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A step toward building a unified framework for managing AI bias
Saadia Afzal Rana1, Zati Hakim Azizul1, Ali Afzal Awan2
1Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
Peerj. Computer Science
|December 11, 2023
Summary
Artificial intelligence (AI) development faces bias challenges. This study proposes a unified framework for AI fairness management, categorizing biases and mitigation strategies to ensure equitable AI systems.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Artificial intelligence (AI) integration has improved living standards but faces significant challenges due to inherent biases and unfairness.
- Addressing these biases is crucial for the responsible development and deployment of AI technologies.
Purpose of the Study:
- To evaluate existing knowledge on fairness management in AI development.
- To establish a foundation for a unified framework to address and mitigate AI bias throughout the AI development pipeline.
Main Methods:
- Mapping the Software Development Life Cycle (SDLC), Machine Learning Life Cycle (MLLC), and Cross-Industry Standard Process for Data Mining (CRISP-DM) to understand development phases.
- Categorizing biases into pre-existing, technical, and emergent types.
- Identifying mitigation strategies including conceptual, empirical, and technical approaches, alongside fairness sampling, learning, and certification.
Main Results:
- A comprehensive overview of AI bias types and corresponding mitigation strategies.
- A proposed structure for a unified framework to manage AI fairness across the development lifecycle.
- Identification of recommended practices for bias mitigation and overcoming implementation challenges.
Conclusions:
- A unified framework is essential for systematically managing AI fairness.
- Understanding the interplay between development lifecycles and bias is key to effective mitigation.
- The proposed categorization and strategies provide a roadmap for developing equitable AI systems.
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