15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Scientists
- AI Developers
- Policy Makers
- Data Analysts
- Machine Learning Engineers
- Business Analysts
- Anyone needing data ethics and responsible AI skills
Session Objectives
- Understand the fundamentals of data ethics and responsible AI.
- Master bias detection and mitigation techniques in data and models.
- Utilize fairness metrics to evaluate AI system performance.
- Implement transparency and explainability in AI applications.
- Design and build ethical data science workflows.
- Optimize AI systems for compliance with ethical guidelines and regulations.
- Troubleshoot and address common ethical challenges in data science.
- Implement privacy-preserving techniques in data analysis.
- Understand how to handle sensitive data and ensure data governance.
- Integrate ethical considerations into real-world AI projects.
- Explore advanced ethical frameworks and regulatory landscapes.
- Apply real world use cases for data ethics and responsible AI.
- Leverage ethical AI tools and frameworks for efficient development.
About the Course
Operationalize fairness, transparency, and accountability across your Machine Learning lifecycles with our Data Ethics and Responsible AI Training Course. Tailored for data scientists, AI engineers, and compliance leaders, this hands-on program equips you to identify, audit, and mitigate algorithmic bias in modern AI models. Guided by ethical AI framework experts, you will master fairness metrics, model explainability (XAI), privacy preservation, and regulatory compliance standards—enabling your team to deploy trustworthy, governance-aligned AI systems that mitigate reputational and legal risk.
Curriculum & Topics
15 Topics | 74 Sessions
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Workshop 1.1:
Fundamentals of data ethics and responsible AI.
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Workshop 1.2:
Overview of ethical principles, bias detection, and fairness metrics.
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Workshop 1.3:
Setting up an ethical data science development environment.
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Workshop 1.4:
Introduction to ethical AI tools and frameworks.
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Workshop 1.5:
Best practices for ethical data science.
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Workshop 2.1:
Implementing bias detection techniques in data and models.
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Workshop 2.2:
Utilizing statistical tests and fairness audits.
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Workshop 2.3:
Designing and building bias mitigation strategies.
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Workshop 2.4:
Optimizing models for fairness and equity.
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Workshop 2.5:
Best practices for bias detection.
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Workshop 3.1:
Utilizing fairness metrics to evaluate AI system performance.
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Workshop 3.2:
Implementing metrics such as demographic parity and equalized odds.
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Workshop 3.3:
Designing and building fair evaluation frameworks.
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Workshop 3.4:
Optimizing fairness metrics for specific applications.
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Workshop 3.5:
Best practices for fairness metrics.
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Workshop 4.1:
Implementing transparency and explainability in AI applications.
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Workshop 4.2:
Utilizing explainable AI (XAI) techniques.
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Workshop 4.3:
Designing and building transparent model documentation.
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Workshop 4.4:
Optimizing explanations for stakeholder understanding.
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Workshop 4.5:
Best practices for transparency.
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Workshop 5.1:
Designing and building ethical data science workflows.
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Workshop 5.2:
Implementing ethical impact assessments.
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Workshop 5.3:
Utilizing ethical guidelines and checklists.
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Workshop 5.4:
Optimizing workflows for ethical compliance.
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Workshop 5.5:
Best practices for ethical workflows.
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Workshop 6.1:
Optimizing AI systems for compliance with ethical guidelines and regulations.
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Workshop 6.2:
Utilizing regulatory frameworks (GDPR, AI Act).
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Workshop 6.3:
Designing and building compliant AI solutions.
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Workshop 6.4:
Optimizing compliance for specific industries.
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Workshop 6.5:
Best practices for compliance.
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Workshop 7.1:
Debugging common ethical challenges in data science.
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Workshop 7.2:
Analyzing ethical implications of AI models.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common ethical dilemmas.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing privacy-preserving techniques in data analysis.
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Workshop 8.2:
Utilizing differential privacy and federated learning.
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Workshop 8.3:
Designing and building privacy-focused data pipelines.
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Workshop 8.4:
Optimizing privacy for data security.
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Workshop 8.5:
Best practices for privacy.
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Workshop 9.1:
Integrating ethical considerations into real-world AI projects.
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Workshop 9.2:
Utilizing case studies and ethical frameworks.
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Workshop 9.3:
Designing and building ethical AI solutions for specific domains.
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Workshop 9.4:
Optimizing integration for societal impact.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle sensitive data and ensure data governance.
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Workshop 10.2:
Utilizing data anonymization and de-identification techniques.
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Workshop 10.3:
Designing and building data governance policies.
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Workshop 10.4:
Optimizing data handling for ethical compliance.
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Workshop 10.5:
Best practices for data governance.
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Workshop 11.1:
Exploring advanced ethical frameworks and regulatory landscapes.
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Workshop 11.2:
Utilizing ethical AI principles and guidelines.
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Workshop 11.3:
Designing and building ethical AI strategies.
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Workshop 11.4:
Optimizing advanced frameworks for specific applications.
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Workshop 11.5:
Best practices for advanced frameworks.
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Workshop 12.1:
Implementing ethical AI in healthcare and finance.
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Workshop 12.2:
Utilizing ethical AI in criminal justice and social services.
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Workshop 12.3:
Implementing ethical AI in marketing and advertising.
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Workshop 12.4:
Utilizing ethical AI in autonomous systems.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing ethical AI tools and frameworks (AIF360, Fairlearn).
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Workshop 13.2:
Implementing bias detection and mitigation with tools.
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Workshop 13.3:
Designing and building ethical AI pipelines.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing ethical model evaluation and monitoring.
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Workshop 14.2:
Utilizing ethical metrics and dashboards.
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Workshop 14.3:
Designing and building monitoring systems for bias and fairness.
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Workshop 14.4:
Optimizing evaluation for ethical compliance.
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Workshop 14.5:
Best practices for evaluation.
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Workshop 15.1:
Emerging trends in data ethics and responsible AI.
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Workshop 15.2:
Utilizing AI for ethical auditing and compliance.
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Workshop 15.3:
Implementing ethical AI in emerging technologies.
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Workshop 15.4:
Best practices for future applications.
