Last week, the jewel of Australia’s tech sector, Atlassian, was lauded for giving staff the privilege of working from home – forever.
After posting this on our team slack channel with a comment by me warning of the longer-term impact of ‘remote forever’, one of our senior team members said:
“Why do people travel in the morning to an office? In a packed tram/train carrying a laptop , then work on that laptop only to carry it back home in a packed train, wasting precious time?”
When I worked for another technology company, we spent a lot of energy trying to convince leadership that WFH did not mean a free ride. And, in fact, would unleash productivity and improve engagement. COVID has brought forward the idea of WFH as an alternative arrangement for many that wouldn’t have otherwise considered it.
Whilst we may be revelling in the success of dismantling the long-held bias, that you need to see someone at work to trust that they are doing the work, it comes with its own set of challenges around organisational relevance.
Work is a relationship
Does it matter what company you work for if the only difference between one job is for whom you are completing a task, and perhaps the one or two people that you work with closely?
Work is a relationship, and relationships thrive on intimate and frequent connections. When we all worked in offices some of that intimacy was built by the serendipity of conversations that you had while going about your day’s work. There was always the potential to catch someone from outside of your team and share an idea and solicit a different perspective.
There was an ease of connections and interactions that can be hard to replicate in a remote work context. Being remote is a little bit like trying to establish a long-distance relationship. Which all of us know have the chances of success stacked against them.
Then there is the influence of place, and of space. At REA Group where I worked for some years the building fed the culture. Its design and redesign were carefully thought through to maximize connections and space to collaborate. With anyone. Not just those in your immediate team.
Connection
Why do people go to church to pray, the pub to drink, and the footy to watch their team, when they have the bible at home, beer in the fridge, and a TV in the living room? Because they are looking for connection, community, and inspiration.
Once the novelty of WFH wears off, and for many it already has, comes the very real challenge of maintaining connection, building affiliation, and building cultures when people and teams are not spending time together – physically, in any shared space.
Ongoing remote work presents very practical challenges for organisations, particularly around company culture and organisational HR.
- How do you assess performance when you can’t see people at work?
- And how do you look out for people, mentor them, develop them when your interactions are all booked in, bound within a strict working day?
- How do you acknowledge someone for something you heard they did well in another meeting like you might as you jump in a lift together?
There is a real risk that our employment relationship becomes transactional, which then impacts engagement, which then impacts productivity etc.
We know from our own work in this space, personality is not 16 types on a table, it is way more nuanced and diverse than that. In a population of 85,000 equal men and women, we find at least 400 uniquely identifiable personality types.
Personalization
While we live in a world of hyper-personalization – our morning news feed is our feed, our Netflix profile is our personal profile based on our viewing history,
How can an organisation retain that diversity of perspective when it usually thinks of two binary ways of working – in an office, or at home? It can’t.
That’s why the future of work has to involve a new type of technology, a technology that can navigate the rich mix of types we work with, adapt to their communication style, their working style.
While I have championed for WFH in senior HR positions I’ve held, this experience has highlighted for me the many things I might have always taken for granted in an office environment.
It has nothing to do with fancy décor and an ergonomic chair. More those human moments of serendipitous connection. It all disappeared so quickly without almost any time to say good-bye.
I’m learning what my motivations are, and what connections I want in a day.
From the conversations I’ve had with friends and workmates, they’re also making similar self-discoveries. I’d like to think we all emerge from this situation with a mind to honour the things we’ve learned about our “work selves.”
And most importantly, to build company cultures that thrive by accommodating those diverse needs.
Barbara Hyman, 03/09/20
Source: recruitingdaily.com/could-personalized-work-be-what-we-aim-for-post-covid/
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It’s been a year of Big Moves at Sapia.ai. From welcoming groundbreaking brands to achieving incredible milestones in our product innovation and scale, we’re pushing the boundaries of what’s possible in hiring.
And we’re just getting started 🚀
Take a look at the highlights of 2024
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This year, with the addition of Live Interview, we’re proud to say our platform now covers screening, assessing and scheduling.
It’s an all-in-one volume hiring platform that enables our customers to deliver a world-leading experience from application through to offer.
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Every 15 seconds, a candidate is interviewed with Sapia.ai.
