Why we need biased AI: How including cognitive biases can enhance AI systemsThilo HagendorffSarah Fabi
We often shorthand our explanation of AI bias by blaming it on biased training data. The reality is more nuanced: bias can creep inlong before the datais collected as well as atmany other stagesof the deep-learning process. For the purposes of this discussion, we’ll focus on t...
It’s important to understand that AI is not just one algorithm. Instead, it is an entire machine learning system that can solve problems and suggest outcomes. Let’s look at how AI works step-by-step. Input The first step of AI is input. In this step, an engineer must collect the d...
A Stanford cardiologist and expert in artificial intelligence and machine learning explains where biased algorithms come from. He offers advice for preventing them and enabling improved decision support for better outcomes.
opening the door for new types of data. That has coincided with anexplosion of generative-AI capabilities. “I think we have not even scratched the surface of where generative-AI applicability is going to take us,” she says. “There are problems we couldn’t solve three months ago that ...
We therefore think a lot about the behavior of AI systems we build in the run-up to AGI, and the way in which that behavior is determined. Since our launch of ChatGPT, users have shared outputs that they consider politically biased, offensive, or otherwise objectionable. In many cases...
You might say, "Of course not!" And that would be a reasonable response. But there are some researchers, like me, who argue the opposite: AI systems like ChatGPTshould indeed be biased—but not in the way you might think. Removingbiasfrom AI is a laudable goal, but blindly eliminating...
As artificial intelligence (AI) continues to expand its reach into virtually every aspect of our lives, an important ethical question arises: how do we ensure that bias is not present inmachine learning? After all, if AI is deeply embedded in technologies that shape our lives and decisions, ...
Just as data can be biased, it can also be insufficient. Without enough data, machine learning models can fail to converge or provide reliable predictions. This is the problem of underestimation. Amazon recently trained amachine learning model to screen applicantsin its hiring process, but like ...
For instance, when developing a language generation model for customer service interactions, the training data should include a range of customer profiles to avoid biased responses that favor certain demographics, Jyoti said. Continuous evaluation of an AI system’s performance is also important...