Data Science Ethics

Information technology has been the core of the third industrial revolution, but with the advent of AI it is transforming what it means to work, to socialise -- and even of what it means to be human. With big data, artificial intelligence, and automated systems -- combined with other technologies robotics, nanotechnology and genetics -- we are at the brink of a fourth industrial revolution that might even be more disruptive than previous waves of technological change. Data science has a central place in these developments, and data scientists and big data specialists have a special responsibility to take ethical aspects of their work and the societal implications of their ideas and products into account. This involves both collecting, using and processing data in ethically appropriate ways, but also reflecting on how data science technologies can be shaped and designed to reflect ethical values.

The overall objective of this course is that students develop competencies (knowledge, skills, attitudes) that enable them to critically reflect on and appraise projects, applications and futures of data science technology.

Students will learn about different approaches and concepts central to ethical reasoning (wellbeing, respect, consent, responsibility, equity, privacy), as a basis for articulating and criticising their own moral views and apply these to a variety of cases and contexts.
Basics of critical reflection, ethical reasoning and specific examples of societal impacts will be presented in lectures and reading materials.

Students will apply these in assignments. Moreover, they explore examples and scenarios in real life or in movies, literature, video clips and other art forms as a basis for class presentations and broader ethical discussions about their topics.

The six lectures will roughly cover the following topics:

• Introduction: what is data science and what is data science ethics?
• What are data? Data ownership and power
• Consequences of data gathering: Privacy and Surveillance
• What is decision-making in humans and in AI? Trust in AI; explainable AI; Moral responsibility
• Consequences of decision-making: Justice; data colonialism; fairness
• Professional ethics for data scientists. Codes of Conduct; privacy laws; intellectual property laws.

• Articulate, explain and apply basic ethical requirements for data science (informed consent, ownership, accuracy, privacy, fairness, avoiding harm), acknowledging the open-endedness of, the ambiguities within, and the trade-offs between such requirements

• Explain the implications of these concepts and requirements for data science

• Recognise consequence-based, rights-based, character-based ethical arguments

• Indicate the weaknesses of specific arguments or requirements

• Imagine, discuss and evaluate scenarios of novel data science technologies and applications and their potentially disruptive impacts in science and society

• Explore and discuss ‘normal’ and disruptive cases of data science applications in real life or fiction

• Explain the ethical dimensions of such applications and propose ways to take these into account in the design or implementation of technology

• Articulate and assess normative and epistemic assumptions in specific technologies, projects or scenarios, in relation to general values and ideals as well as their (students’) own normative judgments

• ? (40%) Ethical case study.
• ? (60%)

• To be advised.