Instructions: Choose and review at least 2 resources from the options below. You can pick any combination that interests you - videos, articles, or podcasts.
The goal is to understand: How do ethics and AI intersect in business intelligence work?
VIDEO OPTIONS
Video 1: Ethics & AI: Equal Access and Algorithmic Bias
Video 2: The Ethics of AI Bias
Video 2: Breaking into AI Ethics Consulting | Lunchtime BABling 10
ARTICLE OPTIONS
Article 1: Ethics of AI Bias Article 2: Ethics Is the Edge: The Future of AI in Higher Education
Article 3: 10 Ethical Considerations Shaping the Future of AI in Business
PODCAST OPTIONS
Podcast 1: AI Hype and Skepticism - Economist Paul Romer
Podcast 2: AI and Safety: How Responsible Tech Leaders Build Trustworthy Systems National Safety Month Special
Podcast 3: Will Society Evolve Fast Enough? AI Ethics, Quantum Futures, and Rethinking Humanity Episode 348 with Dr. Mark van Rijmenam
PART 2: REFLECTION QUESTIONS
Instructions: Answer the questions below based on what you learned from your chosen resources AND your own thinking about your final project.
Submission requirements:Thoughtful answers (3-5 sentences each minimum)
Connect concepts to your final project where relevant
Be specific - use examples
1: Key Concepts
From the resources you reviewed, identify and explain TWO ethical issues related to AI or data analytics.Examples might include: bias, privacy, transparency, fairness, accountability, consent, etc.Issue 1:Issue 2:2: Real-World Application
Describe ONE real-world example of an ethical problem in AI or data analytics that you learned about from the resources (or that you already knew about).Include:- What happened?
- What was the ethical issue?
- What was the impact or consequence?
3: Your Final Project
Identify at least ONE ethical consideration relevant to your final project.Think about:- Does your dataset fairly represent all groups?
- Could your analysis harm or disadvantage anyone?
- Are there biases in how the data was collected?
- Could your visualizations mislead viewers?
- Are you being transparent about limitations?
4: Professional Responsibility
As a future data professional, when should you raise ethical concerns about a project you're working on?Think about: What would make you say "this doesn't feel right" or "we shouldn't do this"?5: Preventing Bias
What is ONE practical step data analysts can take to reduce bias in their work?6: Personal Reflection
What surprised you most or what did you learn that you hadn't thought about before?