Can Synthetic Data Eliminate Bias in AI Recruitment ?

With the development of Artificial intelligence (AI), it becomes more and more common for companies to use AI tools for recruitment to improve efficiency. Trained on data collected about previous or similar applicants, AI recruitment tools save time for recruiters during the screening process. Some studies showed that 99% of Fortune 500 companies had AI tools somewhere within their hiring plans.

However, critics argued that such AI tools can introduce bias as these AI systems are often trained on data collected about previous or similar applicants. For instance, Reuters reported that Amazon had created a recruitment algorithm that unintentionally tended to favor male applicants over female applicants for certain positions. To some extent, although AI recruitment tools can accelerate the recruitment process and reduce recruiters’ burden, they generate biases, which will affect the recruitment outcome. On the other hand, applicants’ data such as past experiences, skill sets, education background, etc., is indeed personal data. Companies should get consent from past applicants to use their data to train the AI model. Otherwise, these companies will face the risk of violating privacy-related regulations, which might end up with huge fines.

Synthetic data can eliminate biases while preserving privacy. Using the AI engine, better data is able to generate a more balanced, abundant synthetic dataset covering more candidate profiles, which can help to train the AI recruitment engine in a more holistic manner.

Currently, AI screening tool is widely used during the recruitment process. Through evaluating application materials such as resumes and cover letters, AI is able to recommend which candidates recruiters should contact first. However, most AI screening tools are trained on actual resumes of previous employees and cross-compare with job descriptions, which may lead to biased recruitment in the end. To eliminate biases, betterdata is able to offer differentiated synthetic dataset for different positions. Synthetic datasets will also be more balanced in the appropriate way, such as having a more balanced sex ratio, having more diverse races, etc., to eliminate biases generated during the hiring process. In addition, using synthetic data to train the model would not encounter privacy issues, as companies do not need to use real applicants’ personal information.

Another widely used AI recruitment tool is a recruitment bot or chatbot, which can help to automate the process of reaching out to previous applicants and to find out whether an applicant meets a position’s basic requirements. These recruitment bots or chatbots are also used in the first round of interview. Since these chatbots are trained by data of similar applicants, it might generate biases. Using synthetic data generated by betterdata’s AI engine, it will introduce a more balanced trained dataset to reduce the biases while preserving privacy.

Online assessment tools are often used during the hiring process to test certain skills of applicants. These assessments are either designed to test applicants’ technical skills such as Excel, Python, and SQL, or test applicants’ behaviors or emotional intelligence. However, scenarios covered in the online assessment dataset might be limited. Synthetic data generated by betterdata’s AI engine is able to provide sufficient and diversified business scenarios to enrich the assessment content. For example, betterdata’s AI engine can generate up to 10,000 lines of tabular data, which could be useful for recruiters to test applicants’ Excel skills (but it would be quite challenging for applicants).

There is no one-fits-for-all synthetic dataset. Betterdata can help to improve the AI recruitment tools while preserving privacy by providing tailored synthetic datasets.

Read more use cases on Synthetic Data: https://www.betterdata.ai/blogs/use-cases-for-synthetic-data-across-industries

Contact us to learn more: https://www.betterdata.ai/company/contact-us