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      in Data Management & Statistics

      A technique to improve both fairness and accuracy in artificial intelligence

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      For workers who use machine-learning models to help them make decisions, knowing when to trust a model’s predictions is not always an easy task, especially since these models are often so complex that their inner workings remain a mystery.

      Users sometimes employ a technique, known as selective regression, in which the model estimates its confidence level for each prediction and will reject predictions when its confidence is too low. Then a human can examine those cases, gather additional information, and make a decision about each one manually.

      But while selective regression has been shown to improve the overall performance of a model, researchers at MIT and the MIT-IBM Watson AI Lab have discovered that the technique can have the opposite effect for underrepresented groups of people in a dataset. As the model’s confidence increases with selective regression, its chance of making the right prediction also increases, but this does not always happen for all subgroups.

      For instance, a model suggesting loan approvals might make fewer errors on average, but it may actually make more wrong predictions for Black or female applicants. One reason this can occur is due to the fact that the model’s confidence measure is trained using overrepresented groups and may not be accurate for these underrepresented groups.

      Once they had identified this problem, the MIT researchers developed two algorithms that can remedy the issue. Using real-world datasets, they show that the algorithms reduce performance disparities that had affected marginalized subgroups.

      “Ultimately, this is about being more intelligent about which samples you hand off to a human to deal with. Rather than just minimizing some broad error rate for the model, we want to make sure the error rate across groups is taken into account in a smart way,” says senior MIT author Greg Wornell, the Sumitomo Professor in Engineering in the Department of Electrical Engineering and Computer Science (EECS) who leads the Signals, Information, and Algorithms Laboratory in the Research Laboratory of Electronics (RLE) and is a member of the MIT-IBM Watson AI Lab.

      Joining Wornell on the paper are co-lead authors Abhin Shah, an EECS graduate student, and Yuheng Bu, a postdoc in RLE; as well as Joshua Ka-Wing Lee SM ’17, ScD ’21 and Subhro Das, Rameswar Panda, and Prasanna Sattigeri, research staff members at the MIT-IBM Watson AI Lab. The paper will be presented this month at the International Conference on Machine Learning.

      To predict or not to predict

      Regression is a technique that estimates the relationship between a dependent variable and independent variables. In machine learning, regression analysis is commonly used for prediction tasks, such as predicting the price of a home given its features (number of bedrooms, square footage, etc.) With selective regression, the machine-learning model can make one of two choices for each input — it can make a prediction or abstain from a prediction if it doesn’t have enough confidence in its decision.

      When the model abstains, it reduces the fraction of samples it is making predictions on, which is known as coverage. By only making predictions on inputs that it is highly confident about, the overall performance of the model should improve. But this can also amplify biases that exist in a dataset, which occur when the model does not have sufficient data from certain subgroups. This can lead to errors or bad predictions for underrepresented individuals.

      The MIT researchers aimed to ensure that, as the overall error rate for the model improves with selective regression, the performance for every subgroup also improves. They call this monotonic selective risk.

      “It was challenging to come up with the right notion of fairness for this particular problem. But by enforcing this criteria, monotonic selective risk, we can make sure the model performance is actually getting better across all subgroups when you reduce the coverage,” says Shah.

      Focus on fairness

      The team developed two neural network algorithms that impose this fairness criteria to solve the problem.

      One algorithm guarantees that the features the model uses to make predictions contain all information about the sensitive attributes in the dataset, such as race and sex, that is relevant to the target variable of interest. Sensitive attributes are features that may not be used for decisions, often due to laws or organizational policies. The second algorithm employs a calibration technique to ensure the model makes the same prediction for an input, regardless of whether any sensitive attributes are added to that input.

      The researchers tested these algorithms by applying them to real-world datasets that could be used in high-stakes decision making. One, an insurance dataset, is used to predict total annual medical expenses charged to patients using demographic statistics; another, a crime dataset, is used to predict the number of violent crimes in communities using socioeconomic information. Both datasets contain sensitive attributes for individuals.

      When they implemented their algorithms on top of a standard machine-learning method for selective regression, they were able to reduce disparities by achieving lower error rates for the minority subgroups in each dataset. Moreover, this was accomplished without significantly impacting the overall error rate.

      “We see that if we don’t impose certain constraints, in cases where the model is really confident, it could actually be making more errors, which could be very costly in some applications, like health care. So if we reverse the trend and make it more intuitive, we will catch a lot of these errors. A major goal of this work is to avoid errors going silently undetected,” Sattigeri says.

      The researchers plan to apply their solutions to other applications, such as predicting house prices, student GPA, or loan interest rate, to see if the algorithms need to be calibrated for those tasks, says Shah. They also want to explore techniques that use less sensitive information during the model training process to avoid privacy issues.

      And they hope to improve the confidence estimates in selective regression to prevent situations where the model’s confidence is low, but its prediction is correct. This could reduce the workload on humans and further streamline the decision-making process, Sattigeri says.

