Course: Detect and Mitigate Ethical Risks
Modules:
Detect and Mitigate Ethical Risks Course Introduction, CEET Specialization Introduction, Course Welcome & Success Tips, The Importance of Managing Risks, Risk Management Process, Risk Identification, Risk Analysis, Risk Mitigation, Types of Ethical Risk, Distributions, Central Tendency, Variance and Standard Deviation, Skewness and Kurtosis, Correlation, Probability, Machine Learning Outcomes, Cost Functions, Reliability, Goodhart's Law
The Importance of Managing Privacy Risks, Private Data, First-Party vs. Third-Party Data, Secondary Use of Data, Combined Data Sources, Identify Personally Identifiable Information (PII), Model Personas, Track Customer Data, Meet Compliance Requirements, Intent and Consent, Minimize Private Data Sharing, Give the User Choices, Minimize Private Data Collection, Reinforce Trust, Anonymization and Pseudonymization, Homomorphic Encryption, Zero-Knowledge Protocols, Incorporate Privacy Risk Management in the Lifecycle
The Importance of Managing Accountability Risks, Use of Third-Party Components, Automation Bias, Extrajudicial Judgment, Lack of Guiding Principles, Recognize Black Box Algorithms, Assess the Organization's Governance Structure, Document and Distribute Company Policies, Document Design Processes, Document Auditing Processes, Responsibility Assignment Matrix (RAM/RACI), Pilot Testing, Collaboration with Data Sharing Partners, Algorithmic Impact Assessment (AIA), Data Visualization, Dashboard Reporting, Incorporate Accountability Risk Management in the Lifecycle
The Importance of Managing Transparency and Explainability Risks, Black Box Systems, Self-Learning Models, Third-Party Integration, Intellectual Property Rights, Shadow Banning, Explainable AI, Identify Algorithmic Decisions, Deconstruct Specific Decisions, Explain How Systems Work, Help Users Seeking Explanations, Keep Humans in the Loop, Ensure Proper Data Disclosure, Be Upfront About Training Data Inadequacies, SHAP and Alibi, ELI5, LIME, and What-If
The Importance of Managing Fairness and Non-Discrimination Risks, Implicit Bias, Sampling Bias, Reinforcement Bias, Temporal Bias, Overfitting to Training Data, Edge Cases and Outliers, Analytical Techniques, Analyze Models in Different Environments, Persona Modeling, Inclusive Design and Foreseeability, STEEPV Analysis, Perform User Testing, Gather Input from External Stakeholders, Bias and Safety Bounties, AI Fairness 360, Radioactive Data Tracing, Incorporate Fairness and Non-Discrimination Risk Management in the Lifecycle
The Importance of Managing Safety and Security Risks, Abnormal System Behavior, Adversarial Machine Learning, Bad Actors, Groupthink and Biases, Cyber Attacks, Quantitative Risk Analysis, Evaluate Training Data and Models, Threat Intelligence, Threat Modeling, Penetration Testing, Forensic Analysis, Ensure Critical AI Systems Follow Rigorous Standards, Establish Baseline System Behavior, Designate Rapid Response Teams, Protect the Security of Data in Storage, Protect the Security of Data in Transit, Threat and Risk Libraries, Threat Modeling and Analysis Tools, Attack Simulation Tools, Vulnerability Scoring Tools, Security Information and Event Management (SIEM), Incorporate Safety and Security Risk Management in the Lifecycle
Algorithmic Impact Assessment (AIA), Ethical AI Project Plan