Artificial Intelligence (AI) Powered Assessments
AI has been part of candidate assessments for some time now and its application in selection is still evolving. The use of AI in selection process ranges from chatbots to algorithm-based decisions made from analysing candidate responses.
Chatbots are used for screening applicants for volume hiring roles. Chatbot-type conversations provide assistance to screen applicants even before they apply for a job, to ensure minimum requirements are met. Just like answering pre-application questions!
Artificial Intelligence (AI) is also being used increasingly in one-way video assessments to reduce human biases and make the decision-making process more objective and fairer. Predictive analytics can compare the personality traits of a candidate against those of an ideal. These type of video interviews are sometimes referred to as behaviour-based video assessments.
Often, these are based around what is known as the Big 5 which is a universally accepted psychological model of personality traits:
These personality traits are viewed as reliable predictors of workplace behaviours which enable employers to make assessment about team and cultural fit.
Behaviour-based video assessments driven by AI analyse facial expressions, language, tone of voice and body language analysing thousands of data points to rank applicants and assess their fit for the role and organisational culture. AI can be used to ask follow-up questions, mimicking a face-to-face interview as closely as possible.AI analyses each video within seconds of completion looking at visual and other cues to predict personality and behaviour patterns.
Machine learning is a subset of AI. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Some on-way video interviews use machine learning to automatically transcript interviews with highlighted keywords and searchable terms. Machine learning may also be used to highlight candidate interviews with high corresponding scores within group of successful hires, within specific industries, locations, and experience levels.
Please see below for a range of AI powered video-based behavioural assessments.
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The Harrison Assessments System provides a comprehensive assessment of the behavioural competencies required for a position and accurately predicts success and potential obstacles. Integrated selection tools include performance based interviewing questions, how to attract the candidate, and the ability to calculate eligibility, suitability, and interview ratings for a composite ranking of candidates.
The assessment is web-based and scored online with comprehensive results available within 15 seconds.
Enjoyment Performance Theory: Enjoyment Performance Theory states that an individual will perform more effectively in a job if they enjoy the tasks required by that job, have interests that relate to the position, and have work environment preferences that correspond with the environment of the workplace. Harrison Assessment’s global research indicates that the enjoyment of these various aspects of a job is highly correlated with good performance. "If you enjoy an activity, you tend to do it more. By doing it more, you tend to learn and improve the related skills. As a result, you tend to gain recognition (including self recognition) which helps you enjoy the activity more."
Candidates rank answers in order of what you they know enjoy. Enjoyment performance theory states that if someone enjoys 75% of the tasks required in a job role they are 3x more likely to be successful.
Paradox Theory: Harrison Assessment’s Paradox Theory provides a greater depth of psychological understanding because it reveals an entire system of behaviour rather than merely offering insights about specific traits. It also predicts stress behaviour and provides a framework that facilitates objective understanding of self and a clear direction for self-development.
The ability to predict job performance is dependent upon identifying all of the critical factors. If one assesses eligibility or technical competencies, it only represents a portion of the critical factors to predict performance. When behavioural competencies are also measured, such as emotional intelligence, personality, and work preferences, a high degree of accuracy is attained to predict performance.
Note: No training required use the assessment as we will support you in administering and delivering the reports to you. should you wish to manage this process yourself, we would be able to offer the training.
Some people claim that AI is responsible for creating biases. Contrary to this belief, AI can actually help us to acknowledge societal biases that we may not be aware of and has been proven to reduce human biases and assist in fairer decision-making. Research by John Kleinberg suggests that algorithms used in AI reduced racial disparity in the criminal justice system. We recommend that you explore with your chosen provider what audits they put in place to ensure biases are minimised while using AI.