Researchers Reduce Bias In AI Models While Maintaining Or Improving Accuracy

Machine-learning models can fail when they try to make predictions for individuals who were underrepresented in the datasets they were trained on.


For example, a model that forecasts the very best treatment option for somebody with a persistent disease may be trained utilizing a dataset that contains mainly male clients. That model may make inaccurate predictions for female patients when deployed in a health center.


To enhance outcomes, engineers can try balancing the training dataset by getting rid of data points until all subgroups are represented equally. While dataset balancing is promising, it frequently needs eliminating big amount of information, injuring the design's overall performance.


MIT researchers developed a brand-new method that recognizes and gets rid of specific points in a training dataset that contribute most to a design's failures on minority subgroups. By getting rid of far less datapoints than other techniques, this strategy maintains the overall accuracy of the design while improving its efficiency regarding underrepresented groups.


In addition, the strategy can determine hidden sources of predisposition in a training dataset that does not have labels. Unlabeled data are far more prevalent than labeled data for numerous applications.


This approach could likewise be integrated with other methods to enhance the fairness of machine-learning designs deployed in high-stakes situations. For example, it may someday help make sure underrepresented patients aren't misdiagnosed due to a biased AI model.


"Many other algorithms that try to address this problem presume each datapoint matters as much as every other datapoint. In this paper, we are showing that presumption is not real. There are particular points in our dataset that are contributing to this bias, and we can discover those data points, remove them, and improve performance," states Kimia Hamidieh, an electrical engineering and grandtribunal.org computer system science (EECS) graduate trainee at MIT and co-lead author of a paper on this strategy.


She wrote the paper with co-lead authors Saachi Jain PhD '24 and fellow EECS graduate trainee Kristian Georgiev; Andrew Ilyas MEng '18, PhD '23, a Stein Fellow at Stanford University; and senior authors Marzyeh Ghassemi, an associate teacher in EECS and a member of the Institute of Medical Engineering Sciences and the Laboratory for Details and Decision Systems, and Aleksander Madry, the Cadence Design Systems Professor at MIT. The research will exist at the Conference on Neural Details Processing Systems.


Removing bad examples


Often, machine-learning designs are trained utilizing substantial datasets gathered from lots of sources throughout the internet. These datasets are far too big to be thoroughly curated by hand, so they might contain bad examples that hurt design performance.


Scientists likewise know that some information points impact a model's performance on certain downstream tasks more than others.


The MIT researchers integrated these 2 ideas into an approach that determines and removes these bothersome datapoints. They seek to resolve a problem called worst-group error, which occurs when a model underperforms on minority subgroups in a training dataset.


The scientists' brand-new technique is driven by prior operate in which they presented an approach, called TRAK, ghetto-art-asso.com that determines the most crucial training examples for a specific model output.


For this new method, they take incorrect predictions the model made about minority subgroups and utilize TRAK to identify which training examples contributed the most to that inaccurate forecast.


"By aggregating this details throughout bad test forecasts in the best method, we are able to discover the particular parts of the training that are driving worst-group precision down overall," Ilyas explains.


Then they eliminate those specific samples and retrain the model on the remaining information.


Since having more information typically yields better total efficiency, getting rid of just the samples that drive worst-group failures maintains the design's overall accuracy while enhancing its efficiency on minority subgroups.


A more available approach


Across 3 machine-learning datasets, their technique outshined several techniques. In one instance, it improved worst-group accuracy while eliminating about 20,000 fewer training samples than a standard information balancing method. Their method likewise attained higher precision than approaches that need making modifications to the inner operations of a design.


Because the MIT method involves changing a dataset rather, it would be much easier for a professional to utilize and can be applied to many types of models.


It can likewise be made use of when predisposition is unidentified because subgroups in a training dataset are not identified. By determining datapoints that contribute most to a function the model is finding out, they can comprehend the variables it is using to make a forecast.


"This is a tool anybody can use when they are training a machine-learning design. They can look at those datapoints and see whether they are lined up with the ability they are attempting to teach the design," says Hamidieh.


Using the strategy to identify unknown bias would require intuition about which groups to search for, so the researchers want to verify it and explore it more fully through future human studies.


They also want to enhance the performance and reliability of their technique and ensure the technique is available and easy-to-use for professionals who might at some point release it in real-world environments.


"When you have tools that let you seriously look at the information and figure out which datapoints are going to result in bias or other unwanted habits, it gives you a primary step toward building designs that are going to be more fair and more reputable," Ilyas says.


This work is moneyed, in part, by the National Science Foundation and the U.S. Defense Advanced Research Projects Agency.