Relying on tainted, inherently biased data to make critical business decisions and formulate strategies is tantamount to building a house of cards. Yet, recognizing and neutralizing bias in machine ...

Affinity Bias: We all have a natural tendency to gravitate toward people similar to us in terms of race, ethnicity, gender, age and lifestyle. This unconscious bias can lead us to hire people who look ...

When bias creeps into machine learning (ML) systems, it can lead to unfair outcomes, legal liabilities and reputational damage for organizations and their stakeholders. Machine learning systems are ...

One of the biggest barriers to using AI successfully is bias, which is one of the terms we defined last time, as follows: Bias, in a general context, refers to a predisposition or inclination towards ...

If you've been online much recently, chances are you've seen some of the fantastical imagery created by text-to-image generators such as Midjourney and DALL-E 2. This includes everything from the ...

Cognitive distortions, unconscious bias, cognitive bias, implicit bias, logical fallacies: you probably heard most or all of these terms. They can get really confusing if you don’t know the difference ...

As organizations try to promote greater diversity and inclusion within their ranks, many recruiters and hiring managers are focused on sourcing candidates from a variety of backgrounds. This is an ...

At birth, our minds are like blank sponges ready to absorb new information. Then, as we grow up, we may start to let some of what we’ve learned mess with how we take on new information. This is known ...

Do you know someone who seems oblivious to the consequences of their own actions? If they take all the credit for successes, but failure is never their fault, you might say that person has a strong ...

verybody has a favorite color and season—preferences that seem innate, always defying reason. Auditors are no different when it comes to haphazard sampling, a process in which—ideally—they choose ...