This year, we’ve saved hiring managers and recruiters hours of precious time that can now be used for higher-value tasks.
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Our platform allows candidates to be their best selves, so our customers can find the people that truly belong with them. They’re proud to use a technology that’s changing hiring, for good.
Leading the way in AI for hiring
We’ve continued to push the boundaries in leveraging ethical AI for hiring, with new products on the way for Coaching, Internal Mobility & Interview Builders.
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Situational Judgement Tests vs. AI Chat Interviews: A Modern Perspective on Candidate Assessment
Choosing the right tool for assessing candidates can be challenging. For years, situational judgement tests (SJTs) have been a common choice for evaluating behaviour and decision-making skills. However, they come with limitations that can make the hiring process less effective and less inclusive.
AI-enabled chat-based interviews, such as Sapia.ai, provide organisations with a modern alternative. They focus on understanding candidates as individuals and creating a hiring experience that is both fair and insightful while enabling efficient screening and selection.
This shift raises important questions: Are SJTs still a tool that should be considered for volume hiring? And what do AI assessments offer in comparison?
1. The Static Nature of SJTs
Traditional SJTs use predefined multiple-choice questions to assess behavioural tendencies and situational knowledge. While useful for screening, these static frameworks lack the flexibility to adapt based on real-world performance data or evolving role requirements.
Once created, SJTs don’t adapt to new data or evolving organisational needs. They rely on fixed scenarios and responses that may not fully reflect the dynamic realities of modern workplaces, and as a result, their relevance may diminish over time.
AI-enabled chat interviews, on the other hand, are inherently adaptive. Using machine learning, these tools can continuously refine their models based on feedback from real-world outcomes such as hiring or turnover data. This ability to evolve ensures the assessments align with organisations’ needs.
2. Richer Data Through Open-Ended Responses
One of the main critiques of SJTs is their reliance on multiple-choice responses. While structured and straightforward, these options may not capture the full scope of a candidate’s thinking, communication skills, or problem-solving ability. The approach is often limiting, reducing complex human behaviour to a few predefined choices.
AI-enabled chat interviews work more holistically and dynamically. These tools provide a more complete picture of a person by allowing candidates to answer questions in their own words. Natural language processing (NLP) analyses their responses, offering insights into personality traits, communication skills, and behavioural tendencies. This open-ended format lets candidates express themselves authentically, giving employers a deeper understanding of their potential.
3. The Candidate Experience: Stressful or Supportive?
SJTs often include time constraints and rigid formats, which can create pressure for candidates. This is especially true when candidates feel forced to choose options that don’t fully reflect how they would actually behave. The process can feel impersonal, even transactional.
In contrast, chat-based interviews are designed to be conversational and low-pressure for candidates. By removing time limits and adopting a familiar chat interface, these tools help candidates feel more at ease. They also frequently include personalised feedback, turning the assessment into a valuable experience for the candidate, not just the employer.
4. Addressing Bias and Fairness
Traditional SJTs are prone to transparency issues, as candidates can often identify and select the “best practice” answers without revealing their true tendencies. Additionally, static test designs can unintentionally embed bias; due to the nature of the timed test, SJTs have been found to disadvantage some groups.
AI chat interviews, when developed ethically within a framework like Sapia.ai’s FAIR Hiring Framework, eliminate explicit bias by relying solely on the content of a candidate’s responses. Their machine learning models are continuously validated for fairness, ensuring that hiring decisions are free from subjective judgments or irrelevant demographic factors.
5. An Assessment That Improves Over Time
Workplaces are constantly changing, and hiring tools need to keep up. SJTs’ fixed nature can make them less effective as roles evolve or organizational priorities shift. They provide a snapshot but not a dynamic view of what’s needed.
AI-enabled chat interviews are built to adapt. With feedback loops and continuous learning, they incorporate real-world hiring outcomes—like retention and performance data—into their models. This ensures that assessments stay relevant and effective over time.
Rethinking Candidate Assessment
As hiring demands grow more complex, so does the need for tools that can capture the whole person, not just their response to hypothetical scenarios. While SJTs have played an important role in hiring practices, they are increasingly being replaced by tools like AI-enabled chat interviews.
