In December, the European Union passed the AI Act, the first major law aiming to regulate technologies that fall under the umbrella of artificial intelligence. The legislation might have arrived sooner, but the sudden success of ChatGPT in late 2022 demanded the act be updated.
The EU’s act, however, does not mention fairness—a measure looking at how well a system avoids discrimination. The field studying fairness in machine learning (a sub-field of AI) is relatively new, so clear regulation is still in development.
Mike Teodorescu, a University of Washington assistant professor in the Information School, proposes in a new paper that private enterprise standards for fairer machine learning systems would inform governmental regulation.
The paper was published Feb. 15 by the Brookings Institution as part of its series “The Economics and Regulation of Artificial Intelligence and Emerging Technologies.”
UW News spoke with Teodorescu about the paper and the field of machine learning fairness.
To start, could you explain what machine learning fairness is?
Teodorescu: It is essentially concerned with ensuring that a machine learning algorithm is fair to all categories of users. It combines computer science, law, philosophy, information systems and some economics as well.
For example, if you’re trying to create software to automate hiring interviews, you might have a group of HR people interview many candidates with diverse backgrounds and experiences and recommend a binary outcome—hire or don’t hire.
Data from actual HR interviews can be used to train and test a machine learning model. At the end of this process, you get accuracy—the percent the model got correct. But this percentage does not capture how well the algorithm performs when considering certain subgroups. U.S. law forbids discrimination based on protected attributes, which include gender, race, age, veteran status and so on.
In the simplest terms, as an example, if you count the number of veterans that you would like to hire, then the algorithm should hire independent of the protected attribute. Of course, this becomes more complex as you have more intersections of subgroups—you might have race, age, socioeconomic status and gender.
From a practical perspective, if you have a system of equalities for dozens of values of protected attributes, it is unlikely that all of them will be satisfied at the same time. I don’t think we have a generalizable solution and we do not have yet an optimal way to check for AI fairness.
What is it important for the general public to understand about machine learning fairness?
It helps to understand procedural fairness, which looks at the methods that are used to come up with decisions. A user might want to ask, “Do I know if this software is using machine learning to make some prediction about me? If yes, what kind of inputs is it taking? Can I correct an incorrect prediction? Is there a feedback mechanism by which I can challenge it?”
This principle is actually found in privacy laws in Europe and California, where we can object to certain information being used. That level of transparency would be great in the case of a machine learning algorithm being applied to make some decision about you. Maybe there is an option to select what variables it’s using to show you certain ads. Now, I’m not sure that’s something we will see in the very near future, but it’s something users might care about.
What’s impeding fairness standards from being widely adopted by companies?
I think it’s a problem of incentives. From an economic perspective, companies want to bring products to market as quickly as possible. If users get an app that uses image recognition AI, they likely won’t read the Terms of Service. So they’re probably not going to spend the time to go through training on whether the tool is fair or not. Many users might not even know that it’s possible for a tool to be unfair.
For a company right now, the incentive to develop such systems would be to put the company at the technological forefront and to signal quality—that its AI tools are fairer than its competitors.” But if the users do not know about this being a problem, they may not be worried about which company’s product is fairer. Probably 10 years from now, many more people will care about fairness, just like they do about cybersecurity and data privacy. Cybersecurity wasn’t such a common concern until we had a lot of these breaches.
More information:
Fairness in machine learning: Regulation or standards? www.brookings.edu/articles/fai … lation-or-standards/
University of Washington
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Q&A: What is the best route to fair AI systems? (2024, February 16)
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