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We are proud to introduce DEI Hiring Propeller, an AI based solution powered by one of the industry’s deepest datasets and state-of-art models, enabling employers to build a diverse, qualified, and winning workforce within a single platform. It is a predictive insights dashboard that detects bias and provides suggestions based on the job description, helping recruiters and hiring managers make quick & smart decisions to optimize the job posting and their hiring strategy before publishing the job to the market. Let’s see how it works! This is where the recruiters would start with, the page to create a job posting. Let’s say we are hiring for a senior python developer position in New York City. After filling in the job title, location, overview, responsibility, and qualification, the DEI Hiring Propeller would send the extracted job information to the models and show the predicted hiring results and suggestions on the next page. On the suggestion page, we can choose the protected attribute we want to research in the drop-down, for example, gender. The cards on the top are the predicted Time to Hire and Number of applications by gender, which are 2 of the most important hiring metrics for employers. The recruiters could get a sense of how many days it would take to hire a female or a male candidate, as well as how many applications they would receive from both groups. It also provides Job board recommendations for each gender and shows the percentages of successful candidates from each job board for the given job. The title recommendations card will suggest similar titles to the given job while maximizing the number of candidates from each gender segment. (For example, if we change the title python developer to software developer, the number of female candidates would increase by 27.1%.) Based on the content of the job description, DEI Hiring Propeller also calculates the skills and competencies bias score on gender, represents the ratio of how commonly a skill or competency was found in the profile of men or women amongst successful candidates for similar roles. Additionally, the seasonal trends of applicants for similar jobs are presented, showing the comparison of the application intensity between different gender segments by month. With these predictions and suggestions, recruiters can think and take action accordingly before posting the job publicly, thus helping them better allocate resources and plan financially to get a diverse candidate pool.