Artificial Intelligence (AI) is transforming companies worldwide, but AI fairness and bias issues remain of critical concern. The AI Bias and Fairness program, offered by the Oxford Training Centre, helps professionals learn and avoid algorithmic bias in AI systems. Through the program, the participants will be taught the ethical implications of biased AI models, AI explainability and fairness, and ethical AI development. After taking the course, participants will have the ability and capability to address AI discrimination issues and guarantee fairness in AI decision-making. The course is comprehensive and provides the requisite knowledge in AI ethics, governance, and accountability, making it a priority for professionals working with AI technologies.
Objectives and Target Group
Objectives
- Understand AI bias and fairness in machine learning applications.
- Identify algorithmic bias in AI and its impact on decision-making.
- Explore ethical AI principles and responsible AI development.
- Learn strategies to improve AI fairness and reduce bias in AI models.
- Assess AI fairness in hiring, recruitment, healthcare, and finance sectors.
- Examine AI regulations and ethical guidelines for AI governance.
- Develop fair AI systems with transparency and accountability.
- Address AI trustworthiness and explainability to enhance user confidence.
Target Group
This course is ideal for professionals and stakeholders involved in AI development, ethics, and regulatory compliance. It is specifically designed for:
- AI engineers and data scientists working on AI fairness and bias mitigation.
- Business leaders and decision-makers implementing AI-driven solutions.
- Policy makers and regulators interested in AI accountability and governance.
- HR professionals addressing AI fairness in hiring and recruitment processes.
- Healthcare and finance professionals concerned with AI bias in decision-making.
- Academics and researchers studying AI ethics and responsible AI development.
Course Content
The AI Bias and Fairness course provides learners with a comprehensive understanding of AI ethics, fairness, and transparency through an in-depth exploration of key topics.
1. Introduction to AI Bias and Fairness
- Defining AI fairness and bias
- AI discrimination problems
- The importance of ethical AI and responsible AI development
2. Algorithmic Bias in AI
- Bias detection and examination in AI algorithms
- The impact of AI data bias on decision-making
- Case studies of AI bias in machine learning models
3. AI Fairness and Transparency
- AI fairness evaluation techniques
- Minimizing bias in AI models: approaches
- Enhancing AI trustworthiness and explainability
4. Bias in AI Applications: Case Studies
- AI bias in recruitment and hiring processes
- Mitigating AI bias in healthcare and finance
- AI and social bias: ethical concerns and solutions
5. AI Accountability and Governance
- Laws and ethical guidelines for AI
- Fair AI systems development and compliance
- AI governance models and best practices
6. Strategies to Improve AI Fairness
- Techniques for creating unbiased AI models
- How various datasets are employed to reduce AI bias
- Fairness of AI in automated decision-making
7. Future Trends in AI Fairness and Ethics
- Emerging research in AI fairness
- The role of AI ethics in AI policy
- Sustaining long-term AI fairness and accountability