Module Details
Module Code: |
FALT |
Module Title:
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Bias in Computational Systems
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Title:
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Bias in Computational Systems
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Module Level:: |
8 |
Module Coordinator: |
Nigel Whyte
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Module Author:: |
Christopher Staff
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Module Description: |
To develop learners' theoretical knowledge of bias in computational systems and the harm it can cause; to provide practical skill to perform analyses to detect and mitigate or compensate for bias in everyday tools learners use to support their own decision making, and to design human-centric and fair computational systems.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Identify and describe how bias may present in real-world computational systems |
LO2 |
Devise a strategy to mitigate bias in a real-world computational system |
LO3 |
Evaluate the ongoing final year project to identify potential bias and formulate a plan to address and mitigate it, to ensure fairness in its outcome |
Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Additional Requisite Information
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No Co Requisites listed
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Indicative Content |
Understanding bias
Bias and poor decision making; examples of bias in business and everyday life; is all bias unfair?; can we be influenced to make biased decisions?
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Identifying bias in computational systems
Case studies; who is being harmed?; stakeholder analysis; critical thinking; bias detection strategies.
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Machine Learning and Bias
Brief introduction to machine learning; algorithmic bias; bias toolkits.
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Mitigating bias in computational systems
Compensating for bias in computational systems.
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Designing fair computational systems
Human-centred vs. data-centred algorithm design; bias impact statements.
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Module Content & Assessment
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Assessment Breakdown | % |
Continuous Assessment | 60.00% |
Project | 40.00% |
AssessmentsFull Time
No End of Module Formal Examination |
Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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Reassessment Description Decided by module academic in conjunction with programme board. Repeat of coursework and/or written examination or other repeat mechanism as appropriate dependent on students performance and module engagement.
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SETU Carlow Campus reserves the right to alter the nature and timings of assessment
Module Workload
Workload: Full Time |
Workload Type |
Workload Category |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
Lecture |
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Contact |
No Description |
12 Weeks per Stage |
2.00 |
24 |
Independent Learning |
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Non Contact |
No Description |
15 Weeks per Stage |
5.13 |
77 |
Practicals |
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Contact |
No Description |
12 Weeks per Stage |
2.00 |
24 |
Total Weekly Contact Hours |
4.00 |
Module Resources
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This module does not have any book resources |
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Recommended Article/Paper Resources |
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Friedman & Nissenbaum. (1996), Bias in Computer Systems, ACM Transactions on Information Systems, Vol. 14, No. 3,
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Mehrabi, Morstatter, Saxena, Lerman, and
Galstyan. (2019), A Survey on Bias and Fairness in Machine
Learning, arXiv, arXiv:1908.09635,
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Baumer. (2017), Toward human-centered algorithm design, Big Data & Society, July–December 2017,
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Iwasiński. (2020), Social Implications of Algorithmic Bias,
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Cramer, H., Garcia-Gathright, J.,
Springer, A., & Reddy, S.. (2018), Assessing and addressing algorithmic
bias in practice, Interactions, 25(6),
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Dale, S.. (2015), Heuristics and biases: The science of
decision-making, Business Information Review, 32(2),
| This module does not have any other resources |
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Discussion Note: |
This module is proposed as an elective in the final year of the semesterised BSc (Hons) degree programmes offered by the Department of Computing. |
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