CPMAI+E Ethical & Responsible AI Framework Development Certification


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AI systems have the potential to provide great value, but also the potential to cause great harm. Knowing how to build or use AI systems is simply not going to be enough. You need to know how to build, use, and interact with these systems ethically and responsibly.

Cognilytica’s comprehensive, internationally recognized approach to ethical and responsible AI covers all you need to know including gaining a comprehensive understanding of boundaries for what is acceptable and not acceptable use of AI technology. You will also gain an understanding of methods, practices and processes by which AI systems can be implemented to fulfill governance, documentation, measurement, and mitigation. You will learn the necessary aspects to turn abstract ethical AI concepts into practical implementation actions and gain knowledge of necessary elements of ethical and responsible AI systems.

Certification is provided for all individuals who successfully complete all modules and exercises for this course.

Course Content

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What Will I Learn?

Module 1: Introduction to Ethical and Responsible AI

  • Why are ethical and responsible AI systems necessary?
  • Ethical and responsible AI: it’s not just about “feel good” 
  • Addressing fears about AI
  • Addressing concerns about AI
  • Addressing issues of bias and human induced error
  • The “Uncanny Valley”… of data
  • Understanding the Limits of Current Cognitive Technology
  • AI reality vs AI science fiction
  • The necessity of keeping the human in the loop
  • The data-centricity of AI
  • Risks associated with the use of AI
  • AI Introduces new Threat Vectors
  • The cost of non-compliance and the risk of being an ethical pariah
  • Awareness of the value of data, and the need for data privacy and appropriate sharing
  • Overview of Cognilytica Comprehensive Ethical & Responsible AI Framework 

Module 2: Societal Ethical AI Principles

  • Making sure systems comply with basic human values
  • AI systems meant for human benefit
  • AI systems under human control
  • Data and AI systems that maintain human dignity
  • Maintaining human freedom of choice and agency in self-determination
  • Data and AI system fairness
  • The great potential for AI system bias
  • AI system representation: issues of diversity and inclusion in data
  • Making sure AI systems don’t run amok of the environment
  • Case studies of Societal AI ethics gone wrong
  • Examples of applications of Societal Ethical AI 
  • Applying the Cognilytica Ethical & Responsible AI Framework for Societal Ethical AI Principles

Module 3: Responsible AI Practices

  • Responsible vs. Ethical: what’s the difference?
  • AI systems implemented to maintain a positive purpose
  • Misuse and Abuse of AI Systems
  • Compliance with rapidly evolving laws and regulations around AI and data
  • Maintaining AI system safety 
  • Maintaining AI system security
  • Maintaining AI system trust
  • Putting into place a chain of command: human accountability
  • Data and AI privacy policies, rules, laws, and regulations
  • Dealing with the potential for AI-caused workforce disruption
  • Case studies of Responsible AI gone wrong
  • Examples of applications of Responsible AI
  • Applying the Cognilytica Ethical & Responsible AI Framework for Responsible AI Principles

Module 4: AI System Transparency

  • Understanding the full scope of the data and decisions that go into AI systems
  • AI System transparency vs. algorithmic transparency 
  • Open systems: technology
  • Open systems: data
  • Bias Measurement & Mitigation
  • AI and data disclosures
  • AI system consent
  • Case studies of AI system transparency gone wrong
  • Examples of applications of AI system transparency
  • Implementing AI System Transparency: Cognilytica Transparency Assessment Approach
  • Implementing AI System Transparency: Google Model Cards
  • Applying the Cognilytica Ethical & Responsible AI Framework for Systemic AI Transparency Principles

Module 5: AI System Governance

  • Governance: it’s about people, processes, and technology
  • AI system risk assessment & mitigation
  • AI System Auditability
  • Contestability of algorithmic decision-making and AI system use
  • AI System Controls: development, training, implementation, and inference
  • AI System Monitoring
  • AI System Quality Verification
  • Third-party supervision, certification, and regulation
  • The importance of an educated and adequate trained workforce 
  • Case studies of AI system governance gone wrong
  • Examples of applications of AI system governance
  • Applying the Cognilytica Ethical & Responsible AI Framework for  AI Governance Principles

