Tackling AI hiring bias

what is AI Bias

According to Ruzvizo  W (2023),AI bias occurs when algorithms, models, or datasets are created with built-in assumptions, leading to unfair or inaccurate outcomes. Around 65% of executives believe their organizations have data bias, which can result in skewed decision-making, discrimination, and legal issues. For instance, Workday Inc. is facing a lawsuit alleging bias in their AI systems against certain applicants. To avoid legal consequences and protect your company's reputation, it's crucial to actively address and reduce AI bias. This effort goes beyond fairness, impacting long-term success and credibility for businesses utilizing AI.

Most common AI Bias

AI biases can manifest as unfair or inaccurate depictions of individuals, often reflecting historical prejudices and societal norms. Recognizing these biases, whether hidden in datasets or influencing algorithm decisions, is crucial for developing more equitable and fair AI applications. Identifying and addressing these biases is a key step in promoting fairness in AI systems.

Tool and strategies for preventing AI Bias

Once you spot AI bias, the next step is to tackle and prevent it. This involves using the right tools and methods, such as gathering diverse datasets and employing thorough testing and validation. Each step improves your AI, making it fairer and more effective, ensuring it's a beneficial ally rather than a harmful force.

Identify and minimize Biases in AI models

Identifying and lessening biases in AI models is crucial for fairness, trust, and accuracy. Tools like AI Fairness 360 or What If can help detect biases and highlight questionable outcomes that might result in unfair decisions.

If biases are found in your business's algorithms, you can address them through fine-tuning. This means training the machine learning model to enhance its performance for a particular task. Allowing the model to adapt to diverse data helps minimize the impact of biases from the initial training data.

How AI Can Help Companies Avoid Bias in Hiring

According to Sullivan T(2022), AI has become essential in the hiring process, with 99% of Fortune 500 companies and 75% of all US companies using AI for hiring. Around 45% of global companies use AI to enhance recruitment and human resources.

AI tools assist in managing a large number of resumes, especially as the workforce becomes decentralized. They address the volume issue by processing resumes efficiently. Additionally, AI in hiring helps eliminate biases from resumes and job descriptions. For instance, it can modify or remove information indicating race or gender, promoting a fairer process.

Over 250 commercial AI-based HR tools exist, streamlining various processes. Modern AI hiring software can analyze diverse aspects of potential hires, such as word choice, speech patterns, facial expressions, social media presence, and personality traits.

However, a challenge arises as AI is still evolving and can unintentionally introduce biases. The same AI software designed to identify diverse candidates may also exhibit discriminatory behavior. Therefore, it's crucial for companies to be cautious and ensure that AI doesn't replace human biases with its own in the hiring process.

How to Avoid Bias in Hiring with AI

An HBS study found that 88% of employers reject qualified candidates because they don't precisely match job criteria in the description.

In the case of Amazon, their AI-based hiring tool, created to expedite the selection of resumes for technical roles, exhibited a bias favoring men over women.

To avoid AI bias in hiring, companies should aim to develop the least biased algorithm possible, according to Frida Polli, CEO of Pymetrics. Federal employment law emphasizes seeking the least biased alternative in algorithm design, ensuring equal outcomes for individuals of different genders and ethnic backgrounds. Although the law was created in 1968 without considering machine learning, using AI to find the least biased alternative today aligns with unbiased algorithmic solutions and demonstrates compliance.

Companies and HR leaders should implement a responsive plan when utilizing AI in hiring. The Federal Trade Commission's 2020 guidance outlines measures to reduce bias, emphasizing transparency, accuracy, fairness, disclosure, and accountability. This includes scrutinizing inputs and testing outcomes to address any disparate impact of AI hiring tools on protected classes.

References

Ruzvizo W( 2023)Tackling AI Bias (online)available at https://writer.com/blog/ai-bias/(accessed on 25 November 2023)

Sullivan T (2022)  How AI can help companies avoid Bias in hiring(online)available  at https://www.staffing.com/how-ai-can-help-companies-avoid-bias-in-hiring/(accessed on 25 November 2023)