The Diversity Dashboard: Proving your DEI strategy is working

Why measuring diversity matters

Organisations invest heavily in their employer brand, career sites, and EVP campaigns, especially to attract underrepresented talent. But without the right data, it’s impossible to know if that investment is paying off.

Representation often varies across functions, locations, and stages of the hiring process. Blind spots allow bias to creep in, meaning underrepresented groups may drop out long before offer.

Collecting demographic data is only step one. Turning it into insight you can act on is where real change and better hiring outcomes happen.

What is the Diversity Dashboard?

The Diversity Dashboard in Discover Insights, Sapia.ai’s analytics tool, gives you real-time visibility into representation, inclusion, and fairness at every stage of your talent funnel. It helps you connect the dots between your attraction strategies and actual hiring outcomes.

Key features include:

• Demographic filters – Switch between gender, ethnicity, English as an additional language, First Nations status, disability, and veteran status. View age and ethnicity in standard or alternative formats to match regional reporting needs.
• Representation highlights – Identify the top five represented sub-groups for each demographic, plus the three fastest-growing among underrepresented groups.
• Track trends over time – See month-by-month changes in representation over the past 12 months, compare to earlier periods, and connect the data back to your EVP and attraction spend.
• Candidate experience metrics – Measure CSAT (satisfaction) and engagement rates by demographic to ensure your hiring process works for everyone. Inclusion is measurable.
• Hiring fairness – Compare representation in your applied, recommended, and hired pools to spot drop-offs. Understand not just who applies, but who progresses — and why.

   

From insight to action

With the Diversity Dashboard, you can pinpoint where inclusion is thriving and where it’s falling short.

• See if your EASL candidates are applying in high numbers but not progressing to live interview.
• Spot if candidates with a disability report high satisfaction but have lower offer rates.
• Track the impact of targeted campaigns month-by-month and adjust quickly when something isn’t working.
  

It’s also a powerful tool to tell your success story. Celebrate wins by showing which underrepresented groups are making the biggest gains, and share that progress with boards, executives, and regulators.

Built on science, backed by trust

Powered by explainable AI and the world’s largest structured interview dataset, your insights are fair, auditable, and evidence-based.

Measuring diversity is the first step. Using that data to take action is where you close the Diversity Gap. With the Diversity Dashboard, you can prove your strategy is working and make the changes where it isn’t.

Book a demo to see the Diversity Dashboard in action.

Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.

In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.

This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.

We’ve come a long way, but we’re not there yet.

Shifting from retrofits to inclusive-by-design

Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:

• Sharing interview questions in advance

• Replacing group exercises with structured simulations

• Offering a variety of assessment formats

• Co-designing assessments with neurodiverse candidates

But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.

Hiring needs to look different.

Insight 1: The next frontier of hiring equity is universal design

Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.

That’s why Sapia.ai was built with universal design principles from day one.

Here’s what that looks like in practice:

• No time limits — Candidates answer at their own pace
• No pressure to perform — It’s a conversation, not a spotlight
• No video, no group tasks — Just structured, 1:1 chat-based interviews
• Built-in coaching — Everyone gets personalised feedback

It’s not a workaround. It’s a rework.

Insight 2: Not all “friendly” methods are inclusive

We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.

When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:

“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”

“It was less anxiety-inducing than video interviews.”

“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”

Insight 3: Prediction ≠ Inclusion

Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.

Inclusion means seeing people for who they are, not who they resemble in your data set.

At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.

Where to from here?

Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.

That’s why we built a platform that:

• Doesn’t rely on disclosure

• Removes ambiguity and pressure

• Creates space for everyone to shine

• Measures what matters, fairly

Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat. 

There’s growing interest in AI-driven tools that infer skills from CVs, LinkedIn profiles, and other passive data sources. These systems claim to map someone’s capability based on the words they use, the jobs they’ve held, and patterns derived from millions of similar profiles. In theory, it’s efficient. But when inference becomes the primary basis for hiring or promotion, we need to scrutinise what’s actually being measured and what’s not.

