⚖️ The Ethics of Algorithms: Bias, Power, and Invisible Decision-Makers
In this C1-level reading lesson, students will explore the ethics of algorithmic decision-making through five thematic chapters spanning ten pages. Beginning with the ubiquity of algorithms in daily life and the illusion of computational objectivity, the text moves through the mechanisms by which training data encodes and amplifies human bias, the failures and civil liberties implications of facial recognition technology, the emerging regulatory landscape including the EU AI Act, and the philosophical question of accountability when autonomous systems cause harm. Students will encounter advanced vocabulary related to computer science, law, sociology, and moral philosophy.
Lesson Plan
- Chapter I: The Algorithmic Society — ubiquity, credit scoring, hiring filters, recommendation engines, and the myth of computational neutrality
- Chapter II: Bias In, Bias Out — training data, proxy discrimination, COMPAS recidivism scores, Amazon's hiring tool, and feedback loops that entrench inequality
- Chapter III: The Face of Surveillance — facial recognition, Joy Buolamwini's Gender Shades study, racial disparities in error rates, predictive policing, and the chilling effect on protest
- Chapter IV: Governing the Machine — the EU AI Act risk tiers, algorithmic impact assessments, transparency requirements, the right to explanation, and the global regulatory patchwork
- Chapter V: Who Is Accountable? — the responsibility gap, black-box opacity, moral agency, trolley problems for AI, and designing ethics into code
Key Vocabulary
Grammar Points
- Complex participial phrases: Processing millions of data points in milliseconds, algorithmic systems now make decisions that profoundly affect individual lives.
- Inversion: Not until Buolamwini's Gender Shades study did the technology industry acknowledge the racial disparities embedded in facial recognition systems.
- Mixed conditionals: Had Amazon's engineers tested their hiring algorithm on a gender-balanced dataset, the discriminatory bias might have been detected before deployment.
- Advanced passive: The COMPAS recidivism tool has been shown to assign disproportionately high risk scores to Black defendants.
- Cleft sentences: It is not the algorithm itself but the data on which it was trained that is the primary source of discriminatory outcomes.
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