Even though very ofter discussions about AI and the future of work are focused on a jobless future and automation I see that the post-labor condition is also creating several other issues related to employer-employee relations and power dynamics.
Algorithmic Transparency in Recruitment
The AI shift has already radically changed the way that many people interact with prospective employers. The standardised CV format allowed jobseekers to be evaluated by multiple firms with a single approach. The burden has been shifted from employer to jobseeker – a familiar feature of the gig economy era – and along with it the ability of job seekers to get feedback or insight into the decision-making process. The role of human interaction in hiring has decreased, making an already difficult process deeply alienating.
The H&R industry is big and the “pre-hire assessment” market is worth £2.14bn a year. There are some big companies such as Hirevue who specialise in automated screening. As they say: “There are over 15,000 traits that can be used to identify top performers. These include your choice of language, the breadth of your vocabulary, your eye movements, the speed of your delivery, the level of stress in your voice, your ability to retain information, your ‘valence’ (emotion), and 14,993 others. With a HireVue interview, it’s not just about running through your work history and academic achievements, it’s about your delivery, and what’s going on beneath the surface.”
Machine learning algorithms used in -automated- recruiting are increasingly analysing applicants’ biometric data, among others. Several inferences are then drawn to predict how successful an applicant will be. Below is my interpretation of such a system based on related literature review.
Proletariat.ai- Explorable Machine Learning for Hiring
Proletariat.ai is a conversational agent that demystiﬁes the automated hiring process. It simulates the interview process and analyses users’ responses based on tone, emotion, and personality. Proletariat.ai explains its decisions and helps people understand how they are being filtered. By opening up the inner workings of automated recruiting systems, it creates awareness about transparent and accountable algorithms and helps people claim their right to explanation. It is also an interface for people to update hiring models and give feedback.