Humble AI (Knowles et al., 2023) argues for cautiousness in AI development and deployments through scepticism (accounting for limitations of statistical learning), curiosity (accounting for unexpected outcomes), and commitment (accounting for multifaceted values beyond performance). We present a real-world case study for humble AI in the domain of algorithmic hiring. Specifically, we evaluate virtual screening algorithms in a widely used hiring platform that matches candidates to job openings. There are several challenges in misrecognition and stereotyping in such contexts that are difficult to assess through standard fairness and trust frameworks; e.g., someone with a non-traditional background is less likely to rank highly. We demonstrate technical feasibility of how humble AI principles can be translated to practice through uncertainty quantification of ranks, entropy estimates, and a user experience that highlights algorithmic unknowns. We describe preliminary discussions with focus groups made up of recruiters. Future user studies seek to evaluate whether the higher cognitive load of a humble AI system fosters a climate of trust in its outcomes.

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Consulting agency, Gartner, has published a threatening report: by 2020, Artificial intelligence could replace skilled professionals in the fields of medicine, law or IT. But this technology won't only be useful for repetitive or time consuming tasks, it will also have its uses in skilled professions where the added value usually lies within human intelligence. Within these particular domains, training and learning require time and resources. With each new hire, the company must factor in a period of low employee productivity before hoping to see a rise in turnover. But by exploiting machine learning the company will see an acceleration in its processes, as only the first machine requires a learning period unlike the ones that will follow.