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New-age Assessment Platforms Ensure Good Candidate Experience

HR Tech Outlook | Tuesday, October 11, 2022

New-age assessment platforms outperform traditional hiring strategies in areas such as cost savings, time savings, easing HR roles, and, most importantly, ensuring a better candidate experience.

FREMONT, CA: The new-age assessment platforms today largely emphasise remote onboarding. It has shifted the in-person hiring experience to an online landscape while constantly evolving tools and assessment methodologies for ensuring suitable hires. The automated approach is a one-way communication system where candidates only interact with the software. This raises the question of whether such assessment platforms ensure a good candidate experience by compensating for face-to-face interaction.

A better candidate experience is understood by ensuring that the process is hybrid and not merely on a digital basis. Hybrid modes will guarantee human touchpoints at relevant milestones and stage gates. Many companies are integrating a range of cognitive and behavioural assessments as an integral part of the digital hiring cycle to optimise the suitable applicant ratio.

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Using video sessions for group discussion and personal interviews enables scalability for wider access and reach. Many auto-interview tools are available to shortlist the most suitable candidates before the interview round. Ultimately, companies should realise that they are dealing with human beings and that there will be risks in making the process completely mechanical. Businesses must exercise caution in retaining the personal touch in any form possible in order to ensure that the process remains human.

Today, candidates have multiple options to interact with their job search platforms. Candidates can view every opening by top institutions, allowing them to navigate and apply for vacancies. Employers also directly reach out to candidates through platforms where they want to schedule interviews with them. As a result, there will be effective two-way communication between candidates and job providers.

However, the most significant problem to be resolved is the possibility of biases during interviews. Many instances prove that AI needs to advance more to be trusted. Many concerns regarding the trustworthiness of these AI-assisted platforms are still in question. This also raises the question of whether it is technically possible for the platform providers to ensure a fair environment for the applicants. Some companies find a solution by filtering via role, qualification, location, and subject, removing any bias's ambiguity.

AI-based recruiting identifies inroads towards pre-screening candidates objectively and providing the most meritocratic set for the final interview with trust scores for every applicant. The results depend upon predicting and assessing every candidate's future behaviour and performance equally and fairly. Bias and human errors can be overcome primarily in three ways: incorporating skill assessments where both quantitative and qualitative skill assessments render highly objective reports and scores. Data-backed blind hiring is another way of covering all irrelevant details and allowing selection to be purely based on the performance of the remote assessment. The final method is to understand biases and restructure assessments. This approach goes back to the drawing board, identifies the various assessment practices needed, and eliminates less suitable candidates.

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