Undergraduate Course: Algorithmic Bias (PHIL10235)
Course Outline
| School | School of Philosophy, Psychology and Language Sciences |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course covers the major conceptual, ethical, and legal questions concerning algorithmic bias. |
| Course description |
This course covers a range of questions about algorithmic bias. Questions that may be covered in a given semester include: What is bias? What are the sources of bias in algorithms? How can algorithmic bias be combatted? Which fairness metrics, which attempt to quantify fairness in algorithmic systems, genuinely capture fairness? What are the limits of such metrics? How does replacing human decision-makers with algorithms change our understanding of discrimination?
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Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 22,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
174 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Essay (75%)
Algorithmic bias analysis report (25%) |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate knowledge of philosophical issues involved in algrotihmic bias
- Demonstrate familiarity with relevant examples of algorithmic systems
- Demonstrate ability to bring philosophical considerations to bear in practical contexts
- Demonstrate skills in research, analysis and argumentation
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Reading List
A representative list of readings is:
Safiya Noble, Algorithms of oppression
Batya Friedman and Helen Nissenbaum, Bias in computer systems
Solon Barocas and Andrew Selbst, Big data's disparate impact
Anya Prince and Daniel Schwarcz, Proxy discrimination in the age of artificial intelligence and big data
Gabrielle Johnson, The hard proxy problem
Lily Hu, What is 'race' in algorithmic discrimination on the basis of race?
Thomas Kelly, Bias: a philosophical study
Brian Hedden, On statistical criteria of algorithmic fairness
Deborah Hellman, Big data and compounding injustice
Seth Lazar and Jake Stone, On the site of predictive injustice
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Additional Information
| Graduate Attributes and Skills |
Mindsets: Enquiry and lifelong learning; Outlook and engagement.
Skills: Personal and intellectual autonomy; Communication. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Milo Phillips-Brown
Tel:
Email: milopb@ed.ac.uk |
Course secretary | Ms Joan MacKenzie
Tel:
Email: jmacken8@ed.ac.uk |
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