Machine learning or ML bias is a known risk that is hard to counteract. Some of them in real-world scenarios have been controversial and significant. For example, Amazon’s recruiting algorithm. In 2018, research helped identify that Amazon showed their AI facial recognition systems had a bias against women and skin, and gender-biased! Similarly, the 21st-century COMPAS- Correctional Offender Management Profiling for Alternative Sanctions used to predict criminal recurrences had a bias against people of color. Hence, let us study what is meant by bias in ML or AI.
Bias in ML:
Artificial intelligence systems use Machine Learning to train and teach algorithms on a dataset to do tasks manually time-consuming and inefficient. For example, the assembly lines at a car manufacturer depend on how accurately and quickly the parts are put together without errors.
Machine learning is a subset of AI or artificial intelligence and depends on the objectivity, quality, training data size, etc., used to teach and train the algorithm. Computer science’s “garbage in, garbage out” concept means that the output quality is dependent on the input quality. When the data is incomplete or faulty, inaccurate predictions result. Also, individuals do not adequately train the algorithm, leading to real-world prejudices and biases, as discussed above.
ML Bias Types:
Bias can be introduced or unintentional in the ML system. Here are the more well-known types of bias in machine learning:
- Algorithm bias occurs when an error or issue is present in the calculating algorithm powering the computations in a machine learning system.
- Prejudice bias occurs when the training data training the system’s algorithm has biases and prejudices, faulty assumptions, stereotypes, etc., injecting them into the real-world situations from the ML system. For example, stereotypes like medical professionals mean male doctors, people of color commit more crimes, and certain minority communities are poorly educated, and so on.
- Sample bias happens if problems occur in the training data of the ML model. The data set is not large enough to teach and train the algorithm, introducing a bias in the ML system. For example, a data set training the algorithm may have data that features female teachers, female nurses, etc. Then the algorithm is biased and assumes all nurses and teachers are females.
- Exclusion bias happens when crucial data points are omitted. For example, the data modellers leave out a significant data point due to an error.
- Measurement bias happens due to underlying issues in data accuracy, its assessment or measurement, etc. For example, a system that uses smiling faces when training the algorithm in a workplace environment could be biased against non-smiling workers since it is trained to assume workers are happy and smiling.
Variance Vs. Bias:
While we have some idea of bias, Data scientists and people involved in teaching, training, or building the ML models must also consider variance.
Variance is also an error in ML that results in the training data having wrong assumptions. Variance is the data sets that have legitimate fluctuation and reactions to the real data sets that are used. The noise or fluctuations cause the readings to be off the mark, and the trained algorithm can accumulate the noise to provide inaccuracies in the trained model.
A certain level of noise helps reduce bias. Thus, if the populated data set consists of a large variety of data, the variance noise introduced in small amounts can cancel out its biases. Therefore, ML works on models using a trade-off between bias and variance to help develop an accurate model with minimum errors.
Data underfitting Vs. Overfitting:
Overfitting of data in data science is when the statistical model fits the training data exactly. In such situations, the trained algorithm on such a data set is incapable of accurate performances when using unseen data, thus defeating its very purpose. Overfitting can be identified by high variance levels and low error rates. Hence, overfitting also means that the algorithm has a poor generalization to unseen data while exhibiting good performances against the training data set.
Underfitting, on the other hand, shows low variance levels and higher biases. This happens when the training data set is insufficient. Hence under fitted models are easier to spot than the overfitted models.
Effects of Bias:
ML or AI bias is also called algorithm bias. It is a phenomenon when the Machine Learning
algorithm produces inaccurate or factually inconsistent results in real-world situations. The ML system algorithm can be systemically prejudiced in the ML process where erroneous assumptions are used in training the algorithm.
Some types of cognitive biases in the algorithms cause wrong stereotyping, selective perceptions, priming, a bandwagon effect, confirmation biases, and more. Depending on how the algorithm is trained and used, the machine learning system exhibits biases that lead to poor customer service experiences, sales reductions, actions against a class of persons, and other potentially serious conditions that can negatively affect a business organization.
How to avoid data bias errors in ML?
The ongoing process of data bias in Machine Learning projects means you need to take certain cautions. Here’s a simple list of corrective measures to follow:
- Research well regarding the users, potential outliers, and general use-cases well in advance.
- Make sure your team consists of diverse data labellers and data scientists.
- Use data diversity to your advantage where possible by using a combination of multiple source inputs.
- Create data labelling standards that are ideal, consistent, and accurately measure the team’s data annotations for your task.
- Use multi-pass annotations for data bias in the project, sentiment analysis, intent recognition, and content moderation.
- Have experts with domain expertise review the annotated and collected data.
- Review and analyze data regularly, keeping track of problem areas, issues, and errors. Ensure you analyze the data points before the decision-making process.
- Use bias testing regularly in the development cycle with tools from Microsoft, IBM, Google, etc.
As you can see, identifying bias, variance, errors, etc., in a machine learning system and its algorithm is a job for professionals. It is also important to train yourself on real-life and industry-relevant models by doing an artificial intelligence course that trains you well, especially in the practical aspects of the job. You can hit the ground running and build a career that pays and advances swiftly with such skills.
The Bottom Line
It’s crucial in a data project that you be aware of and take preventive measures against the potential biases in the Machine Learning domain. Having the right databases and systems in place and ensuring that labelling, data collection, and biases allow for timely intervention when the algorithm shows an error, bias, or implementation issues.
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