A Physicists’ Guide To The Ethics Of Artificial Intelligence

Physics may seem like its own world, but different sectors using machine learning are all part of the same universe.

In 2017, Savannah Thais attended the NeurIPS machine-learning conference in Long Beach, California, hoping to learn about techniques she could use in her doctoral work on electron identification. Instead, she returned home to Yale with a transformed worldview.

At NeurIPS, she had listened to a talk by artificial intelligence researcher Kate Crawford, who discussed bias in machine-learning algorithms. She mentioned a new study showing that facial-recognition technology, which uses machine learning, had picked up gender and racial biases from its dataset: Women of color were 32% more likely to be misclassified by the technology than were White men.

The study, published as a master’s thesis by Joy Adowaa Buolamwini, became a landmark in the machine-learning world, exposing the ways that seemingly objective algorithms can make errors based on incomplete datasets. And for Thais, who’d been introduced to machine learning through physics, it was a watershed moment.

“I didn’t even know about it before,” says Thais, now an associate research scientist at the Columbia University Data Science Institute. “I didn’t know these were issues with the technology, that these things were happening.”

After finishing her PhD, Thais pivoted to studying the ethical implications of artificial intelligence in science and in society. Such work often focuses on direct impacts on people, which can seem entirely separate from algorithms designed to, say, identify the signature of a Higgs boson in a particle collision against a mountain of noise.

But these issues are interwoven with physics research, too. Algorithmic bias can influence physics results, particularly when machine-learning methods are used inappropriately.

And work done for the purpose of physics likely won’t stay in physics. By pushing machine-learning technology ahead for science, physicists are also contributing to its improvement in other areas. “When you’re in a fairly scientific context, and you’re thinking, ‘Oh, we’re building these models to help us do better physics research,’ it’s pretty divorced from any societal implications,” Thais says. “But it’s all really part of the same ecosystem.”

Trusting your models

In traditional computer models, a human tells the program each parameter it needs to know to make a decision—for example, the information that a proton is lighter than a neutron can help a program tell the two types of particles apart.[…]

Physics may seem like its own world, but different sectors using machine learning are all part of the same universe.

Trusting your models

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