Detect and Mitigate Ethical Risks

This course is part of CertNexus Certified Ethical Emerging Technologist Professional Certificate

Instructors: Renée Cummings

What you'll learn

Summarize common sources of ethical risks.
Detect and mitigate ethical risks.
Evaluate risk identification and mitigation strategies within the lifecycle.
Analyze a sample AI model and create a plan to mitigate any identified risks.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

Build your Machine Learning expertise

This course is part of the CertNexus Certified Ethical Emerging Technologist Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from CertNexus

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 7 modules in this course

Data-driven technologies like AI, when designed with ethics in mind, benefit both the business and society at large. But it’s not enough to say you will “be ethical” and expect it to happen. We need tools and techniques to help us assess gaps in our ethical behaviors and to identify and stop threats to our ethical goals. We also need to know where and how to improve our ethical processes across development lifecycles. What we need is a way to manage ethical risk. This third course in the Certified Ethical Emerging Technologist (CEET) professional certificate is designed for learners seeking to detect and mitigate ethical risks in the design, development, and deployment of data-driven technologies. Students will learn the fundamentals of ethical risk analysis, sources of risk, and how to manage different types of risk. Throughout the course, learners will learn strategies for identifying and mitigating risks.

The first module in the course lays the groundwork for some concepts that are fundamental to data-driven technologies like artificial intelligence (AI). As an ethicist, you may not be putting these concepts into practice yourself, but you still need to understand them. That way, you'll be able to make more informed judgments and communicate with other people about how best to detect and mitigate ethical risks.

What's included

19 videos4 readings1 assignment1 discussion prompt

19 videosTotal 79 minutes

Detect and Mitigate Ethical Risks Course Introduction3 minutesPreview module
CEET Specialization Introduction7 minutes
Course Welcome & Success Tips3 minutes
The Importance of Managing Risks3 minutes
Risk Management Process3 minutes
Risk Identification3 minutes
Risk Analysis4 minutes
Risk Mitigation4 minutes
Types of Ethical Risk2 minutes
Distributions2 minutes
Central Tendency3 minutes
Variance and Standard Deviation4 minutes
Skewness and Kurtosis3 minutes
Correlation3 minutes
Probability3 minutes
Machine Learning Outcomes8 minutes
Cost Functions5 minutes
Reliability4 minutes
Goodhart's Law3 minutes

4 readingsTotal 42 minutes

Overview2 minutes
Get help and meet other learners. Join your Community! 5 minutes
Risk Management Frameworks20 minutes
Classification Metrics15 minutes

1 assignmentTotal 30 minutes

Analyzing Ethical Risks30 minutes

1 discussion promptTotal 3 minutes

Reflect on What You've Learned3 minutes

This module begins a series of modules in which you'll manage the many different types of ethical risks involved in data-driven technologies. First, you'll learn more about the risks to users' privacy and private data.

What's included

18 videos3 readings1 assignment1 discussion prompt

18 videosTotal 83 minutes

The Importance of Managing Privacy Risks2 minutesPreview module
Private Data5 minutes
First-Party vs. Third-Party Data5 minutes
Secondary Use of Data3 minutes
Combined Data Sources4 minutes
Identify Personally Identifiable Information (PII)3 minutes
Model Personas4 minutes
Track Customer Data6 minutes
Meet Compliance Requirements2 minutes
Intent and Consent5 minutes
Minimize Private Data Sharing4 minutes
Give the User Choices5 minutes
Minimize Private Data Collection2 minutes
Reinforce Trust7 minutes
Anonymization and Pseudonymization3 minutes
Homomorphic Encryption6 minutes
Zero-Knowledge Protocols6 minutes
Incorporate Privacy Risk Management in the Lifecycle3 minutes

3 readingsTotal 19 minutes

Overview2 minutes
Data Protection Policies2 minutes
Privacy Legislation Sources15 minutes

1 assignmentTotal 30 minutes

Managing Privacy Risks30 minutes

1 discussion promptTotal 10 minutes

Reflect on What You've Learned10 minutes

This module continues the series of modules in which you'll manage the many different types of ethical risks involved in data-driven technologies. Now, you'll tackle the risks to the organization's accountability.

