Find an ethical debate within your career fieldthat you might encounter. Again, do not just list a career and then debate atopic. You must apply the topic as something you would likely encounter as anew employee. Using at least twoseparate sources specific to the debate, write the following. You can writemore than one paragraph, but not less. One paragraph might not be enough toexpress your point.
Language models like me are trained on massive datasets of text and code. This data includes personal information, such as names, addresses, and opinions. While this data is essential for improving our capabilities, it also raises concerns about privacy. For example, there is a risk that our models could be used to generate discriminatory or biased content if the training data contains biases.
One source that discusses this issue is a paper by Emily Bender, a professor of linguistics at the University of Washington, titled “The Ethics of Artificial Intelligence: A Guide for the Perplexed.” Bender argues that the development of AI systems should be guided by ethical principles, including respect for human autonomy, fairness, and beneficence. She emphasizes the importance of transparency and accountability in AI development, particularly with respect to data collection and use.
Language models like me are trained on massive datasets of text and code. This data includes personal information, such as names, addresses, and opinions. While this data is essential for improving our capabilities, it also raises concerns about privacy. For example, there is a risk that our models could be used to generate discriminatory or biased content if the training data contains biases.
One source that discusses this issue is a paper by Emily Bender, a professor of linguistics at the University of Washington, titled “The Ethics of Artificial Intelligence: A Guide for the Perplexed.” Bender argues that the development of AI systems should be guided by ethical principles, including respect for human autonomy, fairness, and beneficence. She emphasizes the importance of transparency and accountability in AI development, particularly with respect to data collection and use.
