FPH
Financiado
Fair predictions in health
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...
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 learning
O2: probabilistic ethics: understand how standard moral concepts such as responsibility, merit, need, talent, equality, and benefit can be understood in probabilistic terms
O3: 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 counterfactuals
O4: 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.
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Duración del proyecto: 40 meses
Fecha Inicio: 2020-04-09
Fecha Fin: 2023-08-31
Fecha Fin: 2023-08-31
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2023-08-31
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
MSCA-IF-2019:
Individual Fellowships
Cerrada
hace 5 años
Presupuesto
El presupuesto total del proyecto asciende a
183K€
Líder del proyecto
POLITECNICO DI MILANO
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
550.56K€ |
Participante