In this extra post, James Garforth, Benedetta Catanzariti, and Meenakshi Mani, from the Centre for Techomoral Futures (Edinburgh Futures Institute), share their reflections on designing an MSc course ‘Translational Data and AI Ethics’, which responds to the broader challenges of implementing translational ethics teaching programmes within more traditional computer and data science degrees.
Data and AI ethics initiatives and training programmes have become widely popular across sectors, although many have noted how the knowledge and tools gained through these efforts are difficult to ‘translate’ into practice. While data and AI ethics principles and guidelines abound, data practitioners see these as too abstract and often ineffective within the practical contexts of technology development. Conscious of these concerns, we developed a curriculum focused on building ‘translational ethics’ skills across disciplines, as part of the EFI MSc programme ‘Data and Artificial Intelligence Ethics’. In this blog post, we reflect on our experience collaborating across social science and computer science to design and teach ‘Translational Data and AI Ethics’. The course catered to students and practitioners with diverse backgrounds, including computer and data science, business, humanities, and social science.
The many meanings of ‘translation’
Translational ethics is a concept first originating in medical ethics, where ‘translation’ typically refers to the work required to bridge the gap between laboratory research and real-world clinical practice. Translational ethics requires engagement with both theory and practice, and a commitment to interdisciplinary collaboration between various forms of knowledge and evidence to produce safe and effective pathways and treatments.
Borg (2022) proposes a similar approach to AI ethics, where ethical judgement is seen as integral to all phases of technology development, from foundational research to real-world AI deployment. Within this approach, discussions around technical choices cannot be separated from considerations around social and political impact, environmental sustainability, and accessibility. Rather than a standalone practice, ethics becomes a form of knowledge essential to develop both ethically and technically sound systems (Goetze 2023).
In the course, we focused on unpacking the values and practices that make up the social and organisational settings of technology development. We explored the different contexts in which AI artifacts and practices are conceived (research), funded (investment), taught (education), and contested (labour organisation), with an aim to develop tools that can help build a common language to translate ethical considerations into practice across disciplinary boundaries.
Mapping data and AI contexts
The course was divided into three sections:
1. Investigative qualitative research
We began by introducing our students to qualitative research investigating the social and organisational actors who form the data and AI industry, asking:
- What groups tend to be more or less represented in the field?
- What values are typically promoted or reinforced within this industry?
- What forms of knowledge practitioners inherit from traditional computer science training programmes, along with disciplinary norms and practices?
2. Context and environment
Next, we looked at the contexts and environments in which tech work is typically performed, providing students with research-supported understanding of the current data and AI landscape, and what social, organisational, and disciplinary norms are shaping technology development and its social impact.
3. Guest speakers and practical application
In the final section of the course, we gave students practical examples of translational approaches to data and AI ethics, through a range of presentations and discussions with researchers and practitioners actively engaged in what we see as translational work.
To help ground everything we had taught them, we ended the course with a practical activity where groups of Translational Data and AI Ethics students were paired up with undergraduate students from the School of Informatics part way through the development of start-up style projects. This provided both sets of students with a rare opportunity to develop a shared language, and to practice the kinds of interdisciplinary conversations we hope to make more commonplace.
What did we learn?
The student response to the course was exceptional from the start, with students expressing excitement about the curation of readings provided as well as the practical – yet theoretically-challenging – nature of the course activities. Furthermore, after the completion of the course, several students chose to revisit their dissertation project ideas to include more elements of translational ethics.
By creating space for rich and thoughtful conversation with students, this course gave us an opportunity to reflect on some of the broader challenges to the implementation of translational ethics teaching programmes within more traditional computer and data science degrees. As already observed by others (Sarder and Fiesler 2022; Darling-Wolf and Patitsas 2024), even within institutions and programmes that do offer ethical training, technical skills are often still taught as if entirely abstract and stripped of their social and ethical significance.
Some students, conversely, hoped that the course would include technical methods to implement ethics in code. We noted however that, while fairness metrics can serve as helpful tools to measure algorithmic bias and discrimination, these alone are not sufficient to mitigate the potential impact of data-driven practices (Corbett-Davies et al. 2023). ‘Representative’ data and ‘accurate’ predictions can still cause harm when AI systems are used in contexts that disproportionately affect certain groups over others, such as surveillance and policing (Eubanks 2014; Browne 2015). Translational ethics, then, requires that technologists go beyond technical and statistical fairness, and situate technical approaches to AI and data ethics within the organisational, social and political contexts of technology development and use.