These modern approaches provide richer data, adapt to changing needs, and create a richer and more engaging experience for candidates. Perhaps most importantly, they emphasise fairness and inclusivity, aligning with the growing demand for unbiased hiring practices.
For organisations evaluating their assessment tools, the question isn’t just which method is “better.” Understanding the specific needs of your roles, teams, and candidates will help you choose tools that help you make decisions that are both informed and equitable.
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Keeping Interviews Real with Next-Gen AI Detection
It’s our firm belief that AI should empower, not overshadow, human potential. While AI tools like ChatGPT are brilliant at assisting us with day-to-day tasks and improving our work efficiency, employers are increasingly concerned that they’re holding candidates back from revealing their true, authentic selves in online interviews.
As an assessment technology provider, we are responsible for ensuring the authenticity and integrity of our platform. That’s why we’re thrilled to unveil the latest upgrade to our flagship Chat Interview: the AI-Generated Content Detector 2.0. With groundbreaking accuracy and a candidate-friendly design, this innovation reinforces our mission to build ethical AI for hiring that people love.
Artificially Generated Content (AGC) is content created by an AI tool, such as ChatGPT, Claude, or Pi. We initially rolled out the first version of our AGC detector last year and have continued to improve it as our data set has grown and these AI tools have evolved.
What’s New?
Our updated AGC Detector 2.0 achieves an impressive 98% detection rate for AI-assisted responses, with a false positive rate of just 1%. This gives organisations peace of mind that they’re getting the most authentic assessment of every candidate.
This cutting-edge system builds on Sapia.ai’s proprietary dataset of over 2 billion words, derived from more than 20 million interview question-answer pairs spanning diverse roles, industries, and regions. It’s trained on real-world data collected before and after the release of tools like ChatGPT, ensuring it remains robust and reliable even as AI tools evolve.
The Challenge of AI in Chat-based Interviews
Our data shows that around 8% of candidates use tools like GPT-4 to generate responses for three or more interview questions. While these tools may offer a quick way for candidates to complete their interview, they can inadvertently hide a person’s true personality and potential – qualities our customers are most interested in understanding through our platform. In fact, research from Sapia Labs shows that these tools have their own personality traits, which may be quite different from the candidate applying for the role.
For Candidates: Enabling Authenticity
When a response is flagged as potentially AI-generated, the system doesn’t disqualify candidates. Instead, a real-time warning pops up, allowing them to revise their answers or submit them as-is. This ensures that candidates are encouraged to present themselves authentically, reflecting their unique communication styles and sharing their genuine experiences.
For Hiring Teams: Actionable Insights
Responses flagged as AI-generated are highlighted in the candidate’s Talent Insights profile, accessible via Sapia.ai’s Talent Hub or ATS integrations. These insights give hiring teams the transparency to make informed decisions, fostering trust while accelerating hiring timelines.
Built on Unmatched AI Interview Expertise
“Our detection model’s strength lies in its foundation of real-world interview data collected from diverse roles and regions,” says Dr Buddhi Jayatilleke, Sapia.ai’s Chief Data Scientist. This depth of understanding enables the AGC Detector to maintain its industry-leading accuracy – even when candidates subtly modify AI-generated answers to appear more human.
Why This Matters
The AGC Detector 2.0 embodies Sapia.ai’s commitment to ethical AI that amplifies human potential. As our CEO Barb Hyman explains:
“The hiring landscape has fundamentally changed since ChatGPT, but our commitment remains clear: AI should amplify human potential, not penalise it. This breakthrough fosters authentic hiring conversations. Our real-time warning system helps candidates make better choices and gives enterprises confidence in their selection decisions.”
Testing and Validation of the AGC Detector 2.0
The new detector has been rigorously tested on over 25,000 interview responses generated by humans and leading AI models like GPT-4, Claude-3.5, and Llama-3. The results speak for themselves, reinforcing the reliability and fairness of this game-changing technology.
Fairness & Transparency in AI-Enabled Hiring
By detecting AI-generated content while allowing candidates to correct their responses, our AGC Detector 2.0 ensures every applicant has the chance to put their best, most authentic foot forward when applying for a role powered by Sapia.ai. For enterprises, it provides confidence in the integrity of their hiring decisions and ensures they’re connecting with real candidates at scale.