The Fair Machine Learning Lab
The Fair Machine Learning Lab (FairML-Lab) is based within the Social, Genetic and Developmental Psychiatry (SGDP) Centre, part of the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) at King's College London.
We are an interdisciplinary research group dedicated to developing fair, trustworthy and ethical artificial intelligence for healthcare. Our research spans the full AI lifecycle, from methodological innovation in fairness-aware machine learning and the evaluation of AI performance across protected features, to the development of ethical AI frameworks, policy guidance, implementation science, AI literacy and educational tools that support the responsible adoption of AI in healthcare.
We believe that responsible AI requires both technical excellence and meaningful collaboration with the people who develop, regulate, use and are affected by these technologies. We therefore work closely with clinicians, patients, policymakers, industry and international collaborators to co-develop practical solutions that address real-world healthcare challenges and translate research into patient and societal benefit.
Our Mission
Our mission is to ensure that artificial intelligence improves healthcare while promoting fairness, transparency, accountability, inclusion, empowerment, non-dehumanisation of medicine and public trust. By combining cutting-edge methodological research with ethics, policy, implementation science and education, we aim to ensure that AI benefits all communities and contributes to more equitable healthcare systems.
Flagship Outputs
The Fair Machine Learning Lab develops practical methods, frameworks and educational resources that support the responsible development and implementation of AI in healthcare.
Our flagship outputs include:
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ELAXIR AI Ethics Cards – an evidence-based educational toolkit designed to help healthcare professionals and patients understand, evaluate and integrate AI into routine care safely, ethically and responsibly. Read a preprint here. Used by the NHS AI Ambassadors Network
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5-Facts Ethical Model – a practical framework that supports ethical decision-making and responsible AI implementation in healthcare.
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Healthcare AI Ethical Compliance and Evaluation Score Sheet (HAI-CESS) – a structured framework for assessing healthcare AI systems against key ethical principles and supporting responsible AI governance.
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Fairness-aware machine learning methodologies – FairGATE is a novel method to identify, quantify and mitigate algorithmic bias across protected features, improving fairness and equity in AI-driven healthcare.
Current Members
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Dr Raquel Iniesta – Reader in Machine Learning and AI Ethics in Healthcare; Group Lead.
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Dr Yiyang Ge – Postdoctoral Researcher in Machine Learning and Topological Data Analysis.
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Lei Luo – PhD Student in Artificial Intelligence and Neuroimaging.
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Giuseppe Merola – PhD Student in Artificial Intelligence for Eating Disorders and Depression.
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Nabila Naeem – Research Associate in AI Ethics and Responsible Artificial Intelligence.
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Dr Rupa Chilvers – Visiting Researcher in Health Systems, Rapid Introduction and Adoption of Innovation, Health Workforce Development, and Health Policy.
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Yuhuan Luo – MSc Student in Fairness-Aware Machine Learning.
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Eliha Sadiq – MSc Student investigating the Association Between Nutrients and Depression.
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Wing Ting Yeung – MSc Student in Artificial Intelligence and Workers' Wellbeing.
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Ciel Burgess – (Close collaborator) Fairness evaluation and methodological approaches for protected features.
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Rhys Holland – (Close collaborator) Development of fairness-aware machine learning models.