Wrap Up
Key Takeaways
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The issue of trust in technology companies is complex and varies across different demographic groups, with factors like race, gender, and educational level influencing how much personal data individuals are willing to share.
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Traditional methods for data regulation, such as notice and consent or anonymization, are becoming increasingly inadequate due to the complexities and secondary uses of big data, making it difficult to genuinely protect user privacy.
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The field of data science is grappling with ethical concerns, particularly around biases that can affect marginalized communities; these biases are often unintentionally built into algorithms due to a lack of diversity among those who create and test technology.
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The regulation of big data faces significant challenges, including jurisdictional issues and the fundamental question of who gets to define what constitutes harmful or beneficial use of data, making it a complex issue of power and control.
Exercises
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In what ways do you personally trust or distrust technology companies with your data? Do you think your race, gender, or educational level influences your level of trust? Discuss your reasons.
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Choose one method of data regulation discussed in the material (e.g., notice and consent, anonymization, deletion) and argue its pros and cons. Can you suggest any modifications to make it more effective in the age of big data?
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Listen to one of the podcasts mentioned in the material and summarize its key points. How does the podcast deepen your understanding of the ethical challenges posed by big data, and what solutions does it offer?
