What Is AI Bias?

AI bias is an erroneous assumption in the AI model's problem-solving process that influences or limits the output, resulting in skewed results that don't best reflect reality. All AI systems make decisions based on their training data. Subtle gaps or missing information in the training data set are less obvious and easily missed by developers, resulting in AI bias. For example, an AI service for medical diagnosis incorrectly identifies a specific disease in a particular demographic because the training data did not have the disease information for patients of that demographic.

AI bias reflects real-world bias and the lack of complete representation that data often has. It reflects human bias and reduces the accuracy and efficiency of artificial intelligence systems.

Poor quality data

Bias could come from the fact that the data itself is biased. This could be the case if the data that researchers collect isn’t completely representative or has gaps in its information.

Improperly selected data

Sometimes, humans must go through data and label it to build useful training datasets. During this process, humans may unconsciously introduce their biases into the dataset. For example, someone may subconsciously label an average product review as negative if they dislike the item or brand.

Outdated data

As society progresses, ideas, views, and understandings begin to shift. If AI systems train on historical data from a few years ago, they may produce responses that are now inaccurate.

Several forms of machine learning bias exist, each of which can reduce the efficacy of AI systems. Here are the most common human biases that will impact artificial intelligence models.

Algorithm bias

Algorithm bias is a misconfiguration of an AI system's model architecture. Incorrectly configured models may disproportionately miscalculate responses. It can increase the prevalence of a specific response type, giving the algorithm bias toward a particular answer.

For example, a video recommendation AI engine boosts specific content while undervaluing other content based on an algorithm that favors engagement. It recommends popular content over videos that customers may enjoy based on their interests.

Measurement bias

Measurement machine learning bias stems from errors in the data collection process. If researchers collect data without using the best data collection practices, the training data may not be accurate or representative, resulting in biases.

For example, when testing a mobile app available for different mobile platforms, if you experiment only with a subset of mobile users who use a single platform, the results cannot be truly reflective and introduce measurement bias.

Exclusion bias

Exclusion bias occurs when researchers who develop an AI system leave out important information from their training datasets. The AI system cannot produce responses relating to the missing data. For example, exclusion bias occurs if voice recognition software is trained on only accents from certain areas in a country. The software cannot recognize and understand accents from other locations.

Recall bias

Recall bias occurs when a model's ability to identify a specific class correctly is influenced by the uneven representation or attention given to that class during the training process. This type of bias is particularly noticeable in classification tasks. For example, in a medical diagnosis application, if a dataset predominantly contains examples of one disease, the machine learning model might perform well in identifying that disease but poorly in detecting others. It has a high recall for the dominant disease but poor overall performance across all potential conditions.

Selection bias

Selection AI bias occurs when the overall dataset scientists collect does not represent the general population directly. This form of bias occurs when researchers do not use random sampling methods or when their biases mean they forget to include a diverse range of data points.

An example is when facial recognition data is not trained on all demographics. A lack of representation in data reduces an AI model’s ability to recognize underrepresented groups.

Label bias

Label AI bias is when a data annotator unconsciously introduces bias into the data labeling process. Especially when the data that an annotator is working with can be subjective, their inherent biases will influence their perspective.

A great example is in social media listening and NLP sentiment analysis. Human biases may cause them to perceive an interaction differently, introducing bias into their datasets and AI algorithms.

You can use several strategies to reduce the likelihood of AI bias and enhance artificial intelligence model accuracy.

Selecting the right model

Different machine learning models are more or less susceptible to certain forms of bias in AI. As a model becomes more complex, it manages more data, making it more likely to have bias. Selecting the right-sized AI model for your use case helps create a fair system free from biased data.

Testing your data

Statistical analysis can help identify inconsistencies or a lack of representation in the data before training. A diverse annotation team increases the likelihood that your data is fully representative. You can avoid blind spots or missing data points.

While we cannot fully prevent bias from creeping into AI technologies, we can use strategies to resolve existing AI bias.

Continuous monitoring

Continuous monitoring is a method of resolving AI bias by regularly evaluating the output of AI tools. Over time, even the best training data becomes outdated and irrelevant. How users interact with the system may also introduce new or previously unknown issues. Feeding the model with updated data using techniques like retrieval augmented generations(RAG) also helps resolve bias.

Human in the loop output

Human-in-the-loop output is a system where an AI's output is reviewed manually by humans. A research and feedback team checks the validity and fairness of several responses generated by the AI system. While this can be time-consuming, it ensures that your AI model meets human-defined standards.

Counterfactual fairness

Counterfactual fairness is a method you can use to resolve bias after you have identified it within your AI. Let’s say that an HR AI technology that reviews CVs highlights the ‘top candidates’ based on a few similar characteristics. Once your team of researchers understands what these characteristics are, they can replace them with synonyms. If the model doesn’t then continue to favor the same CVs, you may have encountered a misconfiguration in your AI model. You can then review the AI model’s architecture to find the source of the error and then mitigate AI bias.

Explainable AI

Explainable AI is a way of coding natural human reasoning into AI models. Using explainable AI means that AI models also reveal the reasoning they used to get to a specific answer. You can review its chain of thought to find where bias enters the system.

Amazon SageMaker Clarify provides purpose-built tools to gain greater insights into your ML models and data based on metrics such as accuracy, robustness, toxicity, and bias to improve model quality and support responsible AI initiatives. It can help you understand why your AI model made a specific prediction and whether bias impacts this prediction during training or inference. With SageMaker Clarify, you can do the following:

• Detect bias in and help explain your model predictions.
• Identify types of bias in pre-training data.
• Identify types of bias in post-training data that can emerge during training or when your model is in production.

Using Amazon SageMaker Clarify, you can also evaluate large language models (LLMs) by creating automated or manual model evaluation jobs. Manual model evaluation jobs use human workers to bring human input to the process. They can be your company's employees or a group of subject-matter experts from your industry.

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