This case study by Bangli Liu and Eufrásio Lima Neto (Module leader and lecturer, respectively, for Big Data Applications – CSIP5203) provides an overview of a seminar students engaged in on fairness and bias mitigation in ML models. Discussion was based on the paper “Ethical AI for Enhancing Decision-Making Processes in Young People Requiring Early Help Service”.
Article available at: https://link.springer.com/article/10.1007/s42001-023-00242-7
What happened?
Context: Discuss about important concepts about Legal, Privacy & Ethics in AI and Fairness and Bias Mitigation in ML algorithms
Description: A case study is presented addressing some topics about Legal, Privacy & Ethics in AI and Fairness and Bias Mitigation in ML algorithms
Evaluation: Students are involved in a discussion about the theme presenting your insights and thoughts.
Who was involved?
100 MSc Data Analytics students enrolled on the 7-week block module ‘Big Data Applications’ (CSIP5203).
What sustainability themes were covered?
Main theme: Social Justice and Ethics.
Lectures were delivered regarding Legal, Privacy & Ethics in AI and a seminar was held on Fairness and Bias Mitigation on ML algorithms.
Specific topics covered included:
- Legal Issues
- Privacy Issues
- Ethics Issues
- Fairness and Bias Mitigation
How has Problem Based Learning been used to embrace sustainability?
Part 1 – the following topics are covered:
- Legal concerns related to AI
- Deal with Legal concerns in AI
- Privacy Issues in AI
- Combat Privacy Issues in AI
- Ethics Issues
- Bias and Fairness
- Key Measures for Mitigating Bias
- Data Manipulation and Visualization
Part 2 – A case study is presented addressing some topics covered on Part 1
What are the successes from this work?
It’s important that students understand and reflect about the responsibility, consequences and impact of the AI and ML algorithms on the society.