Equity, Diversity, Inclusion, and Indigeneity in AI systems
Artificial Intelligence learns from historical data rather than operating in a vacuum.
When data contains societal bias, prejudice, and systemic inequality, AI can replicate and amplify those patterns at scale.
For business leaders, biased AI creates ethical, operational, reputational, and regulatory risk.
According to the lecture, what can AI do when its data contains societal bias, prejudice, and systemic inequality?
Choose an answer to continue
AI learns from historical data. Think about what happens to the patterns in that data once a system runs at scale.
The lecture says AI learns from historical data rather than operating in a vacuum, so it does not correct the patterns in that data on its own.
Correct. When data contains societal bias, prejudice, and systemic inequality, AI can replicate and amplify those patterns at scale.
Week 07 — The Problem: AI as a Mirror of Society
The lecture says AI learns from historical data, which is how bias in that data reaches the model.
Correct. When data contains societal bias, prejudice, and systemic inequality, AI can replicate and amplify those patterns at scale.
Week 07 — The Problem: AI as a Mirror of Society
Correct. When data contains societal bias, prejudice, and systemic inequality, AI can replicate and amplify those patterns at scale.
Week 07 — The Problem: AI as a Mirror of Society
For business leaders, biased AI creates ethical, operational, reputational, and regulatory risk.
Correct. When data contains societal bias, prejudice, and systemic inequality, AI can replicate and amplify those patterns at scale.
Week 07 — The Problem: AI as a Mirror of Society
Bias enters AI through training data bias, algorithmic bias, and self-reinforcing feedback loops.
Training data bias can teach a model that historically dominant groups are predictors of success.
Algorithmic bias often emerges when optimization favors aggregate accuracy while ignoring minority edge cases.
Fairness can be assessed through concepts like demographic parity and equal opportunity.
According to the lecture, when does algorithmic bias often emerge?
Choose an answer to continue
Think about what an optimization target rewards when most of the examples look alike.
Correct. Algorithmic bias often emerges when optimization favors aggregate accuracy while ignoring minority edge cases.
Week 07 — Technical Concept: How Algorithms Learn Bias
Demographic parity and equal opportunity are concepts for assessing fairness, not the source of algorithmic bias.
Correct. Algorithmic bias often emerges when optimization favors aggregate accuracy while ignoring minority edge cases.
Week 07 — Technical Concept: How Algorithms Learn Bias
This reverses the lecture: algorithmic bias emerges when optimization favors aggregate accuracy and ignores minority edge cases.
Correct. Algorithmic bias often emerges when optimization favors aggregate accuracy while ignoring minority edge cases.
Week 07 — Technical Concept: How Algorithms Learn Bias
The lecture links historically dominant groups to training data bias, and it places algorithmic bias in what optimization favors: aggregate accuracy over minority edge cases.
Correct. Algorithmic bias often emerges when optimization favors aggregate accuracy while ignoring minority edge cases.
Week 07 — Technical Concept: How Algorithms Learn Bias
EDII in AI is not just a compliance checkbox, it is a competitive advantage.
The cost of bias includes regulatory fines, lawsuits, customer distrust, and long-term brand damage.
The ROI of inclusion comes from stronger products, more resilient models, and access to underserved markets.
Algorithms audited for fairness often perform more reliably in real-world conditions.
According to the lecture, where does the ROI of inclusion come from?
Choose an answer to continue
The lecture sets the cost of bias against what inclusion returns. Look for gains rather than penalties.
Those make up the cost of bias, not the ROI of inclusion.
Correct. The ROI of inclusion comes from stronger products, more resilient models, and access to underserved markets.
Week 07 — Business Impact: The ROI of Inclusive AI
The lecture says EDII in AI is not just a compliance checkbox, it is a competitive advantage.
Correct. The ROI of inclusion comes from stronger products, more resilient models, and access to underserved markets.
Week 07 — Business Impact: The ROI of Inclusive AI
Favoring aggregate accuracy while ignoring minority edge cases is how algorithmic bias often emerges, not a source of ROI.
Correct. The ROI of inclusion comes from stronger products, more resilient models, and access to underserved markets.
Week 07 — Business Impact: The ROI of Inclusive AI
Correct. The ROI of inclusion comes from stronger products, more resilient models, and access to underserved markets.
Week 07 — Business Impact: The ROI of Inclusive AI
Indigeneity is a critical dimension of EDII, especially when AI systems rely on cultural or community data.
Data colonialism occurs when data is extracted from Indigenous communities without consent or reciprocal benefit.
OCAP principles, Ownership, Control, Access, and Possession, establish that First Nations must control how their data is collected and used.
Business leaders must recognize that data represents people, cultures, and sovereign rights, not just raw fuel for models.
According to the lecture, what do the OCAP principles establish?
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OCAP stands for Ownership, Control, Access, and Possession. Think about who those four words give authority to.
Extracting data from Indigenous communities without consent or reciprocal benefit is what the lecture calls data colonialism.
Correct. OCAP principles establish that First Nations must control how their data is collected and used.
Week 07 — Indigeneity and Data Sovereignty
Correct. OCAP principles establish that First Nations must control how their data is collected and used.
Week 07 — Indigeneity and Data Sovereignty
The lecture says data represents people, cultures, and sovereign rights, not just raw fuel for models.
Correct. OCAP principles establish that First Nations must control how their data is collected and used.
Week 07 — Indigeneity and Data Sovereignty
OCAP places Ownership, Control, Access, and Possession with First Nations, not with AI vendors.
Correct. OCAP principles establish that First Nations must control how their data is collected and used.
Week 07 — Indigeneity and Data Sovereignty
Mandate diverse teams so edge cases are more likely to be identified before release.
Implement independent AI audits and treat algorithmic review as seriously as financial auditing.
Keep humans in the loop for high-stakes decisions in hiring, lending, healthcare, and public services.
Establish transparent governance with clear accountability, escalation paths, and ethical review standards.
According to the lecture, where should business leaders keep humans in the loop?
Choose an answer to continue
Think about which decisions carry the most consequence for the people they affect.
This reverses the lecture: humans stay in the loop for high-stakes decisions.
Correct. Business leaders should keep humans in the loop for high-stakes decisions in hiring, lending, healthcare, and public services.
Week 07 — Strategy: Mitigation for Business Leaders
The lecture lists independent AI audits and keeping humans in the loop as separate measures; humans stay in the loop for high-stakes decisions.
Correct. Business leaders should keep humans in the loop for high-stakes decisions in hiring, lending, healthcare, and public services.
Week 07 — Strategy: Mitigation for Business Leaders
Correct. Business leaders should keep humans in the loop for high-stakes decisions in hiring, lending, healthcare, and public services.
Week 07 — Strategy: Mitigation for Business Leaders
The lecture names hiring as one of the high-stakes decisions where humans stay in the loop.
Correct. Business leaders should keep humans in the loop for high-stakes decisions in hiring, lending, healthcare, and public services.
Week 07 — Strategy: Mitigation for Business Leaders
EDII in AI