Postgraduate Course: Translational Data and Artificial Intelligence Ethics (fusion on-site) (EFIE11169)
|Edinburgh Futures Institute
|College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)
|SCQF Level 11 (Postgraduate)
|Available to all students
|How should you talk to your technical colleagues about ethics? This course aims to bridge the communication gap between data ethicists and computing professionals in terms of their values, assumptions, concepts and practices. Students will learn to engage and lead on ethical issues more effectively with technical colleagues and audiences, and to design ethical interventions which are not only theoretically justified, but practical, comprehensible, and compelling.
How should you talk to your technical colleagues about ethics? This course aims to bridge the gap between data ethicists and computing professionals in terms of their values, assumptions, concepts and practices. Students will learn to engage and lead more effectively on ethical issues with technical colleagues and audiences, and to design ethical interventions which are not only theoretically justified, but practical, comprehensible, and compelling.
Topics will include: an ethnography of technological practitioners, their common values and assumptions; a review of ethics educational content in technical degree programmes; an overview of common development paradigms or processes, such as agile; positive and negative incentives in the tech sector; how development environments might vary in different settings (big tech vs startup vs academia); a look at key terms (such as 'intelligence') and the different understandings of them between fields; analysis of examples of good and bad arguments for changes of behaviour by practitioners; common evaluation methods for technical systems and how these might be critiqued. Learning in an innovative hybrid and intensive mode that brings together online and in-person students, you will work together in collaborative groups to discuss effective educational, procedural and leadership approaches to working effectively with technical colleagues, and maximising the impact of your ethical expertise.
Edinburgh Futures Institute (EFI) - On-Site Fusion Course Delivery Information:
The Edinburgh Futures Institute will teach this course in a way that enables online and on-campus students to study together. This approach (our 'fusion' teaching model) offers students flexible and inclusive ways to study, and the ability to choose whether to be on-campus or online at the level of the individual course. It also opens up ways for diverse groups of students to study together regardless of geographical location. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities.
Students should be aware that:
- Classrooms used in this course will have additional technology in place: students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- Unless the lecturer or tutor indicates otherwise you should assume the session is being recorded.
As part of your course, you will need access to a personal computing device. Unless otherwise stated activities will be web browser based and as a minimum we recommend a device with a physical keyboard and screen that can access the internet.
Entry Requirements (not applicable to Visiting Students)
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2023/24, Available to all students (SV1)
|Course Start Date
|Learning and Teaching activities (Further Info)
Seminar/Tutorial Hours 14,
Online Activities 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
The course will be assessed by means of the following assessment components:
1) Case Study Intervention: 1000 Word Proposal + 1000 Word Essay (100%)
During the post intensive phase, students will be provided with a case study technical setting and asked in small groups to design an intervention (probably process recommendation, or teaching materials, but flexible to other student ideas) for their technical colleagues. This will involve researching the domain, designing and writing up a 1000 word proposal (or equivalent in another format) for the intervention.
Individually, students will supplement this proposal with a submitted 1000 word essay outlining the reasoning behind the design of the intervention and how it was tailored to the audience, including what assumptions, values and approaches they considered.
Where possible, these case studies will be with real computer scientists - either from industry or other student projects.
|Formative feedback will be provided in the immersive phase by the course organiser leading the Q&A session in the second week.
Written summative feedback will be provided on the individual summative assessments, following the post-intensive application phase.
|No Exam Information
On completion of this course, the student will be able to:
- Demonstrate an understanding of the culture of computing and data science, including common terminology, education, values, assumptions, concepts, and attitudes towards the ethical dimensions of their work.
- Critically evaluate a range of typical development pipelines, processes and organisational setups, and discuss how these might impact on ethical work.
- Clearly communicate ethical concepts and interventions for technological practitioners, making arguments tailored to be convincing and appealing to this group.
- Work together with other students to develop an educational or process intervention aimed at improving the ethical understanding, behaviour, or outputs of technical team presented as a case study.
- Demonstrate communication skills and leadership techniques that produce effective ethical engagements with technical organisations and teams.
|Indicative Reading List:
Kristine Bærøe & Edmund Henden (2020) Translational Ethics and Challenges Involved in Putting Norms Into Practice, The American Journal of Bioethics, 20:4, 71-73, DOI: 10.1080/15265161.2020.1730520
Inioluwa Deborah Raji, Morgan Klaus Scheuerman, and Razvan Amironesei. 2021. You Can't Sit With Us: Exclusionary Pedagogy in AI Ethics Education. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing Machinery, New York, NY, USA, 515-525. https://doi.org/10.1145/3442188.3445914
McNamara, Andrew, Justin Smith, and Emerson Murphy-Hill. "Does ACM's code of ethics change ethical decision making in software development?." Proceedings of the 2018 26th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering. 2018.
Schaich Borg, Jana. "The AI field needs translational Ethical AI research." AI Magazine (2022).
Bryan A. Sisk, Jessica Mozersky, Alison L. Antes & James M. DuBois (2020) The 'Ought-Is' Problem: An Implementation Science Framework for Translating Ethical Norms Into Practice, The American Journal of Bioethics, 20:4, 62-70, DOI: 10.1080/15265161.2020.1730483
Taddeo, Mariarosaria, and Luciano Floridi. "How AI can be a force for good." Science 361.6404 (2018): 751-752.
|Graduate Attributes and Skills
|Knowledge and Understanding:
- A critical understanding of a range of specialised theories, concepts and principles drawn from multiple disciplinary and practitioner perspectives.
- Critical knowledge and understanding of the tensions between, and known limitations of, particular approaches, methodologies and interventions.
- A critical awareness of current challenges and debates in an emerging research area involving multiple specialisms.
Applied Knowledge, Skills and Understanding:
- Ability to apply critical knowledge to concrete case studies, research outputs, applications and proposals.
- Ability to identify potential challenges in a case study, as related to design, use and regulation.
- Ability to demonstrate originality and/or creativity, including in practice.
Generic Cognitive Skills:
- Development of original and creative responses to problems and issues.
- Capacity to critically review, consolidate and extend knowledge, skills, practices and thinking across disciplines, subjects, and sectors.
- Ability to deal with complex issues and make informed judgements in situations in the absence of complete or consistent data/information.
Communication, ICT, and Numeracy Skills:
- Communication, using appropriate methods, to a range of audiences with different levels of knowledge/expertise.
- Ability to articulate clear and well-justified advisory recommendations.
Autonomy, Accountability, and Working with Others:
- Skills to manage their own individual contribution to a group presentation or report.
- The ability to engage constructively and productively in critical debate.
- The ability to work in a peer relationship with specialist practitioners.
- Demonstrating leadership and making an identifiable contribution to change and development and/or new thinking.
|Practical Data Ethics,Translational Ethics,EFI,PG,Level 11,Artificial Intelligence,Data
|Mr James Garforth
|Miss Veronica Silvestre