Many applications such as hiring and university admissions involve evaluation and selection of applicants. These tasks are fundamentally difficult, and require combining evidence from multiple different aspects (what we term "attributes"). In these applications, the number of applicants is often large, and a common practice is to assign the task to multiple evaluators in a distributed fashion. Specifically, in the often-used holistic allocation, each evaluator is assigned a subset of the applicants, and is asked to assess all relevant information for their assigned applicants. However, such an evaluation process is subject to issues such as miscalibration (evaluators see only a small fraction of the applicants and may not get a good sense of relative quality), and discrimination (evaluators are influenced by irrelevant information about the applicants). We identify that such attribute-based evaluation allows alternative allocation schemes. Specifically, we consider assigning each evaluator more applicants but fewer attributes per applicant, termed segmented allocation. We compare segmented allocation to holistic allocation on several dimensions via theoretical and experimental methods. We establish various tradeoffs between these two approaches, and identify conditions under which one approach results in more accurate evaluation than the other.

Artificial intelligence (AI) and facial expression technology is being used for the first time in job interviews in the UK to identify the best candidates. Unilever, the consumer goods giant, is among companies using AI technology to analyse the language, tone and facial expressions of candidates when they are asked a set of identical job questions which they film on their mobile phone or laptop. The algorithms select the best applicants by assessing their performances in the videos against about 25,000 pieces of facial and linguistic information compiled from previous interviews of those who have gone on to prove to be good at the job. Hirevue, the US company which has developed the interview technology, claims it enables hiring firms to interview more candidates in the initial stage rather than simply relying on CVs and that it provides a more reliable and objective indicator of future performance free of human bias. However, academics and campaigners warned that any AI or facial recognition technology would inevitably have in-built biases in its databases that could discriminate against some candidates and exclude talented applicants who might not conform to the norm. "It is going to favour people who are good at doing interviews on video and any data set will have biases in it which will rule out people who actually would have been great at the job," said Anna Cox, professor of human-computer interaction at UCL. Hirevue, which last month received a major investment injection from the multi-billion pound Carlyle Group, says it has already used its technology for 100,000 interviews in the UK.

Artificial intelligence (AI) and facial expression technology is being used for the first time in job interviews in the UK to identify the best candidates. Unilever, the consumer goods giant, is among companies using AI technology to analyse the language, tone and facial expressions of candidates when they are asked a set of identical job questions which they film on their mobile phone or laptop. The algorithms select the best applicants by assessing their performances in the videos against about 25,000 pieces of facial and linguistic information compiled from previous interviews of those who have gone on to prove to be good at the job. Hirevue, the US company which has developed the interview technology, claims it enables hiring firms to interview more candidates in the initial stage rather than simply relying on CVs and that it provides a more reliable and objective indicator of future performance free of human bias. However, academics and campaigners warned that any AI or facial recognition technology would inevitably have in-built biases in its databases that could discriminate against some candidates and exclude talented applicants who might not conform to the norm.