Ethics of Artificial Intelligence

Description

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.

Syllabus :

• 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

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