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Economists like to draw triangles. In trade, you can’t have high tariffs, no retaliation, and unchanged prices. In monetary policy, you can’t fix interest rates, fix the money supp...
In this tutorial, we walk through a complete, end-to-end workflow for correcting bias in survey data using the balance library. We simulate a realistic population, deliberately int...
A new debiasing technique called WRING avoids creating or amplifying biases that can occur with existing debiasing approaches. The post Solving the “Whac-A-Mole Dilemma”: A Smarter...
AI systems don’t just process information; they systematically ‘judge’ people in ways that resemble human trust, but with important differences, according to a new study by researc...
Back in the 1980s, growing up as an ethnic minority in the United Kingdom was especially hard. Racism was still rampant, and it was well known then that if you applied for jobs and...
Every machine learning model fails in one of two ways. It's either too simple to learn the pattern in your data, or too complex and ends up memorizing noise instead. These two fail...
AI systems demonstrate predictable and systematic biases that differentiate their judgment of people from human intuition and holistic evaluation.
Identical essays get different feedback in Stanford study and that can have consequences on what students learn.
Dividing resources often divides people. Scientists have struggled to find fair and equitable decision rules. Here we consider a way to divide gains obtained from collaboration.
A new debiasing technique called WRING avoids creating or amplifying biases that can occur with existing debiasing approaches.
Researchers tested 14 major AI models on religious bias and found a consistent pattern: models subtly favor some faiths over others, with Grok showing the strongest bias and Anthro...
While machine learning (ML) models demonstrate high predictive accuracy, recent studies reveal that ML models underperform for smaller subcohorts such as racial and ethnic minoriti...
Predictive algorithms in hiring and finance risk perpetuating systemic biases and unfair decision-making. The post Carissa Véliz: Predictive technologies require enlightened decisi...
MIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
Learn how to use Mimesis library to generate a balanced, counterfactual dataset that helps analyze potential bias in your models.
Artificial intelligence has become increasingly embedded in hiring, promotion, and employee management, which means that employers face heightened legal risks. From automated résum...
AI is making real decisions about real people. Here’s what goes wrong and why it matters more than you think.Continue reading on Medium »
Researchers warn that a growing ‘algorithmic monoculture’ is locking qualified minority candidates out of the job market.
Correctional nurses pride themselves on objectivity, fairness, and professionalism. Yet like all humans, we carry unconscious assumptions that can influence our decisions in ways w...
Negativity bias is the tendency to give more weight to the negative than the positive. For example, people tend to weigh wrongs done to them more heavily than the goodRead More
Time and time again, research has shown that the hiring process is biased and unfair. Factors like unconscious racism, sexism, and ageism, even the weather on the day of the interv...
AI does not imagine beauty on its own. It learns from data. These datasets overwhelmingly reflect existing social hierarchies. The post Beauty, Bias, And Algorithm: AI Beauty Tool...
A 2025 study found that AI-generated summaries influenced users to make purchase decisions 84 per cent of the time, even though the summaries contained hallucinated or altered fact...
In part one of this series, we explored whether a male-dominated AI ecosystem risks widening the gender gap. The conclusion... The post It May Not Be Intentional, But AI Bias Is Re...
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