AI Ethics & Governance Course - Build Responsible AI 2026

AI Ethics & Governance Course - Build Responsible AI 2026

Here is the problem almost nobody was hired to solve five years ago, and that enterprises are now scrambling to staff.

An Indian bank deploys a credit model. A customer is refused a loan. Under the DPDP Act the customer can ask why, and "the model decided" is not an answer a regulator accepts. Somebody inside that bank has to be able to open the model, produce the reasons for that specific decision, and show that the model does not systematically refuse one group more than another.

That person is an AI governance specialist, and there are almost none of them in India.

This guide covers what the discipline actually involves and how our 8-week AI Ethics, Governance & Prompt Safety course is structured. It is an Advanced tier programme — it assumes you already understand what a model is.

Why this became a real job

Three things happened at once.

Regulation arrived with teeth. The EU AI Act classifies AI systems by risk and attaches obligations to each tier. India's DPDP Act creates enforceable rights over personal data, including data used to train and run models. Compliance is no longer a slide in a deck; it is documentation somebody has to produce.

LLMs created a new attack surface. The moment a company puts a language model in front of customers, prompt injection and jailbreaking become security problems. A model that can be talked into revealing its system prompt, or into acting on instructions hidden in a document it was asked to summarise, is a live vulnerability.

Enterprises started saying no. Procurement teams now ask vendors for bias testing, model cards and incident procedures. Deals stall without them.

The result is a role that sits between engineering, legal and risk — and salaries in the 10 – 22 LPA range in India, because very few people can speak all three languages.

What you will actually learn

Module 1: Foundations — Bias, Fairness, Transparency

The vocabulary, used precisely rather than loosely.

• Where bias enters: historical, representation, measurement, aggregation
• Competing fairness definitions, and why you cannot satisfy all of them at once
• The transparency/performance trade-off, stated honestly
• What "explainability" means to an engineer, a lawyer and a customer — three different things

Why it is first: most ethics conversations fail because the participants are using the same words for different concepts.

Module 2: The Regulatory Landscape — EU AI Act, DPDP Act, GDPR

What the law actually requires, as opposed to what LinkedIn says it requires.

• The EU AI Act's risk tiers and the obligations attached to each
• India's DPDP Act: consent, purpose limitation, and what it means for training data
• GDPR's provisions on automated decision-making
• Where these regimes overlap, and where complying with one does not cover you for another

Module 3: Explainable AI — SHAP, LIME, Interpretability

The technical core. This is where the module list stops being theory.

• Global versus local explanation, and when each is the right tool
• SHAP values: what they measure and how to read them without over-claiming
• LIME for local surrogate explanations
• Producing an explanation a non-technical stakeholder can act on
• The honest limits — an explanation is a model of a model, not ground truth

Real project: a credit scoring interpretability audit — take a lending model, use SHAP to explain individual rejections, and produce the documentation a compliance officer would need.

Module 4: Bias Detection & Mitigation

Measuring the problem before reaching for a fix.

• Auditing datasets for historical, algorithmic and statistical bias
• Fairness metrics: demographic parity, equalised odds, calibration
• Mitigation at three stages: pre-processing, in-processing, post-processing
• Measuring what a mitigation actually cost you in accuracy
• Bias in Indian contexts specifically — proxies for caste, region, language and gender that a Western fairness toolkit will not flag

Module 5: Prompt Safety — Jailbreaks, Injections, Red-Teaming

The newest material, and the most immediately marketable.

• Direct and indirect prompt injection, with worked examples
• Jailbreak patterns and why simple keyword filters fail
• Guardrails in practice: Llama Guard, LangChain Guardrails
• Red-teaming your own application systematically rather than ad hoc
• Data leakage through model outputs

Real project: a secured enterprise prompt wrapper — an API layer that filters injection attempts in real time, tested against your own red-team suite.

Module 6: Governance Frameworks & Risk Assessment

Turning principles into a process that survives an audit.

• Building a system inventory — most organisations cannot list their AI systems
• Risk classification and tiered review
• Model cards and datasheets that are actually maintained
• Human oversight: where a person must stay in the loop, and what "meaningful review" means
• Incident response when a model fails in production

Module 7: Responsible AI in Healthcare, Finance & the Public Sector

Because the rules are not the same everywhere.

• Healthcare: patient safety, clinical validation, consent
• Finance: explainability duties, fair lending, audit trails
• Public sector: procurement, transparency obligations, contesting a decision
• Sector case studies of failures, and what would have caught them

Module 8: Building an AI Ethics Policy

The capstone, and a document you can take to work.

Real project: a corporate AI governance playbook — a compliance readiness report for an organisation deploying AI, covering inventory, risk tiers, review gates, data privacy under the DPDP Act, and an incident procedure.

The tools you will work with

Who this is for

Prerequisites: a basic understanding of AI/ML concepts. You do not need to be a strong programmer, but you do need to know what a model, a training set and an evaluation metric are.

It suits AI/ML practitioners, legal and compliance officers, policy makers, product managers, senior IT leaders, risk analysts, and HR and diversity officers.

That mix is deliberate. Governance fails when engineers and lawyers cannot talk to each other, and the discussions in this course are more useful when both are in the room.

Outcomes

Skills

• Audit datasets for historical, algorithmic and statistical bias
• Configure SHAP and LIME to explain individual model decisions
• Shield LLM applications against injection and data leakage
• Run governance risk assessments against global compliance regimes
• Design an ethical AI framework with real privacy and safety guardrails

Careers

• Roles: AI Governance Lead, Responsible AI Specialist, AI Risk Analyst, AI Compliance Officer, AI Policy Advisor
• Salary range in India: 10 – 22 LPA
• Duration: 8 weeks
• Why it pays: the intersection of technical, legal and risk skills is genuinely rare, and demand is regulatory rather than discretionary

An honest caveat

This is a young field and parts of it will change. The EU AI Act's implementation details are still settling; DPDP rules are still being interpreted. Anyone selling you a permanent rulebook is overselling.

What does not change is the method: know where bias comes from, be able to explain a decision, know what the law asks of you, and have a process that produces evidence. That is what this course teaches, and it will still apply when the specific statutes have moved on.

Related: AI Governance and Compliance in India · AI Governance Jobs in India

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