This module explores ethical considerations inherent in data-driven decision-making and the deployment of algorithmic systems. Emphasizes the socially constructed nature of science, shedding light on biases and inequalities. Learners explore the societal and cultural implications of AI and machine learning technologies, analyzing potential biases and disparities in data and algorithms. Practical skills are developed through the application of fairness metrics to evaluate and mitigate bias in algorithmic decision-making. Privacy, ethical considerations surrounding personal data, and navigating key regulatory frameworks related to AI and ML, such as GDPR, California Consumer Privacy Act, and other data privacy laws, are integral components.

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