This section will provide an overview of resources to get you started.
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Biases exist in societal structure and human thought. Since AI and Generative AI use data that we give to them, biases will exist in the work they do. It is our responsibility to be aware of those, just as we should be aware of our own biases. Biases can also occur in Data Labeling, Generative AI Training, and the initial/continuing developments of AI and Generative AI.
To learn about Biases in research, look at our other guide on research.
Implicit and Explicit Biases:
Biases in AI and Generative AI
There are others not mentioned that you can find HERE.
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It is important to note that a big issue (besides Academic Integrity) that Generative AI and AI face is its harm to DEIB.
Here are some examples:
There is also a lack of diversity in the AI field. "The high tech sector employed a larger share of whites (63.5 percent to 68.5 percent), Asian Americans (5.8 percent to 14 percent) and men (52 percent to 64 percent), and a smaller share of African Americans (14.4 percent to 7.4 percent), Hispanics (13.9 percent to 8 percent), and women (48 percent to 36 percent)."- EEOC(2023)
Other points to note:
There are also potential benefits:
Related Articles & Books:
Just like DEI and AI, there are challenges and opportunities in how the tools are used and created.
Challenges:
Opportunities: