AI Governance in HiringBranch

HiringBranch is committed to the trustworthiness of its products. We take our clients’ need to be reassured about the impact, ethics, and accountability of our technology very seriously. The HiringBranch AI Governance Framework has been developed based on guidance from the Government of Canada’s Algorithmic Impact Assessment (AIA), the Institute of Corporate Directors (ICD) and NuEnergy.ai.

Our team is educated about the principles of AI Governance and is trained to identify the risks, ask the right questions, and seek solutions.We keep close track of the AI-related legislation around the world and comply with standards like the GDPR,PIPEDA or PIPA. We actively think ahead and address AI Governance as an integral part of our product development pipeline.

The Five pillars of HiringBranch AI 
Governance are:

Quality - we pride ourselves on producing reliable communication assessments that predict the ability of candidates to perform in their future work environment. To that end, we constantly monitor and validate our assessments performance. Contact us for information on our assessment methodology and validation procedures.
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Security - the security of our assessments is a top priority for us. Several security layers and different security measures are integrated into our system to ensure the reliability of our results. Please contact us for detailed documents on security procedures.
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Privacy - all candidate data is private and confidential. Candidate data can only be accessed by authorized users using the latest security access policies. HiringBranch complies with privacy and confidentiality policies including GDPR and PIPEDA. The Privacy Policy is here.

Fairness - our assessments treat all people fairly and do not discriminate against candidates based on their gender, race, sexual orientation, age, geographical region, nativity in the target languages, etc.

Transparency & Accountability - HiringBranch AI is based on rules-based systems. We produce easy to understand reports and keep an audit trail of the decisions it makes. The system’s decisions are constantly inspected by human evaluators, and in the rare cases where it is necessary, changes can be made. Contact us for more information.

As HiringBranch does not collect data about the candidates gender, race, sexual orientation, and nativity in the target language, we could only conduct fairness analyses based on obvious features that are either provided to us by our corporate clients (like the geographical region of the candidates) or that can be assigned to voice samples of random candidates by human annotators.

Fair and unbiased assessments are of utmost importance to us. We adhere to a zero-discrimination policy when assessing candidates. Our goal is to discover what a candidate can do, regardless of who they are. As previously mentioned, gender, race, sexual orientation, age, location and native language data are not collected in assessments.

In collaboration with an undisclosed customer, we set out to prove that biases don't belong in the workplace. Using sample population data provided by the customer, we cross referenced gender and native tongue data with hired HiringBranch candidates' four-in-one assessment scores.

Nativity in the target language

No significant difference was noted for the final score of native vs. non-native speakers of English

Gender

No significant difference was noted for the final score of male vs. female candidates

ResultsWe are delighted (but not surprised) to see that there is no significant difference in the assessment of scores of native versus non-native speakers of English or in male versus female candidates. We don't stop there, but continue to check for bias in our assessments on an ongoing basis.