Postgraduate Course: Algorithmic Bias, Fairness and Justice (fusion online) (EFIE11168)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course introduces students to one of the most important challenges in academia and industry for making data-driven AI systems ethical: algorithmic bias. By drawing on interdisciplinary perspectives from computing, statistics, political and legal philosophy, and feminist theory, students will learn about current methods for improving fairness in AI and machine learning systems, as well as the limits and opportunities of algorithmic fairness in relation to the aims of social and distributive justice. |
| Course description |
Data-driven machine learning systems learn from hidden patterns in existing data and risk reproducing and amplifying social patterns of bias reflected in this data, such as racial or gender bias. Algorithmic fairness is an emerging research area for removing these wrongful societal biases in the hope of making data-driven AI systems ethical. This course introduces students to issues of bias, fairness, and justice in data-driven machine learning systems by drawing on a diverse set of perspectives from computing, statistics, political and legal philosophy, and feminist theory in order to raise critical awareness and understanding of these issues as well as equipping students with tools to mitigate such problems.
Topics include sources of bias in data and machine learning, methods for measuring and mitigating bias and unfairness, notions of individual and group fairness and the tensions between them, limitations of algorithmic fairness approaches, and philosophical accounts of distributive and structural justice relevant to machine learning systems. Learning in an innovative hybrid and intensive mode that brings together online and in-person students, you will work together in collaborative groups to practice the identification and evaluation of algorithmic bias concerns in concrete cases; students will also practice jointly deliberating about and communicating the benefits and limits of different methods, techniques and approaches to algorithmic fairness and justice.
Edinburgh Futures Institute (EFI) - Online 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 note that their interactions may be recorded and live-streamed. There will, however, be options to control whether or not your video and audio are enabled.
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.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2023/24, Available to all students (SV1)
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Quota: 8 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 16,
Formative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
80 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Summative Assessment:
The course will be assessed by means of the following assessment components:
1) 500 Word Mock 'Authors Ethics Statement' [OR] 500-Word Mock 'Ethics Advisory Panel' Assessment (30%)
Students will be divided into groups to either work in a small group to write a 500-word mock 'authors ethics statement' for an AI research paper that presents significant fairness and bias concerns, or to work in a small group to write a 500-word mock 'ethics advisory panel' assessment of the same paper, which will reply to one of the mock authors statements produced by another group. This assessment practices skills needed for real-world AI research and development, as leading computing conferences are increasingly requiring such authors statements and ethics panel reviews. This is due at the end of the post-intensive phase and counts for 30% of the course mark.
2) 1500 Word 'Fairness Risk Report' (70%)
Students will also produce an individual 1500 word 'fairness risk report' on a selected case study involving an algorithmic model about which there are evident fairness and bias concerns. The report must explicitly identify and weigh relevant harms and trade-offs and the interests of impacted groups, consider multiple types of possible interventions, and include and justify at least three recommendations for further consideration, which may advise technical, organisational, policy, regulatory or political interventions on the bias challenge. The report must also include a reflection on the assumptions, limitations and uncertainties within the report that may invite critique of the recommendations. This report will be due three weeks after the intensive period and will constitute 70% of the course mark. |
| Feedback |
Formative feedback will be provided in the immersive phase for the asynchronous groups and to individuals in the Q&A session, when the course organiser will jointly help to shape the understandings of students of the core issues and the first collaborative task.
Additional live formative feedback will be given on the group presentations of the case studies during the second half of the Day 1 intensive. This feedback will invite a general class discussion of group dynamics and project management to address any potential difficulties groups may encounter. (The course organiser will also be available to meet with groups to discuss work in progress and mediate any significant problems or disagreements within the group that cannot be resolved internally.)
Written summative feedback will be provided on the group and individual summative assessments, following the post-intensive application phase. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a basic understanding of key concepts, theories, metrics, and principles of bias, fairness, and justice from statistics, machine learning, feminist, legal, and political philosophy.
- Critically discuss and evaluate a variety of perspectives in debates on how various conceptions of bias, fairness, and justice are to be used for the design of ethical data and AI ecosystems.
- Work constructively with others to weigh bias, fairness and justice considerations and identify potential remedies and interventions for a concrete instance of a data-driven machine learning model.
- Produce and clearly communicate for non-specialists a basic analysis and advisory output pertaining to bias, fairness, and justice in a concrete data-driven AI application.
- Identify and critically evaluate the technical and moral trade-offs involved in decisions about which fairness metrics or interventions to employ in a given AI application context, while weighing these against the broader aims of justice.
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Reading List
Essential Reading:
Fairness and Machine Learning: Limitations and Opportunities. MIT Press. 2022/3.
Machine Bias Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, ProPublica. 2016.
Limitations of mitigating judicial bias with machine learning Kristian Lum. 2017.
Fairness in Criminal Justice Risk Assessments: The State of the Art Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, Aaron Roth. 2021.
Fairness in Machine Learning: lessons from political philosophy Binns. 2018.
Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse Hoffman. 2019
Algorithmic fairness from a non-ideal perspective Fazelpour and Lipton. 2020.
Responsible Algorithmic Fairness: insights from feminist political philosophy. Kasirzadeh. 2022.
Recommended Reading:
Algorithmic fairness: Choices, assumptions, and definitions Mitchell et al. 2021.
Courts and Predictive Algorithms Angèle Christin, Alex Rosenblat, and danah boyd. 2015.
Further Reading:
COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity Northpointe Inc. |
Additional Information
| 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.
- 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 or research output, as related to both design and use contexts.
- 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
- Management of complex ethical and professional issues and informed judgement on issues not addressed by current professional and/or ethical codes or practices. |
| Keywords | Algorithmic Bias,Algorithmic Fairness,Algorithmic Justice,Fair Machine Learning,PG,EFI,Level 11 |
Contacts
| Course organiser | Dr Atoosa Kasirzadeh
Tel:
Email: atoosa.kasirzadeh@ed.ac.uk |
Course secretary | Miss Veronica Silvestre
Tel:
Email: Veronica.Silvestre@ed.ac.uk |
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