://mlsanity.com/paper/2508.06985/) (2025)
- [Artificially intelligent agents in the social and behavioral sciences: A history and outlook](https://mlsanity.com/paper/2510.05743/) (2025)
- [Cultural Alien Sampler: Open-ended art generation balancing originality and coherence](https://mlsanity.com/paper/2510.20849/) (2025)
- [When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge](https://mlsanity.com/paper/2605.06033/) (2026)
- [Discovery by Dreaming: Cross-Domain Recombination in Artificial Memory](https://mlsanity.com/paper/2607.16256/) (2026)
- [AI-Integrated Scientific Inquiry: A Practice-Centered Vision for Science Education](https://mlsanity.com/paper/2607.21777/) (2026)
- [Collective creativity in hybrid societies](https://mlsanity.com/paper/2609.02620/) (2026)
- [Doc2LoRA Provides Decodable Representations of Scientific Ideas](https://mlsanity.com/paper/2609.38374/) (2026)
- [Historical Anchors for Scientific Forecasting on Human-Aware Knowledge Graphs](https://mlsanity.com/paper/2610.14023/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of discovery. Here we show that incorporating the distribution of human expertise by training unsupervised models on simulated inferences cognitively accessible to experts dramatically improves (up to 400%) AI prediction of future discoveries beyond those focused on research content alone, especially when relevant literature is sparse. These models succeed by predicting human predictions and the scientists who will make them. By tuning human-aware AI to avoid the crowd, we can generate scientifically promising "alien" hypotheses unlikely to be imagined or pursued without intervention until the distant future, which hold promise to punctuate scientific advance beyond questions currently pursued. Accelerating human discovery or probing its blind spots, human-aware AI enables us to move toward and beyond the contemporary scientific frontier.