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Ethical Computing in Practice:  2022-2023



Schedule C1 (CS&P)Computer Science and Philosophy

Schedule C1Computer Science

Schedule C1Mathematics and Computer Science

Hilary TermMSc in Advanced Computer Science



This course is intended for students who want to integrate considerations of ethics and social responsibility into their own practice as computing practitioners. It provides (i) an introduction to some of the most common ethical challenges encountered by computing professionals, (ii) a step-by-step guide on how to identify, address, and communicate ethical dimensions of the student’s own research, and (iii) an overview over various frameworks that can be used to integrate ethics into research design and development.

Learning outcomes

1. Understand the ethical relevance of computing technologies 2. Attain familiarity with some of the most common ethical challenges encountered by computing practitioners, including algorithmic bias, privacy, accountability, misuse, and conflicts of interest. 3. Identify ethically significant harms and benefits of the student’s own research through risk analysis and stakeholder analysis 4. Develop best practice that aligns the project with societal values and address potential ethically significant harms


The following topics will be covered by the course: 

    • Why ethics? Responsible research in practice 
    • Algorithmic bias and fairness 
    • Privacy 
    • Conflicts of interest 
    • Risk management 
    • Stakeholder analysis and engagement 
    • Goal setting 
    • Data acquisition and management 
    • Publishing research 
    • Professional responsibilities 
    • Value-sensitive design 
    • Responsible Research and Innovation 

Reading list

The lectures, classes, and practicals, will be supported by course materials (slides and video lectures), supplemented by additional papers and book excerpts from a range of disciplines including value-sensitive design, philosophy, science and technology studies, and responsible innovation. These readings will be made available for download from the course web page. A sample of readings may include:

  • Barocas, S. and Selbst, A. (2016). “Big data’s disparate impact.” California Law Review. 104: 671-732.
  • Costanza-Chock, S. (2020). Design justice. MIT Press.
  • Friedman, B. and Hendry, D. (2019). Value-sensitive design. MIT Press.
  • Hellman, D. (2019). “Measuring algorithmic fairness.” Virginia Public Law and Theory Legal paper.
  • Hedden, B. (2021). “On statistical criteria of algorithmic fairness.” Philosophy and Public Affairs. 49(2): 209-231
  • Stilgoe, J., Owen, R., and Macnaghten, P. (2013). “Developing a framework for responsible innovation.” Research Policy. 42(9): 1568-1580.
  • Suresh, H. and Guttag, J. (ms). “A framework for understanding unintended consequences of machine learning.”


Students are formally asked for feedback at the end of the course. Students can also submit feedback at any point here. Feedback received here will go to the Head of Academic Administration, and will be dealt with confidentially when being passed on further. All feedback is welcome.