Benefit: Eliminate Bias And Increase Diversity
AI ignores demographic-related information such as the candidate’s name, race, sex, and age during screening and shortlisting.
How We Do It
Workplace diversity has become a competitive recruiting advantage. 67% of job seekers consider a company’s diversity to be an important factor when considering job offers. A significant barrier to increasing diversity is unconscious bias during the recruiting process. Research suggests that bias in the form of adverse impact (i.e., a substantially different rate of selection in hiring that disadvantages members of a race, sex or ethnic group) can happen during resume screening:
- Resumes with white-sounding names receive requests for interviews 50 percent more often than identical resumes with African American-sounding names
- Resumes with English-sounding names receive requests for interviews 40 percent more often than identical resumes with Chinese, Indian, or Pakistani names
- Resumes with male names receive requests for interviews 40 percent more often than similar resumes with female names
Ideal’s AI can help eliminate bias by excluding demographic data such as the candidate’s race, sex, and age and data correlated with demographics (e.g., names, zip codes) during its screening decision making.
If your organization collects demographic data for the race, sex, and ethnic group of your applicants, Ideal can further avoid bias during screening using two options:
- Ideal can perform backtests on its candidate grades for adverse impact (based on EEOC’s 4/5ths rule) using the employer’s historical hiring decisions.
- Ideal can monitor its candidate grades by assessing the percentages of the race, sex, and ethnic group of candidates and removing any identified bias to remain EEOC and OFCCP compliant.
By eliminating bias at both the candidate sourcing and screening level, Ideal’s AI helps remove an important barrier to diversity at the beginning of the recruiting cycle.