<ul><li>Algorithms, intended to be fair and objective, can perpetuate and magnify existing biases, impacting critical sectors like employment and healthcare.</li><li>Addressing algorithmic bias and privacy requires a collaborative, multidisciplinary approach.</li><li>Real-world examples of algorithmic bias include biased job application screening and racial profiling by law enforcement.</li><li>Privacy professionals must balance the need for effective data use with ethical considerations to mitigate the risks of algorithmic bias.</li></ul>

Unpacking Algorithmic Bias and Privacy: The Hidden Challenges of AI in a Data-Driven World

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