Technology reflects its creators in both conscious and unconscious ways. That's why we see so much inherent bias in AI.
that the biggest risk tied to artificial intelligence is the militaristic downfall of humanity. But there’s a smaller community of people committed to addressing two more tangible risks: AI created with harmful biases built into its core, and AI that does not reflect the diversity of the users it serves. I am proud to be part of the second group of concerned practitioners. And I would argue that not addressing the issues of bias and diversity could lead to a different kind of weaponized AI.
Diversifying the AI talent pool isn’t just about gender. Currently, AI development is a PhD’s game. The community of credentialed people creating scalable AI for businesses is relatively small. While the focus on quality and utility needs to remain intact, expanding the diversity of people working on AI to include people with nontechnical professional backgrounds and less advanced degrees is vital to AI’s sustainability.
Currently, though, there is a lack of testing AI products throughout their development cycle to detect potential harms they may do to humans socially, ethically, or emotionally once they hit the market. One way to remedy this is by adding bias testing to a new product’s development cycle. Adding such a test in the R&D phase would help companies remove harmful biases from algorithms that run their AI applications and datasets that they pull from to interact with people.
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