The Bias Within: How Amazon’s AI Recruitment Missed Great Candidates

Amazon, one of the biggest and most successful companies in the world, has been at the forefront of using Artificial Intelligence (AI) in its operations. The company uses AI to automate tasks, improve customer experience, and even enhance recruitment processes. However, Amazon’s AI recruitment system has come under fire for being biased and discriminatory against women. The system was found to have learned from data that was historically biased towards men, and this led to the exclusion of qualified female candidates from the recruitment process.

In this article, we’ll explore how Amazon’s AI recruitment learned to be biased and missed great candidates, the impact of the bias, how the error was spotted, and the steps taken to rectify the problem.

What was Amazon’s AI Recruitment System?

Amazon’s AI recruitment system was designed to streamline the recruitment process, reduce bias, and enhance the quality of candidates. The system uses machine learning algorithms to review resumes and job applications, and it ranks the candidates based on the requirements of the job. The system was trained on resumes and job applications submitted to Amazon over a 10-year period.

How Did the Bias Occur?

The bias within Amazon’s AI recruitment system occurred because of the data that was used to train the machine learning algorithms. The system was trained on resumes and job applications that were historically biased towards men. For instance, the system was more likely to rank resumes that contained phrases such as “captain of the chess club” or “member of the robotics team,” which are activities that are more commonly associated with men. As a result, qualified female candidates who didn’t have these phrases on their resumes were excluded from the recruitment process.

What Was the Impact of the Bias?

The bias within Amazon’s AI recruitment system had a significant impact on the recruitment process. The system excluded qualified female candidates from the recruitment process, which led to a gender imbalance within Amazon’s workforce. According to reports, the company’s technical workforce was made up of only 13% women in 2018. This gender imbalance had implications for the company’s culture, innovation, and bottom line.

How Was the Error Spotted?

The error within Amazon’s AI recruitment system was spotted by a team of Amazon engineers and data scientists. They discovered that the system was biased against female candidates and that it was learning from data that was historically biased towards men. The team identified the phrases and keywords that the system was using to rank resumes and job applications, and they found that these phrases were more commonly found on resumes submitted by men.

What Steps Were Taken to Rectify the Problem?

Once the bias within Amazon’s AI recruitment system was identified, the company took several steps to rectify the problem. These steps included:

Removing gendered language from the system: Amazon removed gendered language from the machine learning algorithms that were used to rank resumes and job applications. This included removing phrases such as “captain of the chess club” or “member of the robotics team.”

Conducting a thorough review of the system: Amazon conducted a thorough review of the AI recruitment system to identify other biases that might be present. The company also reviewed the training data to ensure that it was more diverse and representative of the population.

Introducing human oversight: Amazon introduced human oversight into the recruitment process to ensure that the system was not excluding qualified candidates. The AI tool that originally had the issue has reportedly been completely discontinued since.

Conclusion

The bias within Amazon’s AI recruitment system highlights the importance of ensuring that machine learning algorithms are free from discrimination and bias. The case of Amazon’s AI recruitment system underscores the fact that AI systems are only as unbiased as the data they are trained on. If the data is biased, the system will be biased too.

However, the problem is not limited to Amazon alone. Several other companies have reported bias in their AI systems. For instance, facial recognition technology has been found to be less accurate in identifying people with darker skin tones. The problem of bias within AI systems is a serious one, and it requires a concerted effort from all stakeholders to address it.

The case of Amazon’s AI recruitment system also highlights the importance of human oversight in the development and deployment of AI systems. While AI has the potential to automate and streamline many tasks, it cannot replace human judgment entirely. Human oversight is essential to ensure that AI systems are functioning as intended and that they are not perpetuating bias and discrimination.

In conclusion, the case of Amazon’s AI recruitment system serves as a cautionary tale for companies developing AI systems. It is crucial that companies take proactive measures to ensure that their systems are free from bias and discrimination. Failure to do so can have serious consequences for the company’s culture, innovation, and bottom line.