“A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.”
Many businesses are using machine learning to analyze massive volumes of data, from assessing credit for loan applications to checking legal contracts for flaws to reviewing employee conversations with consumers to spot inappropriate behavior. Building and deploying machine-learning engines is now easier due to newer and better technologies.
The "garbage in, garbage out" principle still applies to machine learning algorithms, despite the fact that they help businesses achieve new efficiencies. Biased data is the kind of "junk" that self-learning systems deal with. Giving biased data to self-learning systems without checking the results might have unexpected and occasionally harmful results.
You will learn more about machine learning bias in this blog.
What is Machine Learning Bias?
Tom Mitchell coined the term bias in 1980 in a study titled "The need for biases in learning generalizations." In order for the model to generalize better for the larger dataset with a variety of additional traits, some features were given more weight than others in the concept of bias. In machine learning, bias actually improves generalization and makes our model less sensitive to a single data point.
However, the issue arises when the outcomes of our assumptions for a more extended method are consistently biased. Even if we leave out the features we don't want our model to emphasize, the algorithm may frequently be biased on some features. They accomplish this by inferring the latent representation of those features from features that are already available.
This is troubling since machine learning models have begun to play a larger part in many important life decisions, including loan applications, medical diagnoses, credit card fraud detection, and suspicious activity detection from CCTV. The bias in machine learning will, therefore, not only provide results based on societal stereotypes and beliefs, but will also reinforce them.
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Bias vs. Variance
When developing systems that can produce consistently correct results, data scientists and others engaged in the development, training, and application of machine learning models must take into account both bias and variance.
Similar to bias, variance is a mistake that occurs when machine learning makes erroneous assumptions about the training data. Variance, as contrast to bias, is a response to actual, valid oscillations in the data sets.
But even though these variations or noise shouldn't affect the desired model, the system is still using it to model. In other words, variance is a sensitivity to little variations in the training set that, like bias, can result in false positives.
Contrary to popular belief, bias and variation are related in that a certain amount of variance can aid in the reduction of prejudice. If the data population is sufficiently diverse, biases ought to be masked by the variance.
As a result, the aim of machine learning is to achieve a balance, or tradeoff, between the two to create a system that makes the lowest possible errors.
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Types of Machine Learning Bias
The different types of Machine learning Bias are given below :
Types of Machine Learning Bias
This happens when an algorithm that runs the calculations that supports machine learning has a malfunction. Another reason for algorithmic bias is insufficient training data.
Predictions from the model may also be consistently poorer for under- or unrepresented groups if the data used to train the algorithm are disproportionately representative of specific groups of people. Different forms of algorithmic bias might appear, each with differing degrees of negative effects on the subject group.
This occurs when the data that was used to train the machine learning model has an issue. This type of bias occurs when the data that was used to train the system is either too unrepresentative or too small. For instance, the system will be trained to believe that all teachers are male if training data exclusively includes male teachers.
Since the data used to train the system in this instance reflects current prejudices, preconceptions, and/or incorrect societal assumptions, those biases are also included into the machine learning process.
The computer system would reinforce a real-world gender prejudice about healthcare workers, for instance, if it used data on medical professionals that only comprises male doctors and female nurses.
This bias occurs due to core issues with the accuracy of the data and the methods that were used to evaluate or collect it.
For example, a system that is being trained to accurately assess weight will be biased if the weights included in the training data were consistently rounded up, and using images of happy employees to train a system that is meant to assess a workplace environment may be considered biased if the employees in the photos knew that they were being evaluated for happiness.
This occurs when a crucial data point is excluded from the data set that is being used, which may occur if the modelers fail to notice the importance of the data point.
The data preprocessing stage is where exclusion bias is most prevalent. Most frequently, vital data that is valuable but is deemed to be trivial is deleted. But it can also happen when specific information is purposefully left out.
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Prevention of Machine Learning Bias
Along with deciding how and where machine-learning models should initially be implemented, managers must be on the lookout for potential reputational and regulatory concerns that can arise from skewed data. There are growing best practices that can aid in preventing bias in machine learning. Few of them are given below.
Consideration of bias when selecting training data
Predictive engines are the fundamental component of machine learning models. Machine learning models are trained on large data sets to predict the future using historical data.
