Artificial intelligence and illusions of understanding in scientific research – Nature.com

Crabtree, G. Self-driving laboratories coming of age. Joule 4, 25382541 (2020).

Article CAS Google Scholar

Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 4760 (2023). This review explores how AI can be incorporated across the research pipeline, drawing from a wide range of scientific disciplines.

Article CAS PubMed Google Scholar

Dillion, D., Tandon, N., Gu, Y. & Gray, K. Can AI language models replace human participants? Trends Cogn. Sci. 27, 597600 (2023).

Article PubMed Google Scholar

Grossmann, I. et al. AI and the transformation of social science research. Science 380, 11081109 (2023). This forward-looking article proposes a variety of ways to incorporate generative AI into social-sciences research.

Article CAS PubMed Google Scholar

Gil, Y. Will AI write scientific papers in the future? AI Mag. 42, 315 (2022).

Google Scholar

Kitano, H. Nobel Turing Challenge: creating the engine for scientific discovery. npj Syst. Biol. Appl. 7, 29 (2021).

Article PubMed PubMed Central Google Scholar

Benjamin, R. Race After Technology: Abolitionist Tools for the New Jim Code (Oxford Univ. Press, 2020). This book examines how social norms about race become embedded in technologies, even those that are focused on providing good societal outcomes.

Broussard, M. More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech (MIT Press, 2023).

Noble, S. U. Algorithms of Oppression: How Search Engines Reinforce Racism (New York Univ. Press, 2018).

Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. On the dangers of stochastic parrots: can language models be too big? in Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 610623 (Association for Computing Machinery, 2021). One of the first comprehensive critiques of large language models, this article draws attention to a host of issues that ought to be considered before taking up such tools.

Crawford, K. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence (Yale Univ. Press, 2021).

Johnson, D. G. & Verdicchio, M. Reframing AI discourse. Minds Mach. 27, 575590 (2017).

Article Google Scholar

Atanasoski, N. & Vora, K. Surrogate Humanity: Race, Robots, and the Politics of Technological Futures (Duke Univ. Press, 2019).

Mitchell, M. & Krakauer, D. C. The debate over understanding in AIs large language models. Proc. Natl Acad. Sci. USA 120, e2215907120 (2023).

Article PubMed PubMed Central Google Scholar

Kidd, C. & Birhane, A. How AI can distort human beliefs. Science 380, 12221223 (2023).

Article CAS PubMed Google Scholar

Birhane, A., Kasirzadeh, A., Leslie, D. & Wachter, S. Science in the age of large language models. Nat. Rev. Phys. 5, 277280 (2023).

Article Google Scholar

Kapoor, S. & Narayanan, A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4, 100804 (2023).

Article PubMed PubMed Central Google Scholar

Hullman, J., Kapoor, S., Nanayakkara, P., Gelman, A. & Narayanan, A. The worst of both worlds: a comparative analysis of errors in learning from data in psychology and machine learning. In Proc. 2022 AAAI/ACM Conference on AI, Ethics, and Society (eds Conitzer, V. et al.) 335348 (Association for Computing Machinery, 2022).

Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206215 (2019). This paper articulates the problems with attempting to explain AI systems that lack interpretability, and advocates for building interpretable models instead.

Article PubMed PubMed Central Google Scholar

Crockett, M. J., Bai, X., Kapoor, S., Messeri, L. & Narayanan, A. The limitations of machine learning models for predicting scientific replicability. Proc. Natl Acad. Sci. USA 120, e2307596120 (2023).

Article CAS PubMed PubMed Central Google Scholar

Lazar, S. & Nelson, A. AI safety on whose terms? Science 381, 138 (2023).

Article PubMed Google Scholar

Collingridge, D. The Social Control of Technology (St Martins Press, 1980).

Wagner, G., Lukyanenko, R. & Par, G. Artificial intelligence and the conduct of literature reviews. J. Inf. Technol. 37, 209226 (2022).

Article Google Scholar

Hutson, M. Artificial-intelligence tools aim to tame the coronavirus literature. Nature https://doi.org/10.1038/d41586-020-01733-7 (2020).

Article PubMed Google Scholar

Haas, Q. et al. Utilizing artificial intelligence to manage COVID-19 scientific evidence torrent with Risklick AI: a critical tool for pharmacology and therapy development. Pharmacology 106, 244253 (2021).

Article CAS PubMed Google Scholar

Mller, H., Pachnanda, S., Pahl, F. & Rosenqvist, C. The application of artificial intelligence on different types of literature reviews a comparative study. In 2022 International Conference on Applied Artificial Intelligence (ICAPAI) https://doi.org/10.1109/ICAPAI55158.2022.9801564 (Institute of Electrical and Electronics Engineers, 2022).

van Dinter, R., Tekinerdogan, B. & Catal, C. Automation of systematic literature reviews: a systematic literature review. Inf. Softw. Technol. 136, 106589 (2021).

Article Google Scholar

Aydn, . & Karaarslan, E. OpenAI ChatGPT generated literature review: digital twin in healthcare. In Emerging Computer Technologies 2 (ed. Aydn, .) 2231 (zmir Akademi Dernegi, 2022).

