A Non-Biased Solution for Resume Screening

Introduction

When analyzing candidate resumes, it's incredibly challenging to eliminate biases from the process, even when we are actively aware of them. Unconscious or implicit bias refers to the mental processes that lead individuals to reinforce stereotypes, often without realizing it. In the resume screening process, these biases can manifest in various ways:

• Name Bias: Judging candidates based on the perceived ethnicity or gender of their name.

• Gender Bias: Making assumptions about a candidate's abilities or fit based on their gender.

• Race and Ethnicity Bias: Allowing stereotypes about race or ethnicity to influence hiring decisions.

• Age Bias: Preferring candidates of a certain age group, regardless of their qualifications.

The Brainner Solution

At Brainner, we recognized this problem and developed a solution. Brainner is a resume screening software that follows the specific hiring criteria set by recruiters. Using AI, it analyzes candidates' resumes in real-time, extracting information on whether they meet each criterion. Brainner then presents this information in a dynamic table, enabling data-driven analysis and decision-making.

To address biased decision-making, Brainner allows recruiters to analyze candidate rankings without seeing names, emails, ages, or genders. This feature ensures that the evaluation is based solely on relevant variables like current job, current company, and whether they meet the mandatory or preferred criteria for the role.

Tackling AI Bias

There's been much discussion about the potential for AI to perpetuate biases, as it can be trained on historical data that includes these biases. For example, if past hiring decisions were influenced by biases related to gender, age, faith, or race, an AI system might replicate these patterns, inadvertently excluding qualified candidates from underrepresented backgrounds.

At Brainner, we tackled this issue by using our AI model differently. Instead of making decisions, our AI acts as an information analyzer and extractor. This approach ensures that the AI provides objective data, while human recruiters make the final decisions based on a fair and unbiased analysis.

Conclusion

Brainner is designed to promote fair hiring practices by eliminating biases from the resume screening process. By focusing on objective criteria and allowing recruiters to evaluate candidates without seeing potentially biased information, Brainner ensures a more equitable hiring process. This innovative approach not only improves the quality of hires but also supports a diverse and inclusive workplace.