Module 6: Algorithmic Interpretability & Explainability

  • The AI “black box”
  • Algorithmic Explainability
  • Algorithmic Interpretability
  • A lower bar to meet: Understandability / Root Cause Explanations
  • The challenges of “Algorithmic Discrimination”
  • The varying levels of opacity of different AI algorithms
  • The need for algorithmic explainability for different AI use cases and patterns
  • Technology solutions for algorithmic explainability
  • Other approaches to algorithmic interpretability and explainability
  • Case studies of AI algorithmic explainability gone wrong
  • Examples of applications of AI algorithmic explainability
  • Applying the Cognilytica Ethical & Responsible AI Framework for Algorithmic Transparency Principles

Module 7: Worldwide Data and AI laws & regulations

  • The evolving landscape of AI and data laws and regulations
  • Laws and regulations for emerging tech
  • Introduction to current global laws and regulations pertaining to data and AI
  • Current state of worldwide data and AI laws and regulations
  • Facial Recognition and Computer Vision-Relevant Laws and Regulations
  • AI-Relevant Data Privacy Laws and Regulations
  • Autonomous Vehicle-Relevant Laws and Regulations
  • Laws and Regulations Pertaining to AI Ethics, Bias, and Fairness
  • Lethal Autonomous Weapons Systems (LAWS) and AI-enhanced Weapons
  • Chatbot and Conversational Systems Laws and Regulations
  • Laws and Regulations Regarding Use of AI for Predictions or Decision Support
  • Laws and Regulations Regarding Malicious AI and AI-Enabled Forgery and Fakery
  • Other General AI Regulations
  • Specific examples of AI and data laws and regulations in use and enforcement
  • Planning for continued evolution of AI and data laws in the context of the Cognilytica Ethical & Responsible AI Framework

Module 8: Putting Ethical & Responsible AI into Practice

  • Ethical and Responsible AI: it’s something you DO, not just a statement. 
  • Organizational change management for ethical and responsible AI practices
  • The AI governance team
  • Organizational compliance teams
  • Ethical AI teams, boards, and centers of excellence (CoE)
  • Case studies in ethical & responsible AI teams gone wrong
  • Putting ethical and responsible AI practices into place in data science, data engineering, and ML development teams
  • Ethical and Responsible AI components of Best Practices Methodologies (CPMAI)
  • Technology solutions for implementing ethical and responsible AI practices
  • ML Ops facilitating practical ethical & responsible AI implementation
  • Applying ethical & responsible AI at scale
  • Roadblocks to ethical & responsible AI implementation
  • KPIs and success metrics for ethical & responsible AI
  • Responding to AI and data crises
  • Mitigating ethical and responsible AI and data challenges
  • Go forth and Do: Moving Forward with Ethical & Responsible AI

Who is this Training For?​

  • C-Level Executives
  • Business executives
  • Directors
  • Vice Presidents
  • Technical Managers 
  • Non Technical Managers
  • Project Managers
  • Product Managers
  •  Information Security Managers
  • Data Scientist
  • AI / Machine Learning (ML) Engineer

Learning Paths

Supported Learning Paths

  • Cognilytica: Ethical & Responsible AI Certification
  • Edison DSF: Data Science Managers (DSP01 - DSP03)
  • Edison DSF: Data Science Professionals (DSP04 -DSP09)
  • Edison DSF: Data Engineering & Management Professionals (DSP10-DSP16)
  • Edison DSF: Operations & Technical Support (DSP17-DSP20)
  • Edison DSF: Data & User Support (DSP20-DSP22)

Learning Levels

Learning Levels

  • Edison DSF: Level 2-3
  • Category: Ethical & Responsible AI

Course Schedule

Course Schedule & Delivery

  • Course delivered online with a live, virtual instructor
  • Course is sixteen (16) hours of live instruction plus exercises to be completed by trainees on their schedule between online classes.
  • Course is delivered over four (4) consecutive weeks, two (2) days per week,  two (2) hours per day.
  • Next course is scheduled for Q1 2022: please inquire about availability
  • Private instructor courses offered for groups of 20 or more: please inquire about availability
  • Access to recorded videos is provided for thirty (30) days after conclusion of class
  • Additional terms and conditions apply


Course Pricing

Pricing, Group Discounts, Enrollment Details

    • $1,995 USD per trainee
    • Certification & materials included in price
    • Group enrollment discounts:
      • 10% discount for 5 or more trainees
      • 15% discount for 10 or more trainees
      • 20% discount for 15 or more trainees
  • Group enrollment discount applied at time of registration of all trainees in group


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