HR teams are responsible for ensuring AI tools used in the hiring process have a clear, unbiased view of what qualified ...

When it comes to AI (Artificial Intelligence), there's usually a major focus on using large datasets, which allow for the training of models. But there’s a nagging issue: bias.

In this post, we’ll provide examples of how bias can influence AI results and how AI algorithms are used, define sources of bias that can be associated with AI, and discuss mechanisms for legal ...

A recent study by Stanford University offers a warning that therapy chatbots could pose a substantial safety risk to users ...

To mitigate the bias early in the process, Jeong has proposed a three-step process to insert fairness in, when addressing missing values, encoding data and balancing data. “Right now, AI algorithms ...

Even among the most proactive companies, a majority are not yet taking steps to wring out bias from AI, a 2021 survey by McKinsey found.. For example, 47% of respondents, less than half, reported ...

Victims of data bias can also suffer adverse outcomes resulting from intrinsically biased AI algorithms. As many as three quarters (76%) of organisations surveyed by Progress believe wider societal ...

Chances are that you’re familiar with the concept of bias. It is widespread, turning up in discussions about scientific discoveries, politics or cognitive processes. In artificial intelligence ...

Given that AI is now involved in everything from jurisprudence to lending, it's massively important for the future of our increasingly data-driven society that the issue of bias in AI be taken ...

As chief data officer for the Cybersecurity and Infrastructure Security Agency, Preston Werntz has made it his business to understand bias in the datasets that fuel artificial intelligence systems.

The AI also appeared to present gender as a binary, rather than show examples of more fluid gender expression. AI showed women for inputs including non-specialized job titles such as journalists ...