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