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AI Alignment and Ethics

2 ALIGNING THE ALIEN

Artificial Ethics for Alien Minds

The Failure Modes of Predictive AI

Chapter 2 HOW PREDICTIVE AI GOES WRONG

Predictive AI Makes Life-Altering Decisions

A Good Prediction Is Not a Good Decision

Opaque AI Incentivizes Gaming

Overautomation

Predictions about the Wrong People

Predictive AI Exacerbates Existing Inequalities

A World without Prediction

Concluding Thoughts

TABLE 2.1. Five reasons predictive AI fails

🎮 Pixel Quest: Failure Modes Review

Limits of Prediction

Chapter 3: WHY CAN’T AI PREDICT THE FUTURE?

A Brief History of Predicting the Future Using Computers

Getting Specific

The Fragile Families Challenge

Why Did the Fragile Families Challenge End in Disappointment?

Predictions in Criminal Justice

Failure Is Hard. What about Success?

The Meme Lottery

From Individuals to Aggregates

Recap: Reasons for Limits to Prediction

🎮 Crossword: Prediction Terminology

Knowledge Check

Quiz: Aligning the Alien

Quiz: Artificial Ethics

Quiz: Predictive AI Failures

Quiz: Life-Altering Decisions

Quiz: Prediction vs Decision

Quiz: Gaming the System

Quiz: Overautomation

Quiz: Demographic Bias

Quiz: Inequalities

Quiz: Five Failure Reasons

Quiz: Beyond Prediction

Quiz: Prediction Limits

Quiz: History of Prediction

Quiz: Specific Examples

Quiz: Fragile Families

Course Feedback

Survey: Alignment Concepts

Survey: Failure Modes Relevance

Survey: Decision Making Impact

Survey: Gaming Examples

Survey: Overautomation Experiences

Survey: Bias and Fairness

Survey: Inequality Discussion

Survey: Alternative Frameworks

Survey: Historical Context

Survey: Fragile Families Case

Survey: Criminal Justice Content

Survey: Aggregate vs Individual

Survey: Game Experience

Survey: Overall Course Pacing

Survey: Final Thoughts

Mastering AI Trust: Navigating Reliability, Alignment, and Failure Modes is a practical, research-grounded course designed to help product leaders, data professionals, policy makers, and curious practitioners build trustworthy AI systems—and know when not to trust them. You’ll explore why advanced models behave like alien minds, how to align them with human intent and ethics, and where predictive AI breaks down in real-world, high-stakes contexts. Through clear frameworks, case studies, and interactive knowledge checks, you’ll learn to distinguish accurate predictions from good decisions, recognize confident errors, and diagnose five common failure modes. Drawing on leading scholarship in AI alignment and the latest critiques of predictive systems, the course moves from principles to practice: auditing data quality, spotting overautomation and gaming incentives, and setting realistic expectations for prediction at the individual vs aggregate level. By the end, you’ll be equipped with a systematic approach to evaluating AI tools, enforcing human-in-the-loop safeguards, and communicating AI limits to your stakeholders. Whether you’re deploying models in production or reviewing AI-driven policies, this course gives you the critical lens and vocabulary to reduce risk, improve outcomes, and earn user trust.

Mastering AI Trust: Navigating Reliability, Alignment, and Failure Modes

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            <p>The rapid integration of artificial intelligence into professional and academic spheres necessitates a profound understanding of AI alignment—the critical bridge between human intent and machine execution. As these "alien minds" process objectives through logic fundamentally different from human cognition, the risk of misalignment in high-stakes environments becomes a primary concern for leaders, educators, and practitioners. This course examines the intricate challenge of encoding human ethical values into computational systems, distinguishing between mere technical reliability and true ethical alignment. By analyzing how AI adheres to literal instructions while potentially disregarding broader context, participants will gain a sophisticated framework for evaluating the trust they place in automated systems.</p>

<p>Beyond theoretical alignment, the curriculum delves into the practical failure modes of predictive AI, exploring why mathematically accurate models often result in poor real-world decisions. From the phenomenon of "confident errors" to the systemic exacerbation of existing inequalities, the modules provide a rigorous critique of overautomation and the strategic "gaming" of opaque algorithms. Business professionals and creative practitioners will explore the inherent limits of social prediction, contrasting the relative success of aggregate forecasting with the frequent failure of individual-level predictions in fields such as criminal justice and social services. Through historical context and contemporary case studies, including the Fragile Families Challenge, this course equips individuals with the skepticism and diagnostic tools required to navigate an increasingly automated world with precision and foresight.</p>
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