Learners explore key AI terminology and approaches, including how current, real-world AI applications differ from speculative concepts. The course examines how AI can support sustainability through optimisation, modelling, monitoring and forecasting, while also addressing its full environmental footprint, such as energy use, water consumption, resource extraction and electronic waste. 

Learners explore current challenges associated with AI use, including bias, discrimination, data privacy, security, reliability and accountability gaps. Relevant regulatory and rights-based principles are introduced to show how expectations around transparency, fairness and oversight are shaping responsible AI practice.

Learners consider practical approaches to embedding sustainability into AI decision-making. Through applied examples, they evaluate when AI use is justified, how risks can be mitigated, and how governance frameworks support responsible deployment. Learners assess Environmental, Social and Governance impacts and consider proportionate safeguards, monitoring and review.

Learners will gain confidence in proposing and evaluating responsible AI use within their organisation. They will understand how to balance innovation with environmental responsibility, ethical practice and regulatory compliance, supporting sustainable outcomes.

This course is valuable for learners involved in digital, data, innovation, governance or sustainability roles, and for anyone contributing to decisions about AI adoption and use.