Graduate recruitment during COVID-19 – what’s different?

Do you wish you could harness the very best attributes of your people and just hire more of them, quickly, without bias? Do you spend more time recruiting than you would like? Have you ever gone against your better judgement and hired hastily only to discover the whole process has cost your business greatly?

The fact is, in retail, staff turnover is a whopping 2.5x higher than that of other industries. And if every bad hire costs your business 1.5x their annual salary, the costs mount up. Not to mention the lost sales from not having a full team on the store floor every day … bad hire costs, add up – fast.

Know which staff will fit, perform and stay – before you hire them!

You’ll love this. Built on robust psychological and data science, PredictiveHire’s technology compares tens of thousands of data points specifically attained from retail staff based around the world.

Your applicants will be compared to this powerful data to predict their likelihood to stay with you – creating really powerful candidate shortlists. The results speak for themselves.

Superdry are already experiencing the value today

Simon Amesbury, Superdry’s Resourcing Manager sums it up by saying:

“PredictiveHire ticked all the boxes: Cost savings. Time efficiencies throughout the process – less time on screening, sifting, interviewing, assessing, the list continues. A simpler life for store managers by speeding up shortlisting. And a way to boost the number of long-lasting, productive staff.”

Simon says: “Start now. The savings are there to be taken and the benefits are yours to gain!”

To get started and experience smarter hiring with no upfront costs, contact us for a discussion on how PredictiveHire can help you resolve your retail hiring issues.

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Right now video screening is the solution of choice for many, given the challenges of recruiting during the pandemic. Every day I’m asked about video solutions, and every week there seems to be a new video solution for hiring.

This isn’t people simply switching to Zoom, but rather embracing AI  video platforms where you are judged by algorithms. Often algorithms crawl these videos to identify top candidates. This is not great. In fact, it’s horrifying. Not all video interviews are bad, given the pandemic it’s often become a necessity as a default for face-to-face interviews in the final stages of a recruitment process. But when it comes to top-of-the-funnel screening with first interviews, video interviews lead to biased outcomes.

Put simply, image and video recognition is built to favour white faces. In the documentary Coded Bias an M.I.T. Media Lab researcher Joy Buolamwini found that the algorithm couldn’t detect her face–until she put on a white mask. There are hundreds of validated research findings which confirm this.

Video Screening

Video invites judgement. It adds stress to the candidate with added pressure around hair and makeup, picking the right fake backdrop (yes, there are hundreds of advice columns on this), and practising and rehearsing your answers until you nail the recording. It turns a simple interview into a small theatre production.

Not everyone is comfortable on video, most especially introverts, people with autism, and people who feel marginalised. These factors do not influence or speak to a person’s ability to do a job, but by using video as part of the interview process they are put at a deep disadvantage. What percentage of people are you excluding just by using video?

Chat is a better option. It solves the challenges of remote interviews while being inclusive.

Try it for yourself, we’ll send you real results. 

The discussion on ethical AI is gaining significant momentum. With the increasing use of artificial intelligence (AI) in various industries, there is a growing need to ensure that AI is employed ethically and built with ethical considerations in mind.

We’re going to explore the importance of ethical AI and discuss four key components to consider when integrating AI technology into organizations: fairness, accuracy, explainability, and privacy.

The need for ethical AI

AI offers several benefits, one of which is speed. Automating tasks that were previously performed by humans can save time and resources. However, it is crucial to carefully consider the problems AI is meant to solve.

For example, when addressing the scheduling of interviews, the underlying issue may not be the automation of the process but rather the need to hire and retain the right people. Quality should always be prioritized over mere automation.

Sapia.ai’s AI Smart Interviewer goes beyond speed and automation to find candidates that are properly matched to the needs and values of our customers. For one of our retail customers, this approach has achieved a 50% reduction in churn.

That’s what you stand to gain.

Objectivity and removing bias

One of the primary reasons organizations turn to AI is to introduce objectivity and mitigate human bias. While human bias is a natural aspect of decision-making, it can hinder the identification of talent and result in unfair judgments.

AI can provide a more objective assessment by focusing on relevant data that is not influenced by subjective factors like appearance or body language. It is important to understand that AI should not be the sole decision-maker but rather an input that aids the decision-making process.

Four components of ethical AI

1. Fairness: It is essential to evaluate whether AI systems exhibit bias. Good AI vendors should provide data that demonstrates fairness, allowing organizations to assess the impact of the tool on equity in terms of race, gender, and broader demographics. Using training data that is as close to first-party and proprietary data as possible helps minimize biases inherent in third-party datasets.
2. Accuracy: AI should provide meaningful and reliable inputs and outputs. Organizations must verify whether the AI system’s output is relevant and can effectively inform decision-making processes. Meaningless or irrelevant outputs can lead to misguided decisions.
3. Explainability: Transparency and explainability are critical aspects of ethical AI. The ability to understand and explain the decision-making process of AI systems is vital. Candidates, as well as organizations, should be able to comprehend the technology being employed and the factors influencing its decisions.
4. Privacy: As the importance of data privacy continues to grow, organizations must handle candidate data responsibly. Respecting the sanctity of personal data builds trust. It is crucial to only collect necessary data, comply with data protection regulations like GDPR, and ensure that data is not shared with third parties without consent.

Building trust through ethical AI

Trust is the foundation of successful HR and talent acquisition processes. Prioritizing ethical AI contributes to building trust with candidates and creating a positive hiring experience.

Treating data with respect, maintaining data sovereignty, and being transparent about the technology used instills confidence in candidates that their data is handled responsibly.

Ethical AI is not just a buzzword; it is a necessary consideration in today’s AI-driven world. By prioritizing fairness, accuracy, explainability, and privacy, organizations can ensure that AI systems operate ethically and responsibly. Integrating ethical AI practices into HR and talent acquisition processes builds trust, fosters positive cultures, and ultimately leads to better decision-making and outcomes.