AlQuraishi, M. AlphaFold at CASP13. Bioinformatics 35, 48624865 (2019).

Article CAS PubMed PubMed Central Google Scholar

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583589 (2021).

Article CAS PubMed PubMed Central Google Scholar

Lee, J. S., Kim, J. & Kim, P. M. Score-based generative modeling for de novo protein design. Nat. Computat. Sci. 3, 382392 (2023).

Article CAS Google Scholar

Gmez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15, 11201127 (2016).

Article PubMed Google Scholar

Krenn, M. et al. On scientific understanding with artificial intelligence. Nat. Rev. Phys. 4, 761769 (2022).

Article PubMed PubMed Central Google Scholar

Extance, A. How AI technology can tame the scientific literature. Nature 561, 273274 (2018).

Article CAS PubMed Google Scholar

Hastings, J. AI for Scientific Discovery (CRC Press, 2023). This book reviews current and future incorporation of AI into the scientific research pipeline.

Ahmed, A. et al. The future of academic publishing. Nat. Hum. Behav. 7, 10211026 (2023).

Article PubMed Google Scholar

Gray, K., Yam, K. C., ZhenAn, A. E., Wilbanks, D. & Waytz, A. The psychology of robots and artificial intelligence. In The Handbook of Social Psychology (eds Gilbert, D. et al.) (in the press).

Argyle, L. P. et al. Out of one, many: using language models to simulate human samples. Polit. Anal. 31, 337351 (2023).

Article Google Scholar

Aher, G., Arriaga, R. I. & Kalai, A. T. Using large language models to simulate multiple humans and replicate human subject studies. In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) 337371 (JMLR.org, 2023).

Binz, M. & Schulz, E. Using cognitive psychology to understand GPT-3. Proc. Natl Acad. Sci. USA 120, e2218523120 (2023).

Article CAS PubMed PubMed Central Google Scholar

Ornstein, J. T., Blasingame, E. N. & Truscott, J. S. How to train your stochastic parrot: large language models for political texts. Github, https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf (2023).

He, S. et al. Learning to predict the cosmological structure formation. Proc. Natl Acad. Sci. USA 116, 1382513832 (2019).

Article MathSciNet CAS PubMed PubMed Central Google Scholar

Mahmood, F. et al. Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Trans. Med. Imaging 39, 32573267 (2020).

Article PubMed PubMed Central Google Scholar

Teixeira, B. et al. Generating synthetic X-ray images of a person from the surface geometry. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 90599067 (Institute of Electrical and Electronics Engineers, 2018).

Marouf, M. et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 11, 166 (2020).

Article CAS PubMed PubMed Central Google Scholar

Watts, D. J. A twenty-first century science. Nature 445, 489 (2007).

Article CAS PubMed Google Scholar

boyd, d. & Crawford, K. Critical questions for big data. Inf. Commun. Soc. 15, 662679 (2012). This article assesses the ethical and epistemic implications of scientific and societal moves towards big data and provides a parallel case study for thinking about the risks of artificial intelligence.

Article Google Scholar

Jolly, E. & Chang, L. J. The Flatland fallacy: moving beyond lowdimensional thinking. Top. Cogn. Sci. 11, 433454 (2019).

Article PubMed Google Scholar

Yarkoni, T. & Westfall, J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12, 11001122 (2017).

Article PubMed PubMed Central Google Scholar

Radivojac, P. et al. A large-scale evaluation of computational protein function prediction. Nat. Methods 10, 221227 (2013).

Article CAS PubMed PubMed Central Google Scholar

Bileschi, M. L. et al. Using deep learning to annotate the protein universe. Nat. Biotechnol. 40, 932937 (2022).

Article CAS PubMed Google Scholar

Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16, 695698 (2019).

Article CAS PubMed PubMed Central Google Scholar

Demszky, D. et al. Using large language models in psychology. Nat. Rev. Psychol. 2, 688701 (2023).

Article Google Scholar

Karjus, A. Machine-assisted mixed methods: augmenting humanities and social sciences with artificial intelligence. Preprint at https://arxiv.org/abs/2309.14379 (2023).

Davies, A. et al. Advancing mathematics by guiding human intuition with AI. Nature 600, 7074 (2021).

Article CAS PubMed PubMed Central Google Scholar

Peterson, J. C., Bourgin, D. D., Agrawal, M., Reichman, D. & Griffiths, T. L. Using large-scale experiments and machine learning to discover theories of human decision-making. Science 372, 12091214 (2021).

Article CAS PubMed Google Scholar

Ilyas, A. et al. Adversarial examples are not bugs, they are features. Preprint at https://doi.org/10.48550/arXiv.1905.02175 (2019)

Semel, B. M. Listening like a computer: attentional tensions and mechanized care in psychiatric digital phenotyping. Sci. Technol. Hum. Values 47, 266290 (2022).

Article Google Scholar

Gil, Y. Thoughtful artificial intelligence: forging a new partnership for data science and scientific discovery. Data Sci. 1, 119129 (2017).

Read this article:

Artificial intelligence and illusions of understanding in scientific research - Nature.com