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Ethics & Responsible AI

Examine bias, privacy, transparency, and accountability in AI systems, and learn practical checks for using and deploying AI responsibly at work.

Material Overview
Ethics & Responsible AI

Study where AI systems cause harm and what can be done about it. Topics include bias in training data, fairness across groups, privacy and data protection, explainability, misinformation and deepfakes, copyright questions raised by generative AI, and emerging rules such as the EU AI Act. You will practise reviewing a real AI use case against a responsible AI checklist, so ethics becomes part of everyday decisions rather than a document nobody reads.

Curriculum

Learning Path

Each level is enrolled separately. Work through them in order, or start at the level that matches your experience.

  1. Level 01

    Beginner

    Purpose

    Understand the everyday harms AI can cause and the questions every user should ask before trusting an output.

    Outcome

    You can use AI tools with an informed sense of what could go wrong and who could be affected.

    What You'll Learn
    • Learn where bias enters a system through data, labels, and design choices
    • See how privacy is affected when personal data reaches a model
    • Recognise misinformation, deepfakes, and the signs of synthetic content

    203 CREDS

  2. Level 02

    Intermediate

    Purpose

    Learn how fairness, transparency, and accountability are assessed in practice rather than discussed in the abstract.

    Outcome

    You can review an AI feature against concrete fairness and transparency criteria.

    What You'll Learn
    • Measure outcomes across groups and see why a single accuracy number hides discrimination
    • Learn what explainability can and cannot deliver for a given model type
    • Map who is accountable when an automated decision is wrong

    454 CREDS

  3. Level 03

    Advanced

    Purpose

    Apply governance to real deployments, covering regulation, documentation, and the controls an organisation is expected to have.

    Outcome

    You can prepare the documentation and controls an AI deployment needs before it reaches users.

    What You'll Learn
    • Work through data protection duties, consent, retention, and cross border transfer
    • Learn documentation practices such as model cards, data sheets, and impact assessments
    • Follow the risk tiers in the EU AI Act and comparable rules elsewhere

    711 CREDS

  4. Level 04

    Expert

    Purpose

    Lead responsible AI practice by designing review processes that hold up when a system is challenged.

    Outcome

    You can own responsible AI for a team and defend a deployment decision with evidence.

    What You'll Learn
    • Build a review board process with clear decision rights and escalation
    • Run red teaming and incident response for AI specific failures
    • Set monitoring that detects drift, misuse, and harm after launch

    1,096 CREDS