What's included

17 videos1 reading1 assignment1 peer review1 discussion prompt

17 videosTotal 74 minutes

The Importance of Managing Accountability Risks2 minutesPreview module
Use of Third-Party Components5 minutes
Automation Bias6 minutes
Extrajudicial Judgment1 minute
Lack of Guiding Principles4 minutes
Recognize Black Box Algorithms5 minutes
Assess the Organization's Governance Structure4 minutes
Document and Distribute Company Policies4 minutes
Document Design Processes4 minutes
Document Auditing Processes3 minutes
Responsibility Assignment Matrix (RAM/RACI)5 minutes
Pilot Testing2 minutes
Collaboration with Data Sharing Partners3 minutes
Algorithmic Impact Assessment (AIA)8 minutes
Data Visualization4 minutes
Dashboard Reporting3 minutes
Incorporate Accountability Risk Management in the Lifecycle4 minutes

1 readingTotal 2 minutes

Overview2 minutes

1 assignmentTotal 30 minutes

Managing Accountability Risks Quiz30 minutes

1 peer reviewTotal 20 minutes

Create a RACI Matrix20 minutes

1 discussion promptTotal 10 minutes

Reflect on What You've Learned10 minutes

This is the next module in the ongoing series of modules in which you'll manage the many different types of ethical risks involved in data-driven technologies. Next up are the related concepts of transparency and explainability.

What's included

16 videos2 readings1 assignment1 discussion prompt1 ungraded lab

16 videosTotal 69 minutes

The Importance of Managing Transparency and Explainability Risks3 minutesPreview module
Black Box Systems3 minutes
Self-Learning Models3 minutes
Third-Party Integration2 minutes
Intellectual Property Rights3 minutes
Shadow Banning3 minutes
Explainable AI1 minute
Identify Algorithmic Decisions3 minutes
Deconstruct Specific Decisions9 minutes
Explain How Systems Work6 minutes
Help Users Seeking Explanations3 minutes
Keep Humans in the Loop3 minutes
Ensure Proper Data Disclosure5 minutes
Be Upfront About Training Data Inadequacies3 minutes
SHAP and Alibi7 minutes
ELI5, LIME, and What-If5 minutes

2 readingsTotal 7 minutes

Overview2 minutes
Incorporate Transparency and Explainability Risk Management in the Lifecycle5 minutes

1 assignmentTotal 30 minutes

Managing Transparency and Explainability Risks Quiz30 minutes

1 discussion promptTotal 10 minutes

Reflect on What You've Learned10 minutes

1 ungraded labTotal 30 minutes

Explaining a Decision Tree Using ELI530 minutes

This is the penultimate module in the ongoing series of modules in which you'll manage the many different types of ethical risks involved in data-driven technologies. Here, you'll focus on managing risks to fairness and non-discrimination (bias).

What's included

18 videos3 readings1 assignment1 discussion prompt

18 videosTotal 58 minutes

The Importance of Managing Fairness and Non-Discrimination Risks2 minutesPreview module
Implicit Bias2 minutes
Sampling Bias1 minute
Reinforcement Bias1 minute
Temporal Bias1 minute
Overfitting to Training Data1 minute
Edge Cases and Outliers2 minutes
Analytical Techniques4 minutes
Analyze Models in Different Environments2 minutes
Persona Modeling2 minutes
Inclusive Design and Foreseeability5 minutes
STEEPV Analysis4 minutes
Perform User Testing2 minutes
Gather Input from External Stakeholders2 minutes
Bias and Safety Bounties3 minutes
AI Fairness 3606 minutes
Radioactive Data Tracing4 minutes
Incorporate Fairness and Non-Discrimination Risk Management in the Lifecycle3 minutes

3 readingsTotal 32 minutes

Overview2 minutes
Pattern Matching vs. Bias10 minutes
AI Fairness 360 Demo20 minutes

1 assignmentTotal 30 minutes

Managing Fairness and Non-Discrimination Risks30 minutes

1 discussion promptTotal 10 minutes

Reflect on What You've Learned10 minutes

This is the final module in the series of modules in which you'll manage the many different types of ethical risks involved in data-driven technologies. Lastly, you'll address risks to both safety and security.