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Meredith Broussard, New York University – Bias in AI
On New York University Week: Eliminating bias in AI will require work.
Meredith Broussard, professor at the Arthur L. Carter Journalism Institute, examines strategies to do so.
Data journalist Meredith Broussard is a professor at the Arthur L. Carter Journalism Institute of New York University, research director at the NYU Alliance for Public Interest Technology, and the author of several books, including “More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech” and “Artificial Unintelligence: How Computers Misunderstand the World.”
Bias in AI
https://academicminute.org/wp-content/uploads/2025/09/09-15-25-NYU-Bias-in-AI-.mp3
My research is about the many ways that AI systems reproduce real-world biases and inequality. In my research I find that all AI systems discriminate by default.
The problem is that the training data that we’re using is data that comes from the real world.
So, the data that we’re feeding into AI systems has all of the biases of the real world. Instead of assuming that AI decisions are unbiased or neutral, it’s more useful to assume that the AI decisions are going to be biased or discriminatory in some way. Then, we can work to prevent AI from replicating historical problems and historical inequalities.
One thing I like to keep in mind is the difference between mathematical fairness and social fairness. Something that is divided equally mathematically is not the same as something that is divided equally socially. One example is If you have one chocolate chip cookie left and you have two children, you want to divide it in half, right? So mathematically we would divide the cookie 50-50. But in the real world, when you break a cookie, there’s a big half and a small half, so then there’s some negotiation and you want both kids to come out feeling like the division is fair.
AI is really great at math, but it’s not so good at social—and the social context matters. Social determinants of health, for example, directly affect individual outcomes and the health of our communities—and these are usually not factors that AI takes into consideration.
AI is powerful technology, but it is not inevitable—we have an opportunity now to be careful about how we use AI and be thoughtful about the kind of world we want to create with it.
Read More:
[MIT Press] – More Than a Glitch
[MIT Press] – Artificial Unintelligence
The post Meredith Broussard, New York University – Bias in AI appeared first on The Academic Minute.
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By On New York University Week: Eliminating bias in AI will require work.
Meredith Broussard, professor at the Arthur L. Carter Journalism Institute, examines strategies to do so.
Data journalist Meredith Broussard is a professor at the Arthur L. Carter Journalism Institute of New York University, research director at the NYU Alliance for Public Interest Technology, and the author of several books, including “More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech” and “Artificial Unintelligence: How Computers Misunderstand the World.”
Bias in AI
https://academicminute.org/wp-content/uploads/2025/09/09-15-25-NYU-Bias-in-AI-.mp3
My research is about the many ways that AI systems reproduce real-world biases and inequality. In my research I find that all AI systems discriminate by default.
The problem is that the training data that we’re using is data that comes from the real world.
So, the data that we’re feeding into AI systems has all of the biases of the real world. Instead of assuming that AI decisions are unbiased or neutral, it’s more useful to assume that the AI decisions are going to be biased or discriminatory in some way. Then, we can work to prevent AI from replicating historical problems and historical inequalities.
One thing I like to keep in mind is the difference between mathematical fairness and social fairness. Something that is divided equally mathematically is not the same as something that is divided equally socially. One example is If you have one chocolate chip cookie left and you have two children, you want to divide it in half, right? So mathematically we would divide the cookie 50-50. But in the real world, when you break a cookie, there’s a big half and a small half, so then there’s some negotiation and you want both kids to come out feeling like the division is fair.
AI is really great at math, but it’s not so good at social—and the social context matters. Social determinants of health, for example, directly affect individual outcomes and the health of our communities—and these are usually not factors that AI takes into consideration.
AI is powerful technology, but it is not inevitable—we have an opportunity now to be careful about how we use AI and be thoughtful about the kind of world we want to create with it.
Read More:
[MIT Press] – More Than a Glitch
[MIT Press] – Artificial Unintelligence
The post Meredith Broussard, New York University – Bias in AI appeared first on The Academic Minute.