The rise of AI in recruitment promised increased efficiency by swiftly scanning resumes and helping recruiters focus on top candidates. However, this innovation also brought challenges, particularly the risk of bias in artificial intelligence influencing hiring decisions. For instance, Amazon’s automated system, designed to streamline recruitment, was found to be biased against female candidates and was eventually discarded.
In this article, we’ll explore what AI bias is, provide bias in artificial intelligence examples, and discuss strategies for eliminating this bias from the recruitment process.
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What is Bias in Artificial Intelligence?
Bias in artificial intelligence happens when AI systems produce unfair or skewed results. This can occur because the algorithms used to create the AI have built-in biases or because the data used to train the AI contains prejudices. In simpler terms, if the data or assumptions behind the AI are biased, the AI’s decisions or outputs can also be biased, leading to unfair or inaccurate results.
What are the Types of Bias in Artificial Intelligence?
Before looking at bias in artificial intelligence examples, let’s explore the types of bias that can occur in AI systems:
Cognitive Biases
Cognitive biases are unconscious errors in thinking that influence how people make judgments and decisions. These biases arise because our brains tend to simplify complex information processing. There are more than 180 types of cognitive biases recognized by psychologists. In AI, these biases can manifest when designers unintentionally incorporate their own biases into the algorithms or when the training data reflects these biases. As a result, the AI may replicate or even amplify these biases in its outputs.
Lack of Complete Data
When AI systems are trained on incomplete or unrepresentative data, the resulting models can be biased. If the data used is not diverse or comprehensive, it may not accurately reflect the real-world population. For instance, if a study’s data primarily comes from a specific group, such as undergraduate students, it may not generalize well to the broader population. This lack of complete data can lead to skewed or unfair AI outcomes.
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Bias in Artificial Intelligence Examples
Here are some bias in artificial intelligence examples that highlight how AI systems can produce unfair outcomes:
Amazon’s Biased Recruiting Tool
Amazon developed an AI system in 2014 to automate resume screening. By 2015, it became clear that the system was biased against women. Trained on data from a male-dominated industry, the AI unfairly favored male candidates. For example, it penalized resumes mentioning “women’s,” such as “women’s chess club captain.” Amazon discontinued the system due to its inherent bias.
Racial Bias in Healthcare Risk Algorithms
A healthcare algorithm designed to predict which patients need extra care showed racial bias. It favored white patients because it used healthcare spending as a proxy for need, a metric that is closely linked to income and race. This led to inaccurate predictions and unfair treatment of black patients.
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Bias in Facebook Ads
In 2019, Facebook allowed advertisers to target ads based on gender, race, and religion. This resulted in biased job ads: women saw ads for nursing and secretarial jobs, while men, particularly from minority backgrounds, saw ads for janitorial and taxi driver roles. Facebook has since banned such targeting to prevent discrimination.
Eliminating Selected Accents in Call Centers
Sanasa, a startup, created an AI tool to adjust call center workers’ accents to a “neutral” American accent. The goal was to make these workers sound more familiar to American customers and reduce misunderstandings. However, this approach does not address the underlying issue of bias. Instead, it may worsen discrimination by reinforcing the idea that a “white American” accent is the ideal, disadvantaging workers who don’t use the technology.
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How to Fix Bias in Artificial Intelligence?
Apart from these bias in Artificial Intelligence examples, here are some essential steps to fix bias in AI systems:
Assess the Algorithm and Data
Start by closely examining the data used to train your AI system. Make sure the data is diverse and represents all relevant groups to avoid biases. Check if the data is balanced and covers various subpopulations fairly. Additionally, regularly review how the AI performs over time, as its results can change with new data or updates. Continuous monitoring helps catch and fix any new biases that may arise.
Establish a Debiasing Strategy
Create a clear plan to tackle bias that includes technical, operational, and organizational actions. On the technical side, use tools to find and address biases in the data and algorithms. Operationally, improve how you collect and evaluate data by involving internal reviews and external audits. Organize your team to be transparent about how biases are managed and reported, which promotes fairness and accountability.
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Enhance Human-Driven Processes
When you spot biases, use this information to refine how models are built and evaluated. Recognize any biases that have been overlooked and understand why they occur. Make changes to your training methods and organizational processes to better address these issues. By continuously improving your approach, you can reduce bias and ensure more accurate and fair AI outcomes.
Decide on Decision-Making Approaches
Determine which decisions can be handled by AI and which require human judgment. While AI can efficiently process data, some situations need human insight to understand context and make ethical choices. Ensure that decisions involving sensitive matters have human oversight to maintain fairness and accountability.
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Adopt a Multidisciplinary Approach
Bring together experts from various fields, including ethics, social sciences, and specific industry areas, to address bias in AI. A team with diverse expertise will provide a broader perspective on how bias can affect different groups and applications. This collaborative approach helps create more effective strategies for identifying and reducing bias.
Diversify Your Organization
Build a diverse team to improve how you detect and handle biases. People from different backgrounds and experiences can spot issues that others might miss. By including a variety of voices and perspectives, you can create AI systems that are fairer and more inclusive, ensuring that they better serve all user groups.
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Conclusion
In conclusion, addressing bias in artificial intelligence is crucial for ensuring that AI systems are fair and equitable. The bias in artificial intelligence examples discussed highlight how it can manifest in various applications, from call centers to recruiting tools and healthcare algorithms. To effectively combat these biases, it is essential to carefully assess your data and algorithms, implement a comprehensive debiasing strategy, and continually monitor and refine your processes. Additionally, fostering a diverse team and adopting a multidisciplinary approach can significantly enhance your ability to identify and mitigate biases.
For organizations seeking to improve their hiring practices and overall fairness, BarRaiser sets the standard with its Structured interview processes, detailed Interviewer training, and commitment to global diversity initiatives. BarRaiser’s approach helps create a workplace culture that values diversity and equal opportunities, providing a reliable solution for those dedicated to fair and responsible AI development.