In this course we will introduce and critically discuss the social, political, legal and ethical issues surrounding data-driven innovation, including those posed by AI and machine learning, through the lens of real-world controversies, dilemmas and examples of practice.

We will begin by establishing a foundational understanding of core tools and concepts such as ethics, rights and governance, and responsible research and innovation. Having established a common grounding, we will drill down into case studies, structured around core concerns being raised by society, governments and industry such as bias, fairness, data re-use, discrimination, transparency and accountability and the realities of making ethical decisions in a landscape of competing interests.

Collectively, we will engage with normative questions arising from data-based judgments as they apply to critical contexts such as health, journalism, policing, employment, education, social care and politics, exploring the social implications and the tools required to minimise harm, promote fairness and enable human autonomy. Whilst some examples will be dealt with in the abstract, this course will be focused upon practice of data driven research and Innovation. It will thus address cutting edge issues being grappled with by practitioners and new approaches emerging in industry and offer the opportunity for participants to develop and feedback solutions.