Inherent Bias in Artificial Intelligence-Based Decision Support Systems for Healthcare
Abstract
:1. Understanding the Concept of Bias
2. Information Explosion and the Need for Reliability
3. Problems in Processing Knowledge
4. Possible Solutions
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Challen, R.; Denny, J.; Pitt, M.; Gompels, L.; Edwards, T.; Tsaneva-Atanasova, K. Artificial intelligence, bias and clinical safety. BMJ Qual. Saf. 2019, 28, 231–237. [Google Scholar] [CrossRef] [PubMed]
- Althubaiti, A. Information bias in health research: Definition, pitfalls, and adjustment methods. J. Multidiscip. Res. Healthc. 2016, 9, 211. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gurupur, V.; Kulkarni, S.A.; Liu, X.; Desai, U.; Nasir, A. Analysing the power of deep learning techniques over the traditional methods using medicare utilization and provider data. J. Exp. Theor. Artif. Intell. 2019, 31, 99–115. [Google Scholar] [CrossRef]
- Holman, L.; Head, M.L.; Lanfear, R.; Jennions, M. Evidence of experimental bias in the life sciences: Why we need blind data recording. PLoS ONE 2015, 13, e1002190. [Google Scholar] [CrossRef] [PubMed]
- Henriksen, K.; Kaplan, H. Hindsight bias, outcome knowledge and adaptive learning. Qual. Saf. Healthc. 2003, 12, ii46–ii50. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Huth, E.J. The information explosion. Ann. Intern. Med. 1989, 65, 647–661. [Google Scholar]
- Dixon, T. The information explosion. Can. Fam. Phys. 1991, 37, 819, 821–827. [Google Scholar]
- Vrij, A.; Hope, L.; Fisher, R.P. Eliciting reliable information in investigative interviews. Policy Insights Behav. Brain Sci. 2014, 1, 129–136. [Google Scholar] [CrossRef] [Green Version]
- Bohannon, J. Who’s afraid of peer review? Science 2013. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Roberts, P.D.; Stewart, G.B.; Pulin, A. Are review articles a reliable source of evidence to support conservation and environmental management? A comparison with medicine. Biol. Conserv. 2006, 132, 109–423. [Google Scholar] [CrossRef]
- Fragale, A.R.; Heath, C. Evolving informational credentials: The (Mis) attribution of believable facts to credible sources. Personal. Soc. Psychol. Bull. 2004, 30, 225–236. [Google Scholar] [CrossRef] [PubMed]
- Cambridge Dictionary. Available online: https://dictionary.cambridge.org/us/dictionary/english/precision (accessed on 3 May 2019).
- Gurupur, V.; Gutierrez, R. Designing the right framework for healthcare decision support. J. Integr. Des. Process Sci. 2016, 20, 7–32. [Google Scholar] [CrossRef]
- Cambridge Dictionary. Available online: https://dictionary.cambridge.org/us/dictionary/english/accuracy (accessed on 29 February 2020).
- Nelson, G.S. Bias in Artificial Intelligence. N. C. J. Med. 2019, 80, 220–222. [Google Scholar] [CrossRef] [PubMed]
- Gurupur, V.; Suh, S.C.; Selvaggi, R.R.; Karla, P.R.; Nair, J.S.; Ajit, S. An approach for building a personal health information system using conceptual domain knowledge. J. Med. Syst. 2012, 36, 3685–3693. [Google Scholar] [CrossRef] [PubMed]
- Gurupur, V.; Tanik, M.M. A system for building clinical research applications using semantic web-based approach. J. Med. Syst. 2012, 36, 53–59. [Google Scholar] [CrossRef] [PubMed]
- Wan, T.T.H. Evidence-Based Health Care Management: Multivariate Modeling Approaches; Kluwer Academic Publishers: Norwell, MA, USA, 2002. [Google Scholar]
- Dressel, J.; Farid, H. The accuracy, fairness, and limits of predicting recidivism. Res. Methods 2018, 4, eaao5580. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Oaksford, M.; Chater, N. Information gain explains relevance which explains the selection task. Cognition 1995, 1995, 97–108. [Google Scholar] [CrossRef]
- Gurupur, V.; Sakoglu, U.; Jain, G.P.; Tanik, U.J. Semantic requirements sharing approach to develop software systems using concept maps and information entropy: A personal health information system example. Adv. Eng. Softw. 2014, 70, 25–35. [Google Scholar] [CrossRef]
- Wan, T.T.H. Population Health Management for Poly Chronic Conditions: Evidence-Based Research Approaches; Springer: New York, NY, USA, 2018. [Google Scholar]
Factors Leading to Knowledge Bias | Description |
---|---|
Experimental bias | Inherent bias in experiment leading to inaccurate outcomes, and pre-existing beliefs leading to wrong perceptions such as hindsight |
Problems with information reliability | Synthesizing systems based on false or partially accurate data |
Limited expert knowledge | Domain experts may have limited knowledge of their own domain that will limit the knowledge programmed into the system |
Shallow information | Implicit knowledge contained in systems such as electronic health records may be shallow and may not include the necessary details |
Factors Leading to Processing Bias | Description |
---|---|
Bias in the selected algorithm | The selected data processing algorithm may not be appropriate for the required decision support process |
Bias in tacit knowledge used for feedback | Feedback provided by knowledge providers may be biased and may create bias if used to modify the processing structure |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gurupur, V.; Wan, T.T.H. Inherent Bias in Artificial Intelligence-Based Decision Support Systems for Healthcare. Medicina 2020, 56, 141. https://0-doi-org.brum.beds.ac.uk/10.3390/medicina56030141
Gurupur V, Wan TTH. Inherent Bias in Artificial Intelligence-Based Decision Support Systems for Healthcare. Medicina. 2020; 56(3):141. https://0-doi-org.brum.beds.ac.uk/10.3390/medicina56030141
Chicago/Turabian StyleGurupur, Varadraj, and Thomas T. H. Wan. 2020. "Inherent Bias in Artificial Intelligence-Based Decision Support Systems for Healthcare" Medicina 56, no. 3: 141. https://0-doi-org.brum.beds.ac.uk/10.3390/medicina56030141