Canadian company uses machine learning to promote DEI in the hiring process

Toronto-based software company, Knockri has developed an AI-powered interview assessment tool to help companies reduce bias and bolster diversity, equity and inclusion (DEI) in the job hiring process.

Knockri’s interview assessment tool uses Natural Language Processing (NLP) to evaluate only the transcript of an interview, overlooking non-verbal cues, including facial expressions, body language or audio tonality. In addition, race, gender, age, ethnicity, accent, appearance, or sexual preference, reportedly, do not impact the interviewee’s score.

To achieve ‘objective scoring’, Faisal Ahmed, co-founder and chief technical officer (CTO) of Knockri, says that the company adopts a holistic and strategic approach in training their model, including constantly trying new and different data, training, and tests, that covers a wide range of representation in terms of race, ethnicity, gender, and accent, as well as job roles and choices. After training the model, the company conducts quality checks and adverse impacts analysis to analyze scoring patterns and ensure quality candidates do not fall through the cracks. 

Though working with clients with high volume hiring such as IBM, Novartis, Deloitte, and the Canadian Department of National Defence, Ahmed says their model is not able to analyze for every job in the world. “Once we have new customers, new geographies, new job roles or even new experience levels that we’re working with, we will wait to get an update on that, benchmark, retrain, and then push scores. We’re very transparent about this with our customers”.

To ensure that the data fed into the AI is not itself biased, Ahmed adds that the company avoids using data from past hiring practices, such as looking at resumes or successful hires from ten years ago, as they may have been recruiting using biased or discriminatory practices. Instead, Ahmed says, the AI model is driven by Industrial and Organizational (IO) psychology to focus purely on identifying the kind of behaviors or work activities needed for specific jobs. For example, if a customer service role requires empathy, the model will identify behaviors from the candidates’ past experiences and words that reflect that specific trait, Ahmed says.

He recommends that customers use Knockri at the beginning of the interview process when there is a reasonably high volume of applications, and the same experience, scoring criteria, and opportunities can be deployed for all candidates.

Ahmed says their technology seeks to help businesses lay a foundation for a fair and equitable assessment of  candidates, and is not meant to replace a human interviewer. Decisions made by Knockri are reviewed by a human being, and later stages of the interview process will inevitably involve human interviewers. 

“We’re not going to solve all your problems, but we’re going to set you on the right path”, concludes Ahmed.

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Ashee Pamma
Ashee Pamma
Ashee is a writer for ITWC. She completed her degree in Communication and Media Studies at Carleton University in Ottawa. She hopes to become a columnist after further studies in Journalism. You can email her at [email protected]

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