Artificial Intelligence – or AI – touches many parts of our daily life.

Digital voice assistants answer the questions we ask, robots at work take away some of the repetitive tasks we face and chatbots give us extra support when we need it.

The use of AI in assessment within organizations is not new. For years personality questionnaires have been scored and "interpreted" by expert-developed algorithms.

But this was just the start. AI in assessment is growing at a rapid pace – and there are questions you’ll be asked in your role as an HR, recruiter or talent professional.

Do you know the answers?

Below are the 8 fundamental questions to which you need to know the answers.

Question 1

What is meant by AI in assessment?

AI-based assessment is beginning to be part of many candidate and employee psychometric assessments, from realistic chatbot-type conversations with candidates in situational judgment tests (SJT) to proven algorithm-based decisions made from looking at candidate responses to test questions. The use of AI in assessment now regularly informs HR and talent decisions.

Question 2

Is Artificial Intelligence (AI) already used in talent assessment?

The simple answer is “Yes!” The use of AI in assessment has ramped up in recent years. There is more debate about how AI affects the workplace, including talk of it changing the way HR services the workforce and how it’s taking on some routine tasks of human employees. And we’re all getting used to AI being part of our lives through online purchase suggestions and smart-home devices.

So it’s not surprising that talent assessment too is being shaped by AI.

The first move to AI in assessment came in the 1990s, when paper-based versions of tests moved to computers, with automated scoring and computer-generated interpretive reports. For the first time, technology was taking on some routine tasks and using algorithms to produce a candidate report.

AI is now being used to generate unique test questions “on the fly,” and in tests that make use of adaptive scoring.

Question 3

What does all the AI jargon mean?

It’s certainly possible to feel a bit lost in a sea of technical-sounding terms when it comes to AI. Here are a few key ones that you need to know regarding the use of AI in assessment.

Robotic process automation: This is achieved by gathering and transferring expert knowledge and then programming the system with an "if/then" rule-based approach. Chatbots are a great example. But this rule-based system is not capable of learning and improving without being given explicit instructions. In terms of its use in talent assessment, computer-generated interpretative reports make use of this.

Machine learning: Even though a computer can’t think for itself (not yet anyway), statistical tools can enable a system to model predictions from any given data – and to add to this to improve its predictions over time. This is used in data analysis to create predictive people analytics as a means to help employers make better talent decisions.

Pattern matching: This AI technique gets the computer to check the sequence of responses to determine if there is a pattern. It can be used to carry out some "human" tasks, such as recognising faces or identifying emotions.

Natural language processing: Makes use of text and speech analytics to extract the underlying meaning. This could have applications in analyzing speech in interview question responses.

By combining all these aspects, AI has a key role to play in analyzing and interpreting vast amounts of candidate data.

Question 4

How does AI improve candidate and employee assessment?

There are five key benefits of AI in assessment.

1. Precision. AI can analyze massive amounts of data, far more than any human. The increased power of today’s computers means more candidate data can be precisely evaluated to help make better selection decisions.

2. Efficiency. AI lets recruiters and talent teams conduct consistent and objective assessments of job-relevant data at a much earlier stage. For example, AI lets you use video interviews far earlier in the selection process than is typically possible with in-person interviews.

3. Reducing bias. Human biases and stereotypes are often to blame for poor selection decisions. In theory, AI’s objectivity helps recruiters eliminate conscious and unconscious bias, but in reality you have to be very careful about how your AI system is programmed. An algorithm is only as good as the data that’s fed into it. Your AI design can’t mimic just one assessor (and all his or her biases); it has to draw from several assessors.

4. Legally defensible. Your assessment AI must be transparent and open to challenge. Complex “black box” algorithms can make selection decisions difficult to justify, because they make it almost impossible to understand how their conclusions are reached — and if your selection decisions can’t be easily explained, they could be challenged in court. Allowing AI to continuously learn by “observing” the best practices of human raters offers the best and most legally defensible approach.

5. Engagement. AI can significantly improve the candidate experience. It lets recruiters offer immediate support and help — for example, through interactive chatbots that can answer queries about the selection process or about specific assessments. AI can also optimize and enhance the selection experience for candidates — for example, by allowing open-ended responses in personality questionnaires and situational judgment tests. Faster decisions, reduced bias and enhanced assessments all are ways that AI can improve the experience for job-seekers.

Question 5

How does AI support video interviewing?

Video interviewing typically involves candidates recording themselves responding to competency-based interview questions. These recordings mean that candidates no longer need to travel for interviews, that interviewers get to rewatch and share candidate responses, and that less time is spent on the interview itself. But there’s a considerable amount of time that is spent analyzing the responses.

How great would it be if the analysis could be done quickly and objectively? This is where AI has a role.

AI means the audio can be transcribed and analyzed for clarity of speech and proficiency in English. And AI helps to analyze the visual elements through emotion-tracking software and facial recognition.

Question 6

What challenges need to be overcome when using AI in assessment?

Beyond the technological considerations, there are four challenges that must be addressed:

1. Defensibility. Standardized “plug-and-play” AI systems are available, but they won’t differentiate your employer brand. If your competitors use the same systems you’ll all be chasing the same talent. Also these systems use “deep learning networks” that learn as they go. This sounds promising, but actually it makes it difficult to explain why a candidate was accepted or rejected. These systems can lead you to selection decisions that you can’t defend, which leaves you vulnerable to litigation. Only custom AI systems offer the ability to make transparent and defensible selection decisions.

2. Time. Custom AI systems mirror human behavior and replicate the best practices of your assessors and raters. To achieve this, you have to pre-feed the system with relevant information. It can take up to six months to “train” an AI system to assess candidates in the same way that your assessors and raters would judge them. Managing this lead time will be a major challenge.

3. Ethics. There is an ethical question around how much support you take from an AI system. For example, are you happy for an AI system to reject your candidates? Or would you prefer it to flag unsuitable candidates so you can review and check their details? How to use AI ethically will be a key consideration for many employers.

AI’s role should be restricted to providing additional information and enhancing efficiency. Recruiters should always set the objectives when hiring. AI can then deliver useful information, at various stages of the selection process, that will support a final decision.

4. Data handling. AI excels at analyzing massive amounts of data, but the results can be misinterpreted or even deliberately abused. Good data-handling practices will be essential not just for confidentiality but also for maintaining your organization’s reputation. AI should be used carefully and honorably to help you predict which candidates will be effective in the role and engaged by your organization.

Question 7

How can we make sure that AI in assessment is legally defensible?

The process of selecting candidates — both for entry into the organization and promotion within — must always be legally defensible. It must not be discriminatory nor favor a particular group of candidates based on gender, race or any other protected aspect of identity.

There are also the rights of the individual, and the right to be informed how assessment information is to be used. Under the EU’s General Data Protection Regulation, you need to make sure candidates know how and why assessment is being used, including profiling to make decisions. But regardless of AI, this is good practice.

Question 8

How is AI in assessment likely to develop?

We’ve come a long way applying AI in assessment contexts, and the progress will only speed up and become more refined.

Assessment developers are already looking at how AI can help interpret responses to open-ended questions in personality questionnaires and some other progressive assessments, such as those based around messaging apps. Real-time interviews could be carried out by an avatar over the internet, or with the avatar being the observer of a hiring manager’s interview.

Using AI in assessment takes care of administrative tasks and reduces hassles for HR decision-makers while making sure the humans are still in control. For candidates, AI in assessment means they get to respond to assessment tools in a more natural way, rather than being restricted to completing written tests.

Want to use AI across your talent assessment?

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