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There’s a shockingly simple answer to the AI bias conundrum: More diversity
How can we proactively mitigate AI bias and create less harmful models if the data we train them on is inherently biased?
As we approach the two-year anniversary of ChatGPT and the subsequent “Cambrian explosion” of generative AI applications and tools, it has become apparent that two things can be true at once: The potential for this technology to positively reshape our lives is undeniable, as are the risks of pervasive bias that permeate these models. In less than two years, AI has gone from supporting everyday tasks like hailing rideshares and suggesting online purchases, to being judge and jury on incredibly meaningful activities like arbitrating insurance, housing, credit and welfare claims. We must also celebrate and amplify the women role models who continue to boldly pioneer this space — like CEO AMD Lisa Su, OpenAI CTO Mira Murati or Joy Buolamwini, who founded The Algorithmic Justice League — so girls can see in STEM it isn’t just men behind the wheel.
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