      This research was funded, in part, by the MIT-IBM Watson AI Lab and its member companies Boston Scientific, Samsung, and Wells Fargo, and by the National Science Foundation. More

    • 163 Shares99 Views

      in Data Management & Statistics

      A universal system for decoding any type of data sent across a network

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      Every piece of data that travels over the internet — from paragraphs in an email to 3D graphics in a virtual reality environment — can be altered by the noise it encounters along the way, such as electromagnetic interference from a microwave or Bluetooth device. The data are coded so that when they arrive at their destination, a decoding algorithm can undo the negative effects of that noise and retrieve the original data.

      Since the 1950s, most error-correcting codes and decoding algorithms have been designed together. Each code had a structure that corresponded with a particular, highly complex decoding algorithm, which often required the use of dedicated hardware.

      Researchers at MIT, Boston University, and Maynooth University in Ireland have now created the first silicon chip that is able to decode any code, regardless of its structure, with maximum accuracy, using a universal decoding algorithm called Guessing Random Additive Noise Decoding (GRAND). By eliminating the need for multiple, computationally complex decoders, GRAND enables increased efficiency that could have applications in augmented and virtual reality, gaming, 5G networks, and connected devices that rely on processing a high volume of data with minimal delay.

      The research at MIT is led by Muriel Médard, the Cecil H. and Ida Green Professor in the Department of Electrical Engineering and Computer Science, and was co-authored by Amit Solomon and Wei Ann, both graduate students at MIT; Rabia Tugce Yazicigil, assistant professor of electrical and computer engineering at Boston University; Arslan Riaz and Vaibhav Bansal, both graduate students at Boston University; Ken R. Duffy, director of the Hamilton Institute at the National University of Ireland at Maynooth; and Kevin Galligan, a Maynooth graduate student. The research will be presented at the European Solid-States Device Research and Circuits Conference next week.

      Focus on noise

      One way to think of these codes is as redundant hashes (in this case, a series of 1s and 0s) added to the end of the original data. The rules for the creation of that hash are stored in a specific codebook.

      As the encoded data travel over a network, they are affected by noise, or energy that disrupts the signal, which is often generated by other electronic devices. When that coded data and the noise that affected them arrive at their destination, the decoding algorithm consults its codebook and uses the structure of the hash to guess what the stored information is.

      Instead, GRAND works by guessing the noise that affected the message, and uses the noise pattern to deduce the original information. GRAND generates a series of noise sequences in the order they are likely to occur, subtracts them from the received data, and checks to see if the resulting codeword is in a codebook.

      While the noise appears random in nature, it has a probabilistic structure that allows the algorithm to guess what it might be.

      “In a way, it is similar to troubleshooting. If someone brings their car into the shop, the mechanic doesn’t start by mapping the entire car to blueprints. Instead, they start by asking, ‘What is the most likely thing to go wrong?’ Maybe it just needs gas. If that doesn’t work, what’s next? Maybe the battery is dead?” Médard says.

      Novel hardware

      The GRAND chip uses a three-tiered structure, starting with the simplest possible solutions in the first stage and working up to longer and more complex noise patterns in the two subsequent stages. Each stage operates independently, which increases the throughput of the system and saves power.

      The device is also designed to switch seamlessly between two codebooks. It contains two static random-access memory chips, one that can crack codewords, while the other loads a new codebook and then switches to decoding without any downtime.

      The researchers tested the GRAND chip and found it could effectively decode any moderate redundancy code up to 128 bits in length, with only about a microsecond of latency.

      Médard and her collaborators had previously demonstrated the success of the algorithm, but this new work showcases the effectiveness and efficiency of GRAND in hardware for the first time.

      Developing hardware for the novel decoding algorithm required the researchers to first toss aside their preconceived notions, Médard says.

      “We couldn’t go out and reuse things that had already been done. This was like a complete whiteboard. We had to really think about every single component from scratch. It was a journey of reconsideration. And I think when we do our next chip, there will be things with this first chip that we’ll realize we did out of habit or assumption that we can do better,” she says.

      A chip for the future

      Since GRAND only uses codebooks for verification, the chip not only works with legacy codes but could also be used with codes that haven’t even been introduced yet.

      In the lead-up to 5G implementation, regulators and communications companies struggled to find consensus as to which codes should be used in the new network. Regulators ultimately chose to use two types of traditional codes for 5G infrastructure in different situations. Using GRAND could eliminate the need for that rigid standardization in the future, Médard says.

      The GRAND chip could even open the field of coding to a wave of innovation.

      “For reasons I’m not quite sure of, people approach coding with awe, like it is black magic. The process is mathematically nasty, so people just use codes that already exist. I’m hoping this will recast the discussion so it is not so standards-oriented, enabling people to use codes that already exist and create new codes,” she says.

      Moving forward, Médard and her collaborators plan to tackle the problem of soft detection with a retooled version of the GRAND chip. In soft detection, the received data are less precise.

      They also plan to test the ability of GRAND to crack longer, more complex codes and adjust the structure of the silicon chip to improve its energy efficiency.

      The research was funded by the Battelle Memorial Institute and Science Foundation of Ireland. More