Content will be delivered to learners through lectures with class interaction; discussion around case studies and role playing; through the use of and application of bias toolkits; supported by practical sessions with reflection and critiquing of practical session outcomes; learners will be expected to actively participate in class and work throughout to accomplish assigned tasks.
Module Aim:
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 should be able to:
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
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed.
No requirements listed
Module Content & Assessment
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
%
Continuous Assessment
60.00%
Project
40.00%
Continuous Assessment
Assessment Type
Assessment Description
Outcome addressed
% of total
Assessment Date
Multiple Choice Questions
n/a
1
10.00
Week 3
Case Studies
n/a
1
20.00
Week 6
Written Report
n/a
2
20.00
Week 8
Other
Contribution to in-class discussions
1,2,3
10.00
n/a
Project
Assessment Type
Assessment Description
Outcome addressed
% of total
Assessment Date
Project
n/a
3
40.00
n/a
No Practical
No End of Module Formal Examination
SETU Carlow Campus reserves the right to alter the nature and timings of assessment