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How can machine learning be both fair and accurate?

When it comes to utilising machine learning to make public policy decisions, researchers at Carnegie Mellon University are questioning a long-held belief that there is a trade-off between accuracy and fairness.

Concerns have grown as the use of machine learning has grown in areas such as criminal justice, hiring, health care delivery, and social service interventions, raising questions about whether such applications introduce new or amplify existing inequities, particularly among racial minorities and people with low income. Adjustments are made to the data, labels, model training, scoring systems, and other parts of the machine learning system to defend against this bias. The underlying theoretical assumption is that the system will become less accurate as a result of these modifications.


In new research just published in Nature Machine Intelligence, a CMU team hopes to refute that belief. Rayid Ghani, a professor in the School of Computer Science's Machine Learning Department and the Heinz College of Information Systems and Public Policy; Kit Rodolfa, a research scientist in ML; and Hemank Lamba, a post-doctoral researcher in SCS, tested that assumption in real-world applications and discovered that the trade-off was negligible in practice across a range of policy domains.


"You can truly obtain both. You don't have to forgo precision to create fair and equal processes "Ghani said. "However, it does need the conscious creation of fair and equitable processes. Off-the-shelf solutions aren't going to cut it."

Ghani and Rodolfa concentrated on circumstances in which in-demand resources are restricted and machine learning techniques are utilised to assist in resource allocation. The researchers looked at four systems: prioritising limited mental health care outreach based on a person's risk of returning to jail to reduce reincarceration; predicting serious safety violations to better deploy a city's limited housing inspectors; modelling the risk of students not graduating from high school on time to identify those who need additional support; and assisting teachers in reaching crowdfunding goals for classroom needs.


In each case, the researchers discovered that models tuned for accuracy—a common strategy in machine learning—could accurately predict the desired results, but there were significant differences in intervention recommendations. When the researchers made tweaks to the models' outputs aimed at increasing fairness, they observed that discrepancies based on race, age, or income—depending on the situation—could be addressed without sacrificing accuracy.


Ghani and Rodolfa believe that their findings will persuade other researchers and policymakers to reconsider using machine learning in decision-making.


"We urge the artificial intelligence, computer science, and machine learning groups to stop assuming that accuracy and justice are mutually exclusive and instead start creating systems that optimise both," Rodolfa said. "We expect that policymakers will use machine learning as a decision-making tool to assist them to attain more egalitarian outcomes."




reference- techexplore



26 commentaires


This is a really interesting study! It's encouraging to see research pushing back against the idea that fairness and accuracy are always at odds in machine learning. It sounds like the key is a deliberate effort to build fair processes, not just relying on default settings. Overcoming that accuracy vs. fairness obstacle could be a real block breaker in deploying ML for public good. Really hopeful this research inspires more focus on building ethical and equitable AI systems.

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16 mai

This research is encouraging because it shows that fairness and accuracy in machine learning don’t have to be at odds. In my work with community projects, I've seen how biased algorithms can deepen inequalities. It’s refreshing to hear that by thoughtfully designing systems—like how Crazy Cattle 3D uses AI for better livestock management—we can achieve equity and precision simultaneously. Conscious effort in model tuning really seems to make a difference.


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13 mai

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Machine learning can be both fair and accurate, challenging the belief of a trade-off. Researchers are finding that adjustments for fairness don't necessarily reduce accuracy in areas like criminal justice and healthcare. It's like mastering a difficult level in Snow Rider 3D – achieving both speed and precision takes conscious effort. Off-the-shelf solutions won't cut it; we need to consciously create fair and equitable systems to optimize both fairness and accuracy.

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03 mai

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