LPHIL7252 Artificial Intelligence and Data Ethics Course Descriptor

Course Overview

This course will give students a strong foundation in key ethical issues that are emerging in data science and artificial intelligence (AI). They will gain a critical understanding of how these technologies present ethical questions which require a deep understanding both of the detailed context of application, and of pertinent philosophical questions. This encompasses not simply practical ethics as generally conceived but requires consideration of other areas of philosophy such as metaethics, philosophy of mind, and epistemology. Methodological questions concerning the application of ethical theory to practice will also be discussed. As well as developing an appreciation of the background philosophical debates, students will also be introduced to the history of work on the ethics of AI, and the considerable current work in policy and regulation for this area.

Students will develop skills in the application of abstract philosophical and ethical concepts to practical uses, including the analysis of cases and the production of relevant policy for AI and data ethics. They will gain an appreciation of how engagement in ethical debates can contribute to the beneficial development of such technologies.

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 a range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, and discussion boards) to orientate and engage students in their studies.

Teaching and learning strategies for this course will include:

• Lectures: Instructor-led classes.
• Seminars/workshops): Interactive sessions on project management principles, focused on applying theoretical concepts.
• Experiential Learning, which may include simulations and role-playing for hands-on experience, or guest speakers for insight from professionals.
• Online Resources: Flexible learning with additional study materials.

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, including a minimum of 16.5 scheduled hours

Employability Skills

• Ability to present succinct argument and analysis in both written and oral form
• Ability to work in collaborative groups
• Ability to find practical solutions and acceptable comprises to complex problems
• Awareness of how to track and understand contemporary issues in the ethics of technology
• Basic understanding of the main points of GDPR

Assessment

Formative

Students will be formatively assessed during the course by means of set assignments. These do not count towards the end of year results, but will provide students with developmental feedback, both written and oral.

Summative

Assessment will be in two forms:

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 for courses are produced annually in the Course Syllabus or other documentation provided to students; the indicative reading list provided below is used as part of the approval/modification process only.

The literature in this field is developing very rapidly and reading for the course will reflect this.

Books

Boddington, P., 2017. Towards a code of ethics for artificial intelligence. Springer International Publishing.

Coeckelbergh, M., 2020. AI ethics. Mit Press.

Dubber, M., Pasquale, F., and Dased, S. 2020. The Oxford Handbook of the Ethics of AI, Oxford, Oxford University Press.

Véliz, C., 2023. The Ethics of Privacy and Surveillance. Oxford University Press.

Wallach, W. and C. Allen. 2008. Moral Machines: Teaching Robots Right from Wrong, Oxford: Oxford University Press.

Journals

Buolamwini, J. and Gebru, T., 2018, January. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77-91). PMLR.

Chouldechova, A. and Roth, A., 2018. The frontiers of fairness in machine learning. arXiv preprint arXiv:1810.08810.

Just, N., & Latzer, M. (2017). “Governance by algorithms: reality construction by algorithmic selection on the Internet.” Media, Culture & Society, 39(2), 238-258. doi: 10.1177/0163443716643157

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.

Russell, S., Hauert, S., Altman, R. and Veloso, M., 2015. Ethics of artificial intelligence. Nature, 521(7553), pp.415-416.

Electronic Resources

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/other/ead_v2.pdf

Talking Machines podcast http://www.thetalkingmachines.com

Indicative Topics

• Ethical frameworks for AI: ethical theories and concepts, policy and regulation landscapes for data and AI ethics
• Human and machine agency, human enhancement
• Autonomy, control, and trust in AI
• ‘Moral’ machines, accountability and responsibility
• Transparency and explainability in Machine Learning, data, and AI
• Bias of algorithms and fairness in the use of AI
• AI and data: governance and regulation

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