Addressing bias in big data and AI for health care: A call for open science

By Natalia Norori, Qiyang Hu, F. M. Aellen, F. Faraci, Athina Tzovara. Patterns, 2021

711 citations recorded by the source corpus.

Summary Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making and revolutionizing the field of health care. A major open challenge that AI will need to address before its integration in the clinical routine is that of algorithmic bias. Most AI algorithms need big datasets to learn from, but several groups of the human population have a long history of being absent or misrepresented in existing biomedical datasets. If the training data is misrepresentative of the population variability, AI is prone to reinforcing bias, which can lead to fatal outcomes, misdiagnoses, and lack of generalization. Here, we describe the challenges in rendering AI algorithms fairer, and we propose concrete steps for addressing bias using tools from the field of open science.

Subfields: Artificial Intelligence, General Health Professions, Health Informatics

View Paper: https://www.semanticscholar.org/paper/f0b72514d8ea9f49b74eac9401b29d399e15ca16

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wei Dang Lu
2. Mauro Giuffrè
3. Dennis L. Shung
4. Bryan Perozzi
5. Chengqi Zhang
6. William L. Hamilton

Citations in the corpus

• Large language models in health care: Development, applications, and challenges (2023, background): “Moving towards fair artificial intelligence and combating bias will be a significant challenge for LLMs [56].”
• Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design (2023, neutral): “Therefore, it is essential to guarantee that the training data used to create AI models are representative of the population for whom the model will be utilized and that the data are trustworthy, comprehensive, and impartial [218,219].”
• Bias in artificial intelligence algorithms and recommendations for mitigation (2023, background): “Over the past decade, governments, funders, and institutions have promoted open data sharing to provide access to a variety of data sources [54].”
• Harnessing the power of synthetic data in healthcare: innovation, application, and privacy (2023, background): “This can result in misinformed or discriminatory outcomes in medical research and practice, further perpetuating existing inequalities and placing vulnerable populations at heightened risk of harm and discrimination 38,39 .”