NCHNAP789 The Social Context of Artificial Intelligence and Data Science Course Descriptor

Course Summary

This course will give learners a strong foundation in key ethical issues that are emerging in data science and artificial intelligence (AI). Learners will gain a critical understanding of how these technologies present ethical questions that require a deep understanding both of the detailed context of application, and of pertinent philosophical questions. Learners will be introduced to the current work in policy and regulation for this area and will analyse cases relating to AI and data ethics. Learners will gain an appreciation of how engagement in ethical debates can contribute to the beneficial development of such technologies and examine future trends of AI.

Course Aims

• Train learners in the major ethical theories, concepts, and values relevant to current and emerging ethical issues in data science and AI.
• Give learners the tools to develop critical skills needed to assess the business impact of current ethical issues in the workplace.
• Train learners in future trends in AI and how technology may impact upon ethical reasoning and philosophical debates.

Learning Outcomes

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

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

Teaching and Learning

This is an e-learning course, taught throughout the year. 

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include: 

• Online learning
• Online discussion groups
• Online assessment

Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).

Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.

The course learning and teaching hours will be structured as follows:

• Off-the-job learning and teaching (6 days x 7 hours) = 42 hours
• On-the-job learning (12 days x 7 hours) = 84 hours (e.g. 2 days per week for 6 weeks)
• Private study (4 hours per week) = 24 hours

Total = 150 hours

Workplace assignments (see below) will be completed as part of on-the-job learning.

Assessment

Formative

Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback. 

Summative

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Regular tri-partite reviews between the learner (apprentice), their apprenticeship advisor (provider) and workplace line manager (employer) formally monitor and evaluate the learner’s progress.

Indicative Reading

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

Books 

Anderson, M. and Anderson, S.L. eds. (2011). Machine ethics. Cambridge University Press.

Boddington, P. (2017). Towards a Code of Ethics for Artificial Intelligence. Cham, Switzerland : Springer

Müller, V. (2013). Philosophy and Theory of Artificial Intelligence. Berlin ; New York : Springer

Journals

Learners are encouraged to read material from relevant journals on AI and ethics as directed by their course leader.

Electronic Resources

Learners are encouraged to consult relevant websites on AI and ethics.

Indicative Topics

Learners will study the following topics: 

• Ethical frameworks for AI
• Future trends in AI
• Social impact of AI