Elixir, Erlang, and the Quest for AI Justice - Building Unbiased ML Systems

Overview

Explore how Elixir and Erlang can be leveraged to build unbiased machine learning systems, addressing algorithmic discrimination and promoting fairness in AI development.

Syllabus

• Introduction to Elixir and Erlang
Brief history and evolution
Key features and benefits in AI development
Comparison with other programming languages in the AI landscape
• Basics of Machine Learning
Core concepts and terminologies
Overview of common ML algorithms
Evaluation metrics and model performance
• Algorithmic Bias in Machine Learning
Understanding bias and discrimination in AI systems
Case studies of biased AI models
Impact of biased algorithms on society
• Leveraging Elixir and Erlang for ML
Concurrency and fault tolerance in AI systems
Integration with machine learning libraries and frameworks
Real-time data processing and analysis
• Building Unbiased ML Systems
Techniques for identifying and mitigating bias
Fairness-aware algorithms and model adjustments
Tools for bias detection and correction in Elixir and Erlang
• Developing Fair AI Applications
Best practices for ethical AI development
Ensuring transparency and accountability in AI systems
Incorporating diverse datasets and perspectives
• Practical Projects and Case Studies
Hands-on projects using Elixir and Erlang
Analysis of the effectiveness of bias mitigation strategies
Discussion of real-world applications promoting AI fairness
• Future Directions in AI Justice
Emerging trends in AI fairness and ethics
Policy and regulatory frameworks for AI development
The role of technology in fostering social justice
• Course Review and Final Project
Summary of key learnings
Presentation and critique of final projects
Discussion of potential career paths and impacts in AI ethics and fairness

Subjects

Programming