LPHIL5248 Artificial Intelligence and Data Ethics Course Descriptor

Course Overview

This course introduces students to the ethical issues that emerge in data science and the related field of artificial intelligence. Students will be introduced to the main ethical concepts and values shaping the current debates of data and AI ethics, including gaining an understanding of the philosophical background, the history of such debates, the relevance of professional ethics and of the ethics of research on the human subject, and the current regulatory and policy landscape. We will examine the production, collection, storing, curation and sharing of data as well as the context of the construction and evaluation of models and hypotheses using artificial intelligence.

Students will learn how to apply abstract ethical concepts to practical uses in the analysis of cases and in the production of policy for these ongoing debates. Students will also be encouraged to consider how issues raised in a technological context may be illuminated by considerations from a wide range of disciplines, including literature and the social sciences.

Learning Outcomes

On successful completion of the course, students will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Employability Skills

Teaching and Learning

This course has a dedicated Virtual Learning Environment (VLE) page with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, discussion boards) to orientate and engage students in their studies.

The scheduled teaching and learning activities for this course are:

Lectures/seminars/labs/studios/workshops

40 scheduled hours – typically including induction, consolidation or revision, and assessment activity hours.

• Version 1:all sessions in the same sized group

OR

• Version 2: most of the sessions in larger groups; some of the sessions in smaller groups

Faculty hold regular ‘office hours’, which are opportunities for students to drop in or sign up to explore ideas, raise questions, or seek targeted guidance or feedback, individually or in small groups. 

Students are to attend and participate in all the scheduled teaching and learning activities for this course and to manage their directed learning and independent study.

Indicative total learning hours for this course: 150

Assessment

Both formative and summative assessment are used as part of this course, with purely formative opportunities typically embedded within interactive teaching sessions, office hours, and/or the VLE.

Summative Assessments

Further information about the assessments can be found in the Course Syllabus.

Feedback

Students will receive formative and summative feedback in a variety of ways, written (e.g. marked up on assignments, through email or the VLE) or oral (e.g. as part of interactive teaching sessions or in office hours).

Indicative Reading

Note: Comprehensive and current reading lists are produced annually in the Course Syllabus or other documentation provided to students; the indicative reading list provided below is for a general guide and part of the approval/modification process only.

• Boddington, P., 2017. Towards a code of ethics for artificial intelligence. Springer International Publishing.
• O’Neil, C., (2016) Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books
• Pasquale, F., (2015) The Black Box Society. Harvard University Press.

Journals

• Freeman, K., 2016. Algorithmic injustice: How the Wisconsin Supreme Court failed to protect due process rights in State v. Loomis. North Carolina Journal of Law & Technology, 18(5), p.75
• Lawrence, N.D., 2017. Living together: Mind and machine intelligence. arXiv preprint arXiv:1705.07996.
• Mittelstadt, B.D., Allo, P., Taddeo, M., Wachter, S. and Floridi, L., 2016. The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), p.2053951716679679.
• Rawahn, I. et al. (2019). Machine behaviour. Nature, 568, 477-486. https://doi.org/10.1038/s41586-019-1138-y
• Russell, S., Hauert, S., Altman, R. and Veloso, M., 2015. Ethics of artificial intelligence. Nature, 521(7553), pp.415-416.
• Zwitter, A., 2014. Big data ethics. Big Data & Society, 1(2), p.2053951714559253.

Electronic Resources

• Executive Office of the President (Obama Whitehouse) Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination.pdf
• IEEE Ethically Aligned Design: A vision for prioritising human wellbeing for autonomous and intelligent systems, v 2 https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/

Indicative Topics

Note: Comprehensive and current topics for courses are produced annually in the Course Syllabus or other documentation provided to students; the indicative topics provided below is used as a general guide and part of the approval/modification process only.

• Data privacy, data ownership, the digital self, and GDPR
• Transparency and explainability in Machine Learning, data, and AI
• Accountability and responsibility in the collection, analysis and use of data
• Autonomy and control
• Access to and control over information, governance of social media
• Professional ethics, ethical issues in human subject research
• Current policy and regulation landscapes in data and AI ethics
• AI, data science, and the future of employment