Machine learning can never make interpretations on its own. It’s what we fed the AI/ML model during training, and its outcome is reliant on it. And what do we provide? — data wrapped in our opinions. As humans, we have opinions for every scenario, called prejudices, and building a model with that perception will give you biased results. Bias can be deliberate or unintentional. If it is intentional, it is primarily due to human intervention. Then we talk about AI’s trustworthiness. That’s not fair; it’s more like a blame game.
Every coin has two sides; if there is a benefit, there is also a drawback; ethically leveraging technology, including AI, is essential.
Understanding the various types of biases is the first step toward bias mitigation.
–Limited data bias: No doubt, small datasets can be used to make predictions; however, they must be pattern rich. The results may be incorrect if datasets have missing data points or are insufficient to train the model. The limited data set cannot depict the entire picture in such cases. This bias is also known as sample or selection bias.
–Algorithmic bias: Inappropriate algorithm selection may result in incorrect results for specific data points. It can also be due to technical issues within the core of an algorithm. It could be intentional or unintentional. If the results are erroneous, one must rethink the choice of an algorithm for the problem at hand.
–Gender bias: Social class, gender, nationality, and race infiltrate your model, introducing bias due to cultural stereotypes among those involved in the process. It can also be intentional or unintentional; however, it is ignored throughout the process. The outcomes of such models will always be inclined towards certain groups of people, resulting in unfair outcomes. As a result, researching a situation in such a case may yield inaccurate results. When processes, such as hiring or promotion practices, have a tendency to produce certain outcomes. Hiring or promoting from a talent pool deficient in women and ethnic minorities. Unless you change the whole dataset at the system level, you cannot prevent such bias.
–Measurement bias: Data collection that is inaccurate or biased will always result in partial results. It can occur even during data pre-processing or data labeling, where the same data point has been labeled multiple times.
–Exclusion bias: It is challenging to select an appropriate dataset with relevant features for training a model. Simultaneously, keeping those data points that contain essential information about the problem at hand determines a model’s success. Exclusion bias can occur if we overlook any important information simply by discarding it while selecting features.
–Confirmation bias: If you have a preconceived notion about a situation and firmly reside with it even if it’s wrong. So you look for information to back it up. Getting to the conclusion that your dataset is consistent as it supports your belief is confirmation bias. When used to train the model, such datasets will make you firmly hold false beliefs. It may lead to risky decision-making and overlook important information. Say doctors frequently have an early hunch about diagnosing a medical condition. This hunch can make it difficult to consider information that suggests a different diagnosis is more likely.
How to prevent bias in AI and ML models:
To mitigate bias, awareness is the first step. Second is the governance of data. The quality and quantity of data play a crucial role in building a successful AI/ML model and reassuring trust in such models. Humans need to be more cautious about playing their role ethically through the processes of AI/ML.
Practices that can be followed to reduce bias are
- The data collected should be reviewed by a domain expert and must combine inputs from multiple sources to maintain diversity and avoid bias due to limited data.
- Make clear guidelines for data labeling to be consistent.
- Analyze data regularly to keep track of errors. Be very careful while discarding or keeping any data points.
- To ensure that the results of machine learning systems do not reflect bias caused by algorithms or data sets, test and validate them.
We may not be able to eliminate biases because they can arise for various reasons; however, one must be mindful of implementing practices that reduce bias to some extent.