This unit teaches learners how to design, evaluate, and implement data-informed DEI and employee well‑being initiatives that align with business goals. Students examine program evaluation fundamentals, metric selection and proactive data collection (including ERG considerations), and assess how AI tools can enable or threaten equitable outcomes through bias and deepfakes.
Learning Objectives
- Analyze the difference between correlation and causation and apply that understanding to interpret DEI and well-being data accurately
- Apply program evaluation frameworks to develop measurable DEI and employee well-being objectives, select key performance indicators, and integrate Employee Resource Groups (ERGs) into measurement plans
- Evaluate data collection strategies and analytic tools (including AI-enabled methods) for validity, bias, ethical risks, and vulnerability to deepfakes, and recommend mitigation steps
- Demonstrate the ability to construct a data-informed business case for DEI and well-being initiatives using case study evidence, clear metrics, and projected business impacts
Curriculum
- 1 Section
- 13 Lessons
- 30 hours
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- Unit 3 — Data-Driven DEI and Well-Being13
- 1.1A Brief Note on Correlation Versus Causation20 mins
- 1.2Notes20 mins
- 1.3Program Evaluation Fundamentals20 mins
- 1.4A Discussion on Employee Resource Groups20 mins
- 1.5Running DEI and Well-being as a Business with Clear Metrics20 mins
- 1.6Focusing on Business Questions and Proactive Data Collection20 mins
- 1.7Recognizing the Strong Connection Between DEI and Well-being20 mins
- 1.8Case Studies: Data-informed DEI and Well-being Programs20 mins
- 1.9Notes20 mins
- 1.10The Double-edge Sword: AI’s Potential and Risks to Advancing DEI20 mins
- 1.11Biases in AI Development and Deployment20 mins
- 1.12The Danger of Deepfakes20 mins
- 1.13The Role of AI in Employee Well-being20 mins
Instructor
Free
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