MIT’s Neural Network Algorithms Can Counter Dataset Bias Issues

Neural network algorithms are set to tackle dataset bias and enhance productivity in the future

Artificial intelligence or AI models have a mixed reputation in the global tech market. On one hand, smart performance and boosting productivity with automation create a good image for AI models. On another hand, the negative image includes bias, racism, sentience, and many more. The dataset bias issue is a constant struggle for tech companies to fight against to drive better customer engagement. Meanwhile, the Massachusetts Institute of Technology the world-famous MIT has built up neural network algorithms to tackle dataset bias for effective data management in the nearby future. Neural network algorithms from MIT, integrated into the AI models, can perform more accurately for underrepresented subgroups. Let's explore how the MIT neural network algorithms can settle the ongoing issue of dataset bias in AI models to have seamless data management in the global tech market.

The piece of good news for the global tech market is that MIT and the MIT-IBM Watson AI Lab researchers have discovered neural network algorithms to have the opposite effect on solving the dataset bias issue. It will represent the underrepresented people in the dataset for effective data management. The neural network algorithms can provide confidence to AI models with selective regression to increase the rate of right prediction efficiently and effectively.

The MIT research group has successfully developed two neural network algorithms for the ongoing dataset bias issue. AI models showed that neural network algorithms can reduce the performance disparities for the underrepresented data set or the marginalized subgroups of the data set. MIT wants to reduce the rate of data error in AI models in a smart way— developing neural network algorithms with regression for an appropriate prediction system.

Regression is used for completing prediction tasks of AI models to make one of two options for each data input. The main aim of MIT researchers is to guarantee that the error rate of AI models must enhance with the selective regression method. This, in turn, will enhance the subgroup performances to mitigate the dataset bias problem. Monotonic selective risk will help the AI model to perform better across all subgroups to ensure good quality outputs by including all kinds of datasets.

Features of the MIT neural network algorithms

As mentioned above, there are two neural network algorithms from the MIT research group. One neural network algorithm ensures the features of AI models must be used to create predictions consisting of all kinds of real-time data including race, sex, and many other sensitive attributes. This sensitive information may not be used for any business decisions in the global tech market. But it can be used for organizational policies for efficient data management.

The second neural network algorithm helps to employ a calibration technique for guaranteeing AI models provide the same predictions for input, despite the fact that the attributes are present or not.

Achieving more goals with algorithms

Meanwhile, the MIT group also wants to explore some different techniques by leveraging less sensitive data during the AI model training to tackle the dataset bias as well as navigate through privacy issues. Another aim is to enhance the AI model's confidence with the correct predictions. This will benefit the human employees as they will receive less workload and boost the decision-making process in the nearby future.