SETU Curriculum - FALT - Bias in Computational Systems

Module Details

Module Code:	FALT
Module Title:	Bias in Computational Systems
Title:	Bias in Computational Systems
Module Level::	8

Credits::	5

Module Coordinator:	Nigel Whyte

Module Author::	Christopher Staff

Domains:

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.

Learning Outcomes
On successful completion of this module the learner will be able to:
#	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.
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed

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?
Identifying bias in computational systems Case studies; who is being harmed?; stakeholder analysis; critical thinking; bias detection strategies.
Machine Learning and Bias Brief introduction to machine learning; algorithmic bias; bias toolkits.
Mitigating bias in computational systems Compensating for bias in computational systems.
Designing fair computational systems Human-centred vs. data-centred algorithm design; bias impact statements.

Assessment Breakdown	%
Module Content & Assessment
Continuous Assessment	60.00%
Project	40.00%

Assessments

Full Time

Continuous Assessment

Assessment Type	Multiple Choice Questions	% of Total Mark	10
Timing	Week 3	Learning Outcomes	1
Non-marked	No
Assessment Description n/a

Assessment Type	Case Studies	% of Total Mark	20
Timing	Week 6	Learning Outcomes	1
Non-marked	No
Assessment Description n/a

Assessment Type	Written Report	% of Total Mark	20
Timing	Week 8	Learning Outcomes	2
Non-marked	No
Assessment Description n/a

Assessment Type	Other	% of Total Mark	10
Timing	n/a	Learning Outcomes	1,2,3
Non-marked	No
Assessment Description Contribution to in-class discussions

Project

Assessment Type	Project	% of Total Mark	40
Timing	n/a	Learning Outcomes	3
Non-marked	No
Assessment Description n/a

No Practical

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.
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.

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		Contact	No Description	12 Weeks per Stage	2.00	24
Independent Learning		Non Contact	No Description	15 Weeks per Stage	5.13	77
Practicals		Contact	No Description	12 Weeks per Stage	2.00	24
Total Weekly Contact Hours						4.00

This module does not have any book resources
Module Resources
Recommended Article/Paper Resources
Friedman & Nissenbaum. (1996), Bias in Computer Systems, ACM Transactions on Information Systems, Vol. 14, No. 3, https://dl.acm.org/doi/10.1145/230538.23 0561 Mehrabi, Morstatter, Saxena, Lerman, and Galstyan. (2019), A Survey on Bias and Fairness in Machine Learning, arXiv, arXiv:1908.09635, https://arxiv.org/abs/1908.09635 Baumer. (2017), Toward human-centered algorithm design, Big Data & Society, July–December 2017, https://journals.sagepub.com/doi/full/10 .1177/2053951717718854 Iwasiński. (2020), Social Implications of Algorithmic Bias, https://www.researchgate.net/publication /349120634_SOCIAL_IMPLICATIONS_OF_ALGORI THMIC_BIAS Cramer, H., Garcia-Gathright, J., Springer, A., & Reddy, S.. (2018), Assessing and addressing algorithmic bias in practice, Interactions, 25(6), https://interactions.acm.org/archive/vie w/november-december-2018/assessing-and-a ddressing-algorithmic-bias-in-practice Dale, S.. (2015), Heuristics and biases: The science of decision-making, Business Information Review, 32(2), https://www.researchgate.net/profile/Ste phen-Dale/publication/281232107_Heuristi cs_and_biases_The_science_of_decision-ma king/links/5d4d3d9c4585153e5947c61d/Heur istics-and-biases-The-science-of-decisio n-making.pdf
This module does not have any other resources

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.