Let’s be clear: the technology isn’t the problem. Modern inference engines use advanced natural language processing, embeddings, and knowledge graphs. The science behind them is genuinely impressive. And when they’re used alongside richer sources of data, such as internal project contributions, validated assessments, or behavioural evidence, they can offer valuable insight for workforce planning and development.

But we need to separate the two ideas:

• Skills Measurement: Directly observing or quantifying a skill based on evidence of actual performance. 
• Skills Inference: Estimating the likelihood that someone has a skill, based on indirect signals or patterns in their data. 

The risk lies in conflating the two.

The Problem Isn’t Inference of Skills. It’s the Data Feeding It

CVs and LinkedIn profiles are riddled with bias, inconsistency, and omission. They’re self-authored, unverified, and often written strategically – for example, to enhance certain experiences or downplay others in response to a job ad. 

And different groups represent themselves in different ways. Ahuja (2024) showed, for example, that male MBA graduates in India tend to self-promote more than their female peers. Something as simple as a longer LinkedIn ‘About’ section becomes a proxy for perceived competence.

Job titles are vague. Skill descriptions vary. Proficiency is rarely signposted. Even where systems draw on internal performance data, the quality is often questionable. Ratings tend to cluster (remember the year everyone got a ‘3’ at your org?) and can often reflect manager bias or company culture more than actual output.

Sophisticated ≠ Objective

The most advanced skill inference platforms use layered data: open web sources like job ads and bios, public databases like O*NET and ESCO, internal frameworks, even anonymised behavioural signals from platform users. This breadth gives a more complete picture, and the models powering it are undeniably sophisticated.

But sophistication doesn’t equal accuracy.

These systems rely heavily on proxies and correlations, rather than observed behaviour. They estimate presence, not proficiency. And when used in high-stakes decisions, that distinction matters.

Transparency (or Lack Thereof)

In many inference systems, it’s hard to trace where a skill came from. Was it picked up from a keyword? Assumed from a job title? Correlated with others in similar roles? The logic is rarely visible, and that’s a problem, especially when decisions based on these inferences affect access to jobs, development, or promotion.

Presence ≠ Proficiency

Inferred skills suggest someone might have a capability. But hiring isn’t about possibility. It’s about evidence of capability. Saying you’ve led a team isn’t the same as doing it well. Collecting or observing actual examples of behaviour allows you to evaluate someone’s true competence at a claimed skill. 

Some platforms try to infer proficiency, too, but this is still inference, not measurement. No matter how smart the model, it’s still drawing conclusions from indirect data.

By contrast, validated assessments like structured interviews, simulations, and psychometric tools are designed to measure. They observe behaviour against defined criteria, use consistent scoring frameworks (like Behaviourally Anchored Rating Scales, or BARS), and provide a transparent, defensible basis for decision-making. In doing this, the level or proficiency of a skill can be placed on a properly calibrated scale. 

But here’s the thing: we don’t have to choose one over the other.

A Smarter Way Forward: The Hybrid Model

The real opportunity lies in combining the rigour of measurement with the scalability of inference.

Start with measurement
Define the skills that matter. Use structured tools to capture behavioural evidence. Set a clear standard for what good looks like. For example, define Behaviourally Anchored Rating Scales (BARS) when assessing interviews for skills. Using a framework like Sapia.ai’s Competency Framework is critical for defining what you want to measure. 

Layer in inference
Apply AI to scale scoring, add contextual nuance, and detect deeper patterns that human assessors might miss, especially when reviewing large volumes of data.

Anchor the whole system in transparency and validation
Ensure people understand how inferences are made by providing clear explanations. Continuously test for fairness. Keep human oversight in the loop, especially where the stakes are high. More information on ensuring AI systems are transparent can be found in this paper.

This hybrid model respects the strengths and limits of both approaches. It recognises that AI can’t replace human judgement, but it can enhance it. That inference can extend reach, but only measurement can give you higher confidence in the results.

The Bottom Line

Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.

References

Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.