Towards a translational ethics agenda
The course presented an opportunity to reflect on wider institutional and cultural challenges to the implementation of a translational ethics teaching agenda. First, translational ethics requires sufficient engagement with both qualitative and quantitative forms of knowledge, contrary to assumptions typical within engineering fields over the epistemological superiority of ‘hard science’ over ‘soft’ knowledge (Raji, Scheuerman, and Amironesei 2021). Secondly, students need to be taught to understand and identify AI ethics as contextual and specific, grounded in issues of social justice (Munn 2022; Amugongo et al. 2023). Finally, further research needs to be done into what does or doesn’t work in current translational contexts, so that we know which approaches are most likely to be successful.
Into the future
Based on this experience, it is clear that engaging more meaningfully with the norms and processes involved in standard software development cycles would be beneficial to students without a technical background. We will continue to run this course again next year, and are keen to contribute further to the growing body of research literature which led to the design of the course.
This blog post is adapted from the original blog post available at Bridging the Gap: Reflections from teaching translational data and AI ethics — Centre for Technomoral Futures.
References
- Amugongo, L.M., Kriebitz, A., Boch, A., and Lütge, C. 2023. ‘Operationalising AI Ethics through the Agile Software Development Lifecycle: A Case Study of AI-Enabled Mobile Health Applications’. AI and Ethics, August. https://doi.org/10.1007/s43681-023-00331-3.
- Bærøe, K. 2014. ‘Translational Ethics: An Analytical Framework of Translational Movements between Theory and Practice and a Sketch of a Comprehensive Approach’. BMC Medical Ethics 15 (1): 71. https://doi.org/10.1186/1472-6939-15-71.
- Borg, J. 2022. ‘The AI Field Needs Translational Ethical AI Research’. AI Magazine 43 (3): 294–307. https://doi.org/10.1002/aaai.12062.
- Browne, S. 2015. Dark Matters: On the Surveillance of Blackness. Duke University Press.
- Corbett-Davies, S., Gaebler, J.D., Nilforoshan, H., Shroff, R., and Goel, S. 2023. ‘The Measure and Mismeasure of Fairness’. arXiv. https://doi.org/10.48550/arXiv.1808.00023.
- Darling-Wolf, H, and Patitsas, E. 2024. ‘“Not My Priority:” Ethics and the Boundaries of Computer Science Identities in Undergraduate CS Education’. Proc. ACM Hum.-Comput. Interact. 8 (CSCW1): 174:1-174:28. https://doi.org/10.1145/3641013.
- Downey, G. L. 2021. ‘Critical Participation: Inflecting Dominant Knowledge Practices through STS’. In Making & Doing. Activating STS through Knolwedge Expression and Travel, edited by Gary Lee Downey and Teun Zuiderent-Jerak. The MIT Press. https://direct.mit.edu/books/oa-edited-volume/5153/chapter/3403234/Critical-Participation-Inflecting-Dominant.
- Eubanks, V. 2014. ‘Want to Predict the Future of Surveillance? Ask Poor Communities.’ The American Prospect. 15 January 2014. https://prospect.org/api/content/36656b9e-c446-5205-9257-0120f64aabdb/.
- Goetze, T. S. 2023. ‘Integrating Ethics into Computer Science Education: Multi-, Inter-, and Transdisciplinary Approaches’. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, 645–51. SIGCSE 2023. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3545945.3569792.
- Hostiuc, S., Moldoveanu, A., Dascălu, M-J., Unnthorsson, R., Jóhannesson, Ó.I., and Marcus, I. 2016. ‘Translational Research—the Need of a New Bioethics Approach’. Journal of Translational Medicine 14 (1): 16. https://doi.org/10.1186/s12967-016-0773-4.
- Munn, L. 2022. ‘The Uselessness of AI Ethics’. AI and Ethics, August. https://doi.org/10.1007/s43681-022-00209-w.
- Raji, D.I., Klaus Scheuerman, M., and Amironesei, R. 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, 515–25. FAccT ’21. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3442188.3445914.
- Sarder, E, and Fiesler., C 2022. ‘Entering the Techlash: Student Perspectives on Ethics in Tech Job Searches’. In Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing, 85–88. CSCW’ 22 Companion. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3500868.3559446.
James Garforth
Dr James Garforth (CTMF Senior Research Affiliate) teaches ethics, social responsibility and teamwork to undergraduate students in the School of Informatics, and supervises projects to develop tools and practices supportive of responsible development.
Benedetta Catanzariti
Dr Benedetta Catanzariti (CTMF Postdoctoral Affiliate) is a British Academy Postdoctoral Fellow in Science, Technology and Innovation Studies. Her work explores the social, ethical, and political dimensions of data-driven technologies, with a focus on machine learning and its related data practices.
Meenakshi Mani
Meenakshi Mani (CTMF PhD Fellow) is an interdisciplinary researcher with experience in the fields of computer science and education who is critically examining how EdTech engineers conceptualise and construct AI education technologies.
