Our Explainable AI Approach
At Interviewer.AI, we build Explainable AI [1] to help teams identify desirable talents
in their talent acquisition processes. Having an Explainable AI framework allows us
to provide insights on the key performance factors of candidates and minimize
the risk of prejudice judgements that may arise from black-box AI approaches.
Our Explainable AI assesses interview candidates’ soft skills through evaluation of
key success factors identified by I/O psychology heuristics and industry
knowledge [2],[3] . From asynchronous video interview data provided by candidates,
visual, audio and textual information can be extracted for computer vision,
natural language processing, and audio analysis tasks for our AI to perform. These
observable pieces of information are evaluated by our Narrow AIs [4] that were
built to specifically measure these observable features of a candidate such as
eye-contact, emotional state, energy level, etc. These observable features are
then provided as data points to evaluate the candidates’ key success factors
through a combination of I/O psychology heuristics and machine learning to arrive
at the final interview score of the candidate.
AI in Recruiting
Our AI models are trained to be blind towards age, genders, ethnicity for a fair and objective assessment approach. Our model training datasets are global and purposefully curated to avoid any undesirable historical biases in hiring [5],[6],[7] . We ensure that there are fair representation of age-groups, genders, and ethnicity for each label in our supervised training datasets.
When augmented with traditional talent acquisition human resource processes, our Explainable AI brings the value of objectiveness, scalability, and explainability to the professional human teams to boost the effectiveness and efficiency of the department.
Addressing Common Concerns Regarding Our AI
References
1Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. Z. (2019). XAI—Explainable artificial intelligence. Science Robotics, 4(37). 2 Robles, M. M. (2012). Executive perceptions of the top 10 soft skills needed in today’s workplace. Business communication quarterly, 75(4), 453-465. 3 Kačamakovic, M. K., & Lokaj, A. S. (2021). Requirements of Organization for Soft Skills as an Influencing Factor of Their Success. Academic Journal of Interdisciplinary Studies, 10(1), 295-295. 4 Dickson, B. (2017, May 12). What is Narrow, General and Super Artificial Intelligence. TechTalks. https://bdtechtalks.com/2017/05/12/what-is-narrow- general-and-super-artificial-intelligence/. 5 Correll, S. J., & Benard, S. (2006). Gender and racial bias in hiring. Memorandum report for University of Pennsylvania. 6 Petersen, T., & Togstad, T. (2006). Getting the offer: Sex discrimination in hiring. Research in Social Stratification and Mobility, 24(3), 239-257.. 7 Wilson, M., Parker, P., & Kan, J. (2007). Age biases in employment: Impact of talent shortages and age on hiring. University of Auckland Business Review, 9(1). Dickson, B. (2017, May 12).
