Wrap Up
Key Takeaways
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Algorithms are not neutral; they can encode biases present in their training data or in the values of their designers, affecting decisions in areas like criminal justice, healthcare, and employment.
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The lack of standardized definitions for algorithmic fairness and the absence of regulatory oversight make it difficult to hold companies and organizations accountable for biased or harmful algorithms.
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Transparency in algorithmic decision-making is complicated by factors such as trade secrets, complexity, and the potential for system manipulation.
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Journalists, in collaboration with academics and whistleblowers, have become important watchdogs in auditing algorithms, but there is a need for formal governance structures.
Exercises
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Discuss a real-world example of algorithmic bias and explore how it could be mitigated. What challenges would you anticipate in implementing these changes?
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Conduct a mock audit of a hypothetical algorithm used for job recruitment. What criteria would you use to assess its fairness and accountability?
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Debate the pros and cons of algorithmic decision-making in a specific sector (e.g., healthcare, criminal justice, or advertising). How do you weigh the benefits of efficiency and scale against the risks of bias and lack of accountability.
