FPH · Fair predictions in health
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-09-01 → 2023-08-31
- EU contribution
- €183,473
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Fair predictions in health
Problem Statement: There are significant diagnostic disparities in medical AI, with algorithms potentially misdiagnosing diseases differently across genders and ethnicities due to biased data and uniform thresholds. Societal Impact: As AI becomes prevalent in healthcare, addressing potential biases in diagnostic tools is vital. These biases can significantly affect patient well-being, making a deep understanding of fairness in AI essential. Research Objectives: The project aims to examine philosophical concepts of justice and fairness, the intersection of probability theory and ethics, and real-world case studies to identify and address biases in AI healthcare diagnostics. Project Conclusion: My research, rooted in the "fair equality of chances" principle, reveals complex algorithmic biases in healthcare that reflect broader societal inequalities. It underscores the need for a nuanced understanding of fairness in healthcare AI to avoid perpetuating these disparities.
Data: CORDIS, © European Union
Project objective
In clinical care, machine learning is progressively used to enhance diagnosis, therapy choice, and effectiveness of the health system. Because machine-learning models learn from historically gathered information, populations that have suffered past human and structural biases (e.g. unequal access to education or resources) — called protected groups — are susceptible to damage from inaccurate projections or resource allocations, reinforcing health inequalities. For example, racial and gender differences exist in the way clinical data are produced and these can be transferred as biases in the models. Several techniques of algorithmic fairness have been suggested in the literature on machine learning to ameliorate the performance of machine learning with respect to its fairness. The debate in statistics and machine learning has however failed to provide a principled approach for choosing concepts of bias, prejudice, discrimination, and fairness in predictive models, with a clear link to ethical theory discussed within philosophy. The specific scientific objectives of this research project are:O1: ethical theory: mapping the ethical theories that are relevant for the allocation of resources in health care and draw connections with the literature in fair machine learningO2: probabilistic ethics: understand how standard moral concepts such as responsibility, merit, need, talent, equality, and benefit can be understood in probabilistic termsO3: epistemology of causality: understand if current claims made by counterfactual and causal models of fairness in AI are robust with respect to different philosophical understandings of probability, causality, and counterfactualsO4: application: to show the relevance these philosophical ideas by applying them to a limited number of paradigmatic cases of the application of predictive algorithms in health care.
Original text from CORDIS.
Participants
- POLITECNICO DI MILANO · MilanoCoordinatorItaly
Links
Data: CORDIS, © European Union