Bias, Ethics & Governance in AI for Health — Hands-on

Develop a practical understanding of bias, ethics and governance in AI for health. This module covers how bias arises in data and models, fairness notions, consent and privacy basics, documentation practices, and governance structures needed to deploy clinical and omics ML responsibly.

Bias, Ethics & Governance in AI for Health

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Session Index

Session 1 — Foundations of Bias & Ethics in AI for Health Session 2 — Data Governance, Consent & Privacy Basics Session 3 — Fairness Assessment, Mitigation & Documentation Session 4 — Governance, Oversight & Responsible Deployment

Session 1

Fee: Rs 8800

Foundations of Bias & Ethics in AI for Health

Where bias comes from in health AI projects

sampling and measurement bias label and annotation bias deployment and feedback loop bias

Ethical frames and fairness notions (high level)

individual vs group fairness ideas distributive justice and harm minimisation trade offs between different fairness goals

Clinical and public health context for AI ethics

safety, beneficence and non maleficence ideas equity considerations across populations high level view of regulatory expectations

Session 2

Fee: Rs 11800

Data Governance, Consent & Privacy Basics

Data governance along the AI lifecycle

data access and stewardship roles data use agreements and scope retention and disposal basics

Consent, de identification and privacy concepts

broad vs study specific consent ideas pseudonymisation and de identification tactics re identification risk thinking

Cross site and international data collaborations

governance for multi centre projects simple data sharing patterns role of ethics and privacy review boards

Session 3

Fee: Rs 14800

Fairness Assessment, Mitigation & Documentation

Assessing performance across subgroups

stratified metrics and calibration checks identifying systematic error patterns communicating limitations clearly

Mitigation strategies (conceptual overview)

data balancing and re weighting ideas simple constraints and post processing steps monitoring after mitigation

Documentation: model cards and datasheets style summaries

capturing intended use and non intended use documenting data sources and limitations recording evaluation and monitoring plans

Session 4

Fee: Rs 18800

Governance, Oversight & Responsible Deployment

Oversight structures for AI in hospitals and labs

roles of AI governance or review committees stakeholder involvement and sign offs alignment with broader quality systems

Risk management and incident response thinking

risk registers and simple scoring incident logging and escalation routes feedback into model and process updates

Deliverables: governance summary and checklist pack

simple governance and review checklist example model documentation outline one page risk and oversight summary

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Bias, Ethics & Governance in AI for Health Training | Biostatistics & ML for Omics

Bias, Ethics & Governance in AI for Health — Hands-on