While blatant discrimination is easy to condemn because of how obvious it is, there are subtler, more insidious forms that also need to be rooted out.
While women in poverty are more likely to experience sexual harassment and domestic abuse than higher-income women, people assume it is less distressing for them.
People who object to affirmative action were more likely to discriminate against job candidates with Black-sounding names than those who supported it, whether or not they had to rush.
Potential inaccuracies in CDC high school surveys may have created an exaggerated perception that LGBQ youth engage in risky behaviors, new research shows.
An analysis of over 1,000 headlines shows key differences in how US media portray the aggressors and victims in the two conflicts.
Far from a touchy-feely community, research shows online platforms such as Airbnb tend to strengthen users’ narcissism and class biases.
Creating bias-free AI systems is easier said than done. A computer scientist explains how controlling bias could lead to fairer AI.
Past controversies and their impacts show how important it is to make the right appointment to the position.
New research shows that frequent posters appear needy, which pushes up against the expectation that ‘real men’ be stoic and self-sufficient.
Regardless of the input, AI image generators will have a tendency to return certain kinds of results. This is where the potential for bias arises.
A survey of nearly 900 politicians in Germany, Switzerland, Belgium and Canada reveals that they systematically overestimate their electorate’s conservatism on a range of issues.
Unlike a human editor, AI cannot explain their decisions or reasoning in a meaningful way. This can be a problem in a field where accountability and transparency are important.
Public data about individual donors’ political contributions supports the perception that American academia leans left.
Companies that want to avoid the harms of AI, such as bias or privacy violations, lack clear-cut guidelines on how to act responsibly. That makes internal management and decision-making critical.
Software you may already use every day can track your productivity for your employer.
Teachers judged the same math work differently based on whether the work was associated with male or female names.
We can now store information outside of our brains, and use computers to retrieve it. That ought to make learning and remembering easy, right?
Understanding how hiring managers evaluate candidates can help us understand current hiring prejudices and, hopefully, help us overcome them.
People tend not to think that their own emotions could simply be wrong. But research shows that people excessively dislike others who disagree with them.
From Aristotle to Darwin, inaccurate and biased narratives in science not only reproduce these biases in future generations but also perpetuate the discrimination they are used to justify.