The article also discusses the risks in data annotation and the importance of output validation. It highlights the potential for human bias in data annotation and the ethical implications of the content that human annotators may have to deal with. In terms of output validation, the article stresses the need for ongoing attention to AI outputs to correct for issues such as model drift and brittleness, especially with the emergence of generative AI. The author concludes by advocating for the recognition and prioritization of the human element throughout the AI lifecycle to build trustworthy AI programs.
Key takeaways:
- Humans play a crucial role in the creation and operation of AI models, and their importance should not be underestimated or overlooked.
- There are ethical considerations and potential risks involved in data annotation, such as the possibility of human annotators injecting their personal biases into the training set.
- Output validation is critical in AI development, especially with the emergence of generative AI, to mitigate risks and ensure governance.
- Deloitte's "Age of With" concept emphasizes the importance of humans working with machines to achieve outcomes neither could do independently, and prioritizing the human element can help build trustworthy AI programs.