Machine Learning: Privacy, Regulations and Ethical Issues

Announcements 

• For literature also have a look at human computer interaction (HCI) conferences: https://hci-deadlines.github.io/?sub=HCI e.g. FAccT: https://facctconference.org/2024/acceptedpapers (currently unranked)
• We will meet 29.10.2024 at 8:15 am in RuW 2.202 

Literature (How to conduct a literature review):

PRISMA-S

PRISMA

From Bias to Balance – A Systematic Literature Review on Gender Bias in AI (example)

Document Analysis as a Qualitative Research Method

Analyzing the Past to Prepare for the Future

Timetable

Learning Goals

• Ability to understand and perform a systematic literature review
• Basic understanding of different Machine Learning approaches
• Basic understanding of Regulation and Privacy in Machine Learning
• Basic understanding of Ethical Issues in Machine Learning
• Demonstrate good writing and presentation skills
• Demonstrate good organizational skills and collaboration in working in groups

Module Description

Machine Learning is becoming more and more important in daily life applications such as self-driving cars or communication assistants. To get these applications working, a lot of data is required what is the reason why, many countries already restrict and regulate the handling and usage of personal data by data protection regulations such as the EU GDPR. Besides the handling of private data, also a lot of ethical questions, such as the demand for fair AI emerge. The biggest challenge at present is opening new markets while at the same time meeting the ethical, privacy and regulatory requirements.

Already, a variety of new technologies that enable privacy preserving machine learning have emerged during the recent years. These techniques aim to protect machine learning models from a variety of attacks that try to reveal data, training features, or the algorithm itself. Also, with regards to fairness, different approaches exist to define rules for a fair AI application that will be analysed and compared within this seminar. 

Objectives:

• Understand privacy issues and possible solutions for Machine Learning applications
• Understand ethical issues and possible solutions for Machine Learning applications
• Understand the aim and need for Machine Learning regulations

Topics are in the area of:

1. Analysis of different Machine Learning applications
2. Ethical issues in Machine Learning
3. Fairness and data Bias
4. Privacy Preserving Machine Learning Techniques
5. Machine Learning in 6G