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AI and Automation in Recruitment
With AI already revolutionising recruitment, we take a look at the benefits (and challenges) and the future direction of AI for both recruiters and applicants.
Attracting the Best Talent
The Growth of Technology
Automation, the Pandemic, and Digital Transformation
Current Examples of AI and Automation in Recruitment
- Tengai - In March 2019, TNG and Furhat Robotics in Sweden developed a social, unbiased recruitment robot called “Tengai” to be used to conduct job interviews with human candidates. The robot, which looks like an internally projected human face on a white head sitting on top of a speaker (with a camera and microphone built-in), is made with pre-built expressions and gestures as part of a pre-loaded OS which can be further customised to fit any character. The neutrality of this robot ensures that from the beginning of the candidate selection process, candidates are judged on an objective and skills-focused basis, identifying the required competencies needed for a successful candidate. Clients for the Tengai robot are believed to include Findus, TNG, Altran (Engineering + R&D) and Cloetta (confectionery company), with its use being further expanded in the recruitment and staffing within the Banking and Finance, FMCG and the Public Sector industries.
- Vodafone - Vodafone uses the HireVue platform to help tackle its challenge of 100,000 graduates applying for just 1,000 jobs. HireVue has been reported to have helped tackle human bias from the recruitment process by enabling the analysis of 25,000 data points from video interviews to sort candidates into highly recommended, recommended and not recommended in a way that correlates well with the company’s assessments. Reports indicate that HireVue has helped Vodafone to cut their time-to-hire from 23 days to 11 days, as well as reducing candidate dropout rates by 30 per cent and tripling cost savings.
- Pymetrics - Pymetrics claims to use behavioural science-based assessments and audited AI technology to collect objective behavioural data in order to measure a job seeker's true potential, rather than just looking at what they have done in the past according to their CV. Some well-known brands that are known to have used Pymetrics include McDonald's, JP Morgan, PwC and the Kraft Heinz food group. The AI-based platform uses questions to evaluate different aspects of an applicant’s personality and intelligence e.g. risk tolerance and speed of response to situations.
- SRO - SRO is a research-driven and cloud-based, evidence-based talent acquisition platform that is designed to centralise recruitment, where everything is viewed on a single dashboard, transform user and applicant experience, and maximise the visibility of jobs across the entire online environment so that more jobs get filled in a time and cost efficient manner. The platform uses advanced CV Parsing and candidate Grading technology, provided by Silicon Valley based Burning Glass, to automatically populate data, rank, and grade applicants. SRO claims to currently support over 700 clients and more than 27,000 UK based recruiters, and to have maintained a 98 per cent independently assessed satisfaction rating since 2016.
- Textico - Seattle-based Textico uses AI to help firms write more inclusive, understandable and accessible adverts that appeal to a broad range of people. Its clients are believed to include World Bank to Dropbox, Spotify, and Tesco.
- Korn Ferry - The AI recruitment software of Korn Ferry, a global organisational consulting firm, enables recruiters to proactively search the Internet for potential job candidates rather than simply waiting for the best people to apply.
How is AI Transforming Recruitment?
- Digital - Increasing efficiency and saving costs through the automation of time-consuming, administrative tasks: The use of AI-based recruitment platforms can help companies to make better use of their available resources by handling some of the time-consuming administration that tends to occur in the early part of the process. The time taken from application to hiring of a successful candidate is shortened due to less sifting and reviewing, and as such resource cost and valuable operational time is saved. For example, “IBM...estimates that it has realised “almost $1 billion in savings” since 2011 by integrating artificial intelligence and other modernization efforts in its HR department, according to global head of talent Obed Louissaint.” (Maria Aspen in Fortune, Special Report on Artificial Intelligence, ‘This tech giant says A.I. has already helped it save $1 billion’, 24/01/2021). This is beneficial for both the recruiter and their resources (i.e. costs and time taken) and the candidates in question, as they can start sooner!
- Human - Reducing human error and biases from the recruitment process: The risk of decisions being made with subconscious biases is significantly reduced through use of computers, and hence lends itself to an overall “fairer” and more consistent process. For example, a recent London School of Economics study of the behaviour of recruiters on employment websites applied the algorithms to the online recruitment platform of the Swiss public employment service. The study found that recruiters treat otherwise identical job seekers who appear in the same search list differently, depending on their immigrant or minority ethnic background. The same study also showed that at certain times of the day, when recruiters become tired, they fall back into ‘intuitive’ decision-making. Furthermore, for some attractive jobs, there is a risk that recruiters, who may be under pressure, are increasingly prone to being overwhelmed by the sheer numbers of responses, and as such not all candidate applications received are properly assessed. An automated AI based system can improve the chances that all applications are initially given consistent consideration.
Challenges of AI and Automation in Recruitment
- Problems with human bias: It may be very difficult to remove completely human bias from AI-recruitment systems, as ultimately the data input into “teaching” (i.e. programming) the machine how to “think” may consist of those very human biases it aims to avoid. For example, in 2018, Amazon was reported to have scrapped its own system due to an apparent bias against female job applicants.
- Prone to manipulation: AI systems could be manipulated by certain applicants. For example, Tribe Pad (software) research found that 88 per cent of candidates who are aware of applicant tracking systems (ATS) have tried to optimise their CV (usually by adding in relevant key words and phrases) to get through what they understand to be the initial selection process.
- Lack of human emotion and interaction: Job applicants may be deterred by the thought of having to go through a completely automated hiring process. This means that more successful AI-based recruitment platforms and processes should at least include some more human elements, such as the use of video interviewing.
The Effect of AI on Recruitment Jobs
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