This Study Handbook is published with reservation. It is not official yet.
|Teaching method||Contact hours|
|Course coordinator(s)||dr. V Blok|
|Lecturer(s)||dr. ir. LWA Klerkx|
|JR de Vries|
|prof. dr. MF Verweij|
|Examiner(s)||dr. V Blok|
|JR de Vries|
|dr. ir. LWA Klerkx|
Language of instruction:
Thesis using Data Science approaches
Information technology has been the core of the third industrial revolution. 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. Topics and questions can include, for example:
- is there a meaningful role for informed consent in biobanks, data use, app licences?|- how to evaluate publishing geographical data on the incidence of specific diseases (e.g via google maps)?|
- what is the value of privacy in a world where many people eagerly share private information via social media?
- how does data science (e.g. in precision farming) influence animal welfare in the livestock farming?
- how to evaluate (e.g. online) systems that steer people’s choices for example about borrowing money or about food choice in particular directions (e.g. via data presentation,
default options, etc.)
- how to articulate and evaluate implicit assumptions in algorithms in specific contexts (police surveillance; health surveillance; etc.)
- who is ethically responsible for the behaviour of intelligent systems?
- can ethics be built into intelligent technologies? And if so – does that imply that we also have obligations to those technologies?
After successful completion of this course students are expected to be able to:
- 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.
Students prepare classroom discussions on these often philosophical questions. In the last part of these discussions different teams develop proposals for how ethical and societal considerations could be ‘built into’ the development or implementation of a technology.
- written exam 60%;
- group assignment 40%.
To be announced.
|Restricted Optional for:||MME||Management, Economics and Consumer Studies||MSc||6AF|
|MFN||Forest and Nature Conservation||MSc||6AF|
|MPB||Master Plant Biotechnology (2020)||MSc||6AF|
|MID||International Development Studies||MSc||6AF|
|MCH||Communication, Health and Life Sciences||MSc||6AF|
|MAM||Aquaculture and Marine Resource Management||MSc||6AF|