Ethical considerations in data and AI: fairness, transparency and societal impact — in every module.
Every AI application affects people: in what they get to see, how they are assessed, which opportunities they are given. That is why ethics is not a separate Friday-afternoon module, but a learning strand that runs through everything.
You learn ethics as a craft: not just holding the view that things should be fair, but making trade-offs explicit, recording them and defending them — even when that clashes with speed or profit.
In every module and every project you make the ethical considerations explicit — they are a standard part of what you deliver.
Your team weighs a real case: more efficient selection versus the risk of excluding groups.
Every project deliverable includes a justification: which choices, which trade-offs, which limits.
You learn how to keep the critical question on the table in an enthusiastic project.
You take this module the way you take the whole programme: classes every other week on Friday and Saturday, with a study load of 15–20 hours per week, of which 10–15 hours is self-study. The teaching is a mix of classroom lessons, practice-based learning, blended learning and working groups or study teams — taught by lecturers who practise the profession themselves on a daily basis.
You conclude each theme with a professional product or a technical solution addressing a real situation in your own work, which you discuss in an assessment with the lecturer. That way your portfolio grows with real work — and your employer benefits directly.
By the end, you make ethical considerations explicit and open to discussion — and you uphold them under pressure.
Email or call the programme — we will gladly help you think it through.