"Congratulations, you have been selected for an interview for the professional minigamer position at Open Mind Corporation," a robotic voice announces over a blank screen. I will be guiding you through the interview. The whole process will take no more than 10 minutes. This is the start of An Interview With Alex, a dystopian online interactive experience taking viewers through a "job interview" conducted by an A.I. hiring manager--one that measures tone to score users on a "State of Mind Index." Carrie Sijia Wang, the multimedia artist behind the project, writes that her work is meant to "criticize the present by speculating about the future." But it's not that far off how your next job interview might look, if you're applying for high-volume, low-skilled roles (or even some high-skilled ones).

Voluntary employee turnover can have a direct financial impact on organisations. And, at the time of this pandemic outbreak where the majority of the organisations are looking to cut down their employee costs, voluntary employee turnover can create a big concern for companies. And thus, the ability to predict this turnover rate of employees can not only help in making informed hiring decisions but can also help in saving a substantial financial crisis in this uncertain time. Acknowledging that, researchers and data scientists from PredictiveHire, a AI recruiting startup, built a language model that can analyse the open-ended interview questions of the candidate to infer the likelihood of a candidate's job-hopping. The study -- led by Madhura Jayaratne, Buddhi Jayatilleke -- was done on the responses of 45,000 job applicants, who used a chatbot to give an interview and also self-rated themselves on their possibility of hopping jobs.

Since the onset of the pandemic, a growing number of companies have turned to AI to assist with their hiring. The most common systems involve using face-scanning algorithms, games, questions, or other evaluations to help determine which candidates to interview. While activists and scholars warn that these screening tools can perpetuate discrimination, the makers themselves argue that algorithmic hiring helps correct for human biases. Algorithms can be tested and tweaked, whereas human biases are much harder to correct--or so the thinking goes. In a December 2019 paper, researchers at Cornell reviewed the landscape of algorithmic screening companies to analyze their claims and practices.