As artificial intelligence (AI) continues to evolve, ethical implications often accompany its technological progression. It is becoming increasingly evident that bias is not only pervasive but is often amplified within these intricate systems. Unveiling the many layers of AI bias is not just an academic pursuit but a way to understand and comprehend the ethical implications woven into our digital society.

We will walk you through the nature of AI bias, discuss different types of AI bias with examples and suggest what you can do to help mitigate AI bias. We hope this information will help add to your awareness of AI’s societal impact and ethical responsibilities.

What Is AI Bias?

Bias in AI refers to the systematic and unfair preferences, prejudices or inaccuracies ingrained within the design, development and deployment of AI systems. AI bias is also commonly referred to as machine learning bias or algorithm bias. Since humans are the original creators of AI models, they can consciously or unconsciously integrate their societal attitudes into the AI systems they program.

While bias in AI machines can form over time as they continue to store large amounts of data, bias can also be perpetuated in AI’s initial stages of formation and training. However, if the initial training data is biased, AI applications can learn skewed patterns and produce biased outputs. Understanding these types of AI biases can help you identify them and follow up with solutions.

Types of AI Bias

Bias in AI manifests in various forms, each with its unique implications and challenges. These types of AI bias include algorithmic bias, sample bias, prejudice bias, measurement bias, exclusion bias, selection bias and recall bias. Understanding these types of bias is essential for mitigating their effects and promoting fairness, transparency and accountability in AI applications.

Algorithm Bias

If AI misunderstands a question because it is not posed correctly, its response is likely to be inaccurate or biased. This bias can result in certain groups or individuals being unfairly advantaged or disadvantaged by the algorithm.

Sample Bias

A large amount of quantitative data is needed upfront for these machines to be capable of responding fairly. Sample bias happens when the data used to train or evaluate a model is not substantial enough. Insufficient data can lead to skewed or inaccurate results, as the model’s understanding of the people and circumstances may be incomplete or biased toward certain demographics or characteristics.

Prejudice Bias

Prejudice bias stems from the preconceived notions or biases of individuals involved in creating, training or using an algorithm. These biases can influence decision-making and lead to unfair or discriminatory outcomes, particularly when they align with societal stereotypes or prejudices.

Measurement Bias

Measurement bias arises when initial data collection, recording or interpretation is incomplete.  Data inaccuracies can result in misleading or incorrect results, as the underlying data may not accurately reflect the true values or attributes being measured.

Exclusion Bias

Exclusion bias occurs when certain information, groups or individuals are excluded from the data used to train or evaluate an algorithm. Data exclusion can lead to disparities in outcomes, as the algorithm may not adequately account for the experiences or characteristics of excluded populations and circumstances.

Selection Bias

Selection bias happens when the process of selecting or collecting data systematically favors certain groups of people or characteristics over others. This can distort or skew the dataset, potentially leading to inaccurate or misleading results when the algorithm is used.

Recall Bias

Recall bias refers to the distortion or inaccuracy in the recollection of past events or experiences, which can affect the quality and reliability of data used in algorithmic decision-making. This bias can arise from various factors, such as memory limitations, cognitive biases or external influences, and may lead to flawed conclusions or predictions. 

AI Bias Examples 

Many examples of AI bias within the real world have impacted AI machine learning. A famous example of AI bias within the court system is their algorithm called “Correctional Offender Management Profiling for Alternative Sanctions” (COMPAS). This technology is used in courtrooms across the U.S. to predict whether or not a defendant will become a repeat offender. The result is that the system generated twice as many false positives for Black defendants as white defendants. 

Aside from this, other AI bias examples have occurred, especially within the healthcare industry. Many discrepancies exist between healthcare systems that favor medical care for white individuals more than Black individuals and those who lack access to sufficient healthcare. AI bias in healthcare can significantly skew the system and lead to bias occurring many times again.  

What Can We Do About AI Bias? 

While it is nearly impossible to completely eliminate bias in AI systems, some actions can be taken to prevent bias from occurring. Diversifying the data sets might help reduce AI bias. However, bias will continue to occur. Tech companies such as Google and OpenAI report that they are working on specific training models that mitigate bias. 

Overall, eliminating AI bias involves human interaction to check the outputs of such AI models. As this process becomes refined over time, we will move closer to mitigating AI bias risk. 

From a user perspective, it is important to look out for AI biases in the responses that these machines provide. It is suggested that individuals engage in fact-based conversations with AI machines to help identify when they produce biased results. 

Stay up to date with the latest advancements in AI systems by following Robert F. Smith on LinkedIn.