Many companies are shifting towards implementing machine learning-based systems to manage their day-to-day operations. Eventually, they become increasingly reliant on these systems to carry out critical business decisions. In some scenarios, the machine learning systems can be programmed to operate autonomously, making it crucial that the automated decision-making functions as it is supposed to.
However, the quality of machine learning systems is dependent on the data used to train them. If there are any biases in the data input into a machine learning algorithm, the outcome can be systems that are inherently untrustworthy and potentially harmful.
Let’s look at a few steps businesses can take to reduce machine learning bias.
1. Identify potential sources of bias.
One method of addressing and mitigating bias is by examining the data and observing how different types of bias can impact the data being used to train the machine learning model. Has the data been selected without bias? Are there any biases happening as a result of errors in data capture or observation? Is the historic data set tainted with prejudice or confirmation bias? Such questions can help to identify and potentially eliminate that bias.
2. Establish rules and guidelines for eliminating bias
To manage bias effectively, businesses need to prepare guidelines, rules and set procedures for discovering, communicating and mitigating potential data set bias. Forward-thinking businesses should document cases of bias as soon as they occur, specifying the steps needed to identify bias and explaining the efforts needed to mitigate said bias. By adhering to these rules and communicating them in an open and transparent way, businesses can make the right steps to address problems of machine learning model bias.
3. Identify accurate representative data.
Before collecting and aggregating data for machine learning model training, businesses should initially try to figure out what a representative data set looks like. Data scientists must use their data analysis skills to understand the nature of the population that is supposed to be modeled along with the characteristics of the data used to develop machine learning models. These two aspects should match in order to create a data set that holds as little bias as possible.
4. Document and share how data is chosen and cleansed.
Different types of bias occur while selecting data from large data sets and also during data cleansing operations. To make sure that limited bias-inducing mistakes are made, businesses should accurately document their methods of selection and cleansing of data. They must also allow others to analyze when and if the models show any indication of bias. In this instance, transparency facilitates root-cause analysis of sources of bias that are to be eliminated in future model iterations.
5. Assess the model for performance and selection bias as well as overall performance.
Machine learning models are usually evaluated before being implemented into an operation. Usually, these evaluation steps tend to focus on aspects of model accuracy and precision. Businesses must also enforce measures of bias identification in their model evaluation steps. Even if the model functions with a decent level of accuracy and precision for certain tasks, there are chances it could fail depending on the measure of bias, which may in turn highlight issues with the training data.
6. Monitor and review models in operation.
There is a difference between the way machine learning models perform in training and the way it performs in the real world. Businesses need to develop methods for monitoring and continuously reviewing the models as they perform their functions. If there are any indications that a particular form of bias exists within the results, then the business needs to take action before the biases lead to significant and irreparable harm.
6. Limiting machine learning bias leads to the development of more robust systems
If bias becomes integrated with machine learning models, it can lead to an adverse impact on our daily lives. The bias can be exhibited in the form of exclusion. For example, it can lead to certain groups being denied loans or not being able to access the technology or the technology not functioning the same for everyone.
As AI and machine learning continue to become an integral aspect of our lives, the risks from bias only seem to get bigger. Hence, businesses, researchers and developers have an inherent responsibility to minimize bias in AI systems. Often, it relies on ensuring that the data sets are representative and that the interpretation of data sets is understood in the way it was intended. However, simply ensuring that data sets are not biased will not actually eliminate bias. As a result, it is crucial to have a diverse team of people working together for the development and precision of AI and machine learning models.
