AI Ethics Flashcard

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What are some potential risks to data privacy in AI?

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Unauthorized access, data breaches, and misuse of personal data

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How can AI systems be designed to protect user data privacy?

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Implementing strong encryption, anonymization techniques, and strict access controls

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What are some ethical considerations related to the collection and use of data in AI?

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Privacy, consent, transparency, bias, and accountability

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What are some potential consequences of data breaches in AI systems?

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Potential consequences include compromised privacy, security risks, loss of trust, and misuse of sensitive information.

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How can individuals protect their data privacy in the context of AI? Options: Be cautious about sharing personal information, use strong and unique passwords, enable two-factor authentication, and regularly update privacy settings.

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Be cautious about sharing personal information, use strong and unique passwords, enable two-factor authentication, and regularly update privacy settings.

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What is algorithmic bias in AI?

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Unfair and discriminatory outcomes produced by AI systems due to biased data or flawed algorithms

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How can bias in AI algorithms impact decision-making processes?

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Bias in AI algorithms can lead to unfair or discriminatory decisions.

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What are some strategies to mitigate bias in AI algorithms?

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Diverse and inclusive training data, regular bias testing and monitoring, transparency in algorithm design, and involving diverse perspectives in the development process.

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What are some examples of bias in AI algorithms in real-world applications?

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Biased training data, lack of diversity in the development team, biased metrics

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How can organizations ensure fairness and transparency in their AI algorithms?

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Regularly auditing for bias, using diverse and representative data sets, and involving diverse stakeholders in the development process