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AI tradeoffs: Balancing powerful models and potential biases


Image of a magnifying glass above balls to represent identifying bias in AI.

Andrea Gagliano


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AI is struggling to adjust to 2020

As developers unlock new AI tools, the risk for perpetuating harmful biases becomes increasingly high — especially on the heels of a year like 2020, which reimagined many of our social and cultural norms upon which AI algorithms have long been trained.

A handful of foundational models are emerging that rely upon a magnitude of training data that makes them inherently powerful, but it’s not without risk of harmful biases — and we need to collectively acknowledge that fact.

Recognition in itself is easy. Understanding is much harder, as is mitigation against future risks. Which is to say tha

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