In this course, you will :
- Explain the context and motivation behind ethical AI.
- Create an ethical AI perspective to better understand our thinking's strengths and weaknesses.
- Explain the impact of bias and fairness on AI decision-making.
- Identify and create ethical AI pipelines, guidelines, and frameworks for your organisation.
- Components of ethical governance initiatives should be articulated.
- Determine the different types of data and machine learning (ML) bias and where they are used.
- Identify potential drawbacks in AI solutions.
- Define the problem statements and priorities for AI fairness.
- Apply methodologies for detecting bias in data and AI models.
- Use metrics to assess AI bias and fairness.
- Using AI lifecycle phases and negative feedback loops, identify bias and fairness.
- Analyze the benefits and drawbacks of bias and fairness mitigation strategies and metrics.
- Put mitigation strategies in place to improve the fairness of AI models and solutions.
- Considerations for designing and building data and models with increased fairness should be articulated.
- Articulate legal programme elements pertaining to data privacy, AI security, and transparency.
- Create compliance metrics to ensure responsible data governance.
- Explain what explainability is in AI/ML and how to apply solutions.
- Using compliance and industry standard documentation, communicate trust to customers and users of AI/ML systems.
- Determine the mechanisms for ethical AI auditing.
- Introduction to Ethical AI
- AI Ethics for Organizations
- Identifying Bias Towards Fairness
- Mitigating Bias Towards Fairness
- Transparency, Trust, and Explainability
- Course Project: AI Ethics for Personalized Budget Prediction