On biases of attention in scientific discovery

Author:

Singer Uriel1ORCID,Radinsky Kira1,Horvitz Eric23

Affiliation:

1. Department of Computer Science, Technion—Israel Institute of Technology, Haifa 3200003, Israel

2. Adaptive Systems and Interaction Group, Microsoft Research, Redmond, WA 98052, USA

3. Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA

Abstract

ABSTRACT Summary How do nuances of scientists’ attention influence what they discover? We pursue an understanding of the influences of patterns of attention on discovery with a case study about confirmations of protein–protein interactions over time. We find that modeling and accounting for attention can help us to recognize and interpret biases in large-scale and widely used databases of confirmed interactions and to better understand missing data and unknowns. Additionally, we present an analysis of how awareness of patterns of attention and use of debiasing techniques can foster earlier discoveries. Availability and implementation The data is freely available at https://github.com/urielsinger/PPI-unbias.

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Computational Inflection for Scientific Discovery;Communications of the ACM;2023-07-25

2. Accelerating science with human-aware artificial intelligence;Nature Human Behaviour;2023-07-13

3. Graph Neural Networks Pretraining Through Inherent Supervision for Molecular Property Prediction;Proceedings of the 31st ACM International Conference on Information & Knowledge Management;2022-10-17

4. The language of proteins: NLP, machine learning & protein sequences;Computational and Structural Biotechnology Journal;2021

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