Bias in AI: Examples of AI Bias & How to Avoid it
As AI becomes more and more ubiquitous in our lives, it’s important to be aware of the biases that can arise from AI models and algorithms. If a machine learning algorithm is trained on biased data, it will be biased too. AI bias can have a negative impact on the company using a biased model as well as on the consumers who are utilizing the products or services offered by said company.
What is AI Bias?
AI bias is a term used to describe the ways in which artificial intelligence systems can be biased against certain groups of people. This can be due to a number of factors, such as the data used to train the AI system, the people who design and operate the AI system, or model drift.
Types of AI Bias
AI bias can come in many forms, including algorithmic, data-related, and human-induced biases. Let’s explore some types of bias in more detail:
Algorithmic bias occurs when the AI algorithm has been programmed with certain biases, either intentionally or unintentionally. This can lead to inaccurate outcomes that are based on flawed logic or prejudiced assumptions. For example, in facial recognition systems, algorithmic bias can lead to incorrect racial classification of people’s faces.
Data-related bias occurs when the data used to train the AI model is of poor quality or is incomplete. This can lead to results that are skewed towards certain groups, as AI models are only as good as the data they’re trained on. An example of this could be an automated loan decision system that incorporates demographic information, such as gender and race, to make decisions. If the data set used to train the model is unrepresentative of the population, it could lead to unfair decisions being made.
Human-induced bias occurs when humans introduce their own biases into the AI system. This can happen in a variety of ways, such as through manual data input or by designing AI systems that rely on human input. An example of this could be a recruiting system that takes into account gender-based language cues when selecting job applicants.
AI Bias Examples
AI bias has a real-world effect and can cause immense harm when biased AI models are deployed, both to consumers and the company using the model. There have been many high-profile cases of companies facing backlash after using biased AI models, including Microsoft, Amazon, and the U.S. healthcare system.
Microsoft’s AI Chatbot
Microsoft’s AI chatbot, Tay, was designed to interact with people in a natural language on the popular social platform Twitter. Unfortunately, the machine learning model powering Tay was quickly corrupted by malicious users and it began sending out offensive tweets that were sexist and racist. This incident was a perfect example of how quickly AI bias can become embedded into an algorithm if not monitored.
Amazon AI Hiring Bias
Amazon has come under fire for using biased artificial intelligence to screen job applicants after it was revealed that the models were biased against women. Amazon’s AI recruiting tool was found to be favoring male candidates, according to researchers from the University of Washington. The study found that the AI program was more likely to recommend male candidates than female candidates, even when the resumes were identical. This happened because the machine was relying on past resumes submitted to the company, which were influenced by the heavily male-dominated industry.
AI Racial Bias in Healthcare
AI’s increasing popularity has impacted the healthcare system, as algorithms assist doctors in ways such as predicting diseases and determining which patients need care urgently. An astonishing study published in the Science Journal in 2019, found that an algorithm used to dictate the care of more than 100 million people was biased against black patients. As a result of long-term systemic racism within the healthcare system, the data fed to the algorithm was biased against black patients, who had to be significantly sicker than white patients in order for the algorithm to recommend care.
The Impact of AI Bias
As AI becomes more prevalent in our everyday lives, it is important to consider the potential impacts that AI bias can have on society. Unfair decision-making based on biased data sets can lead to an unequal playing field which could disproportionately impact certain groups of people. Additionally, if a company’s AI system is found to be exhibiting bias, this could lead to a lack of trust in the system and, ultimately, in the company itself.
Ultimately, it is up to organizations to ensure that their AI systems are fair and equitable. By understanding the different types of AI bias and taking steps to mitigate them, organizations can help ensure that their decisions are based on accurate data and ethical principles. In doing so, they can help create a more equitable and just society.
How to Avoid AI Bias
Organizations can take a number of steps to avoid AI bias. Some ways of avoiding AI bias include reviewing data sets for accuracy, model monitoring, regularly testing algorithms, and making adjustments as necessary. Companies should also invest in training employees on how to identify and mitigate bias in AI systems. By following these steps, organizations can help ensure that their AI systems are unbiased and make decisions based on reliable data. Reliable AI algorithms and models will help create consumer trust in organizations and ultimately benefit both society and the companies who use AI in their decision-making processes.