FALT - Bias in Computational Systems

Module Title:Bias in Computational Systems
Language of Instruction:English
Credits: 5
NFQ Level:8
Module Delivered In 5 programme(s)
Teaching & Learning Strategies: 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 Assessment60.00%
Project40.00%
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

ITCarlow reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Frequency Average Weekly Learner Workload
Lecture 12 Weeks per Stage 2.00
Independent Learning 15 Weeks per Stage 5.13
Practicals 12 Weeks per Stage 2.00
Total Hours 125.00
 

Module Delivered In

Programme Code Programme Semester Delivery
CW_KCCGD_B Bachelor of Science (Honours) in Computer Games Development 8 Group Elective 1
CW_KCIAD_B Bachelor of Science (Honours) in Computing in Interactive Digital Art and Design 8 Elective
CW_KCCYB_B Bachelor of Science (Honours) in Cyber Crime and IT Security 8 Elective
CW_KCCIT_B Bachelor of Science (Honours) in Information Technology Management 8 Group Elective 1
CW_KCSOF_B Bachelor of Science (Honours) in Software Development 8 Group Elective 1
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.