Today’s AI can work so well that people, businesses, governments and economies can easily rely on it without actually understanding how it works.
The specific examples cited—such as real-time surveillance in public spaces and social scoring being viewed as"unacceptable risk," and generative AI being seen as"high risk"—provide us with a loose framework to think about how the systems in question impact people.
With more advanced capabilities, it is unclear how these new machine learning-based algorithms use data derived from our personal information and/or behavior. The issue with the black box is that the underlying assumptions can’t be challenged, the data used for training can’t be interrogated and the inherent biases are left unchecked.
We need to devise new methods to understand and validate how these algorithms function and what underlying assumptions drive the outcomes. We need to explore not just what an algorithm does at a current state but how it can be influenced to produce a different outcome.has been a hot topic for some time. Generally speaking, society has come to the understanding that there are many forms of bias present in any of these algorithms, ranging from model design to bias driven by training data sets.
Because these algorithms have the potential to affect people’s livelihoods and outcomes, we must make sure we are not solely dependent on the algorithm—hence the banning of specific technologies or use cases. The debate should shift from the ban itself to how a technology company can rebuild trust and combat these biases in order to have a viable commercial strategy..
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