When purpose is known, models are able to read large amounts of material and comprehend it. They can pick out differences between, say, a cat and a dog by absorbing millions of pieces of data, including accurately categorized animal photographs.
Machine-learning models have the benefit over conventional statistical models in that they can swiftly process huge amounts of records and, as a result, predict outcomes more precisely. However, because machine learning models can only anticipate what they have been taught to predict, their predictions are only as accurate as the training set of data.
For example, if the historical data used to train the machine-learning model reflects past judgments that resulted in few women being hired or admitted to a college, it may wrongly block out female applicants while scanning reams of resumes or applications to institutions.
These biases are particularly prevalent in data sets that are the result of judgments made by a limited number of individuals. Managers must always keep in mind that bias will exist whenever humans are involved in the decision making process. The smaller the group, the more likely it is that the bias will not be overcome by others.
Root out Bias
The first step in addressing potential bias in machine learning is to honestly and openly question what preconceptions might be present in an organization's processes today.
Next, you should actively look for those biases in the data you are using. Many businesses use outside specialists to question their previous and present procedures because this can be a touchy subject. Once potential biases have been detected, businesses can prevent them by removing problematic data or particular parts of the incoming data set.
A business can also add new data to the training data set to balance out data that could be harmful. As an example, some businesses now consider social media data when assessing the likelihood that a consumer or client may commit a financial crime.
If a customer starts sharing images on social media from nations with possible terrorism or money-laundering connections, a machine-learning algorithm may flag that person as high risk.
However, this conclusion can be challenged and overturned if a user's nationality, occupation, or travel preferences are taken into account for a native visiting their home country or a journalist or businessperson on a business trip.
Managers should avoid taking data sets at face value as a best practice, regardless of the method employed. We can safely assume that all data are biased. The issue is how to spot it and eliminate it from the model.
Counter bias in “dynamic” data sets
Avoiding bias when the data set is dynamic presents another problem for machine learning algorithms. Machine-learning models are trained on past events, hence they cannot forecast future outcomes based on past behavior that has not been statistically quantified.
For instance, despite the widespread use of machine learning in fraud detection, thieves can outwit models by coming up with creative ways to steal or avoid being caught. By employing sneaky strategies like speaking in code, employees can evade the detection of bad conduct by machine learning systems.
Some businesses utilize more advanced, cognitive, or artificial intelligence modeling approaches to simulate hypothetical situations in an effort to derive novel conclusions from the available data. Then, a more modern machine-learning algorithm is manually created using the data.
Yet even in this circumstance, managers run the danger of introducing bias into a model when they add new parameters. Predictive models are increasingly being powered by social media data, such as images shared on Twitter and Facebook, for instance. However, a model that incorporates this kind of information could include unimportant biases into its predictions.
Managers must make sure the new criteria are comprehensive and experimentally evaluated, which is another recommended practice, to prevent this from happening.
Without them, the model might be askew, especially in those cases where the data is inadequate or lacking. Inadequate data might affect, for instance, lending decisions for classes of borrowers that a bank intends to lend to in the future but has never before lent to.
Balance transparency against performance
One temptation with machine learning is to "let the machine figure it out" by feeding it ever-larger volumes of data through an advanced training infrastructure.
Despite the fact that this technique is effective for creating sophisticated predictive models rapidly and affordably, it has the drawback of limiting visibility and running the danger of the "machine going wild" and developing an unconscious bias as a result of training using erroneous data.
Another difficulty is that it is very difficult to describe how sophisticated machine learning models actually operate, which is problematic in highly regulated businesses.
One potential solution to address this risk is to develop the model's sophistication gradually while making a deliberate choice to move forward at each level.
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It is easy to believe that a machine-learning model would function properly without supervision once it has been trained. Managers need to periodically restrict models using new data sets since the environment in which the model is functioning is continuously changing.
In the past ten years, machine learning has emerged as one of the most fascinating technological advancements with practical business applications.
Machine learning holds the potential to transform how individuals use technology and even entire industries when paired with big data technology and the enormous processing power made available by the public cloud. But even while machine learning technology is promising, it needs to be carefully planned in order to prevent unintentional biases.
The effectiveness of the decisions that computers make must be taken into account by those who develop the machine-learning models that will shape the future. By creating models with a biased "mind of their own," managers run the danger of negating the potential advantages of machine learning.