The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts

By Brent Daniel Mittelstadt, Luciano Floridi. Science and Engineering Ethics, 2015

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Fairness of artificial intelligence in healthcare: review and recommendations (2023, background): “They can provide valuable insights into the needs and preferences of diverse patient populations, ensuring that AI addresses the specific challenges faced by various communities [86, 117, 118].”
On the ethics of algorithmic decision-making in healthcare (2019, background): “33 Hence, measures need to be taken to ensure a fair distribution of health risks among populations stemming from the involvement of machine learning in medicine.”
The promise of machine learning in predicting treatment outcomes in psychiatry (2021, background): “Furthermore, a central issue in fair machine learning develop ment arises when the training dataset is not a good representa tion of the phenomenon being studied 192,208 .”
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020, background): “ented by anticipatory questions such as, “what if...?”. The aim is to increase ethical foresight through systematic thinking about the larger sociotechnical system in which a product will be deployed [50]. There are also intersections between this framework and just effective product development the”