⬇️ Inside this issue:

• Ways to eliminate bias in badge creation

• Natalie shares what it’s like being at Badge Summit as a newbie

• NY State Bar weighs in: The Threat of Fake Job Candidates

INTERESTING READS

🎯 Skills-based hiring is changing how companies write job postings and find talent.

🤖 AI agents may be exposing your team's biggest workplace habits, for better or worse.

🎨 California is betting on paid apprenticeships to solve its arts teacher shortage.

🧰 Looking to level up as a manager? These 15 leadership tools are worth adding to your toolkit.

ARTICLE

How to check yourself when it comes to bias and badges

Digital badges are supposed to make opportunity more equitable.

But the language, technology, assessments, and decisions behind them can quietly introduce the same bias they were meant to remove.

That does not mean badges are the problem. It means we need to be more thoughtful about how we create and evaluate them.

Bias can show up in the title, the metadata, the imagery, the career recommendations, the assessment, or even in deciding which badges get created in the first place.

So before you publish another credential, here are a few ways to check yourself.

Look beyond the badge image

Bias can enter long before a badge is issued.

Review the title, description, criteria, metadata, colors, symbols, and imagery. Ask whether the language makes assumptions about who the learner is, what kind of work they should pursue, or what a “successful” earner looks like.

And no, inclusion is not as simple as dropping a diverse stock image into Canva and calling it a day.

Check whether the badge is accessible, whether alt text is included, whether the font is readable, and whether learners can understand the credential in the languages they actually use.

Most importantly, involve the people the credential is meant to serve. A badge created without their input will almost always be built on assumptions.

Question automated recommendations

Many credentialing platforms use AI, algorithms, or taxonomies to connect badges with skills, occupations, and learning opportunities.

These features can be useful. They can also produce recommendations based on biased historical data or identity-related language.

Test them before publishing.

Create a duplicate badge and change one variable at a time. Remove an identity term. Change the organization name. Replace the image. Adjust the description.

Then compare the results.

If a small change creates a dramatically different occupation or skill recommendation, investigate it. Ask the provider how the feature works, document the output, and turn it off when necessary.

“The algorithm did it” is not a quality-control policy.

Examine who gets to make decisions

Bias can also enter through gatekeeping.

Write down who approved the badge, who defined the skill, and who was missing from the room.

Who decides which credentials are funded? Which skills are considered valuable? Which learners are included? Which departments are allowed to create badges?

Bring in learners, employers, accessibility specialists, instructors, community partners, and people from the groups the credential is designed to support.

And do it early. Asking for feedback after every major decision has already been made is not meaningful participation.

Make sure the assessment fits the skill

A badge is only as credible as the assessment behind it.

Ask whether learners can demonstrate the skill in more than one valid way. Look for cultural assumptions, unnecessary writing requirements, inaccessible formats, and one narrow idea of what competence looks like.

Do not accidentally test academic writing when the badge is supposed to measure project management, leadership, or technical ability.

The assessment should measure the skill named on the badge. Nothing extra.

Check whether the badge creates real value

A badge may be affordable, accessible, and beautifully designed and still offer little value if employers do not understand it.

Ask employers whether the skill matters, whether their systems can read the credential, and whether hiring teams know how to verify it.

Also explain the badge clearly to learners. Tell them what it represents, how to accept it, where to share it, and how an employer can verify it.

Digital access does not automatically equal digital understanding.

Keep a human in the loop

No checklist will eliminate every form of bias.

The goal is to build questioning and review into the process. Test the output. Invite different perspectives. Correct problems before they affect learners.

Before publishing a badge, recommendation, or career match, ask:

Could we explain this result directly to the learner without making excuses for it?

If the answer is no, the badge, recommendation, or system is not ready yet.

EVENTS

What it was like as a first-timer at Badge Summit — having never been in an LER room before

I grew up in Lennox, California, a small, working-class pocket of LA right under the airport flight path. My mom raised me mostly on her own, and I proudly watched her finish her BA while I was still in high school.

College felt inevitable after that.

I went straight from high school to Cal State Fullerton, then lost the thread: changed majors, picked up a job at Costco, worked my way up to supervisor. I went back to school twice more — an AA in marketing from El Camino Community College, then a transfer to Cal State LA for finance. In 2021 I had my daughter and moved to Phoenix for my husband's job, and most of my finance credits didn't transfer to ASU. I switched majors again, to whatever would actually transfer, and stepped away from school twice more as we had two more kids. It wasn't until recently that I found out I have ADHD, which explained a lot of the trouble I'd had staying focused, trouble I used to quietly blame on myself.

Along the way, I picked up Coursera courses, Udemy certificates, even a free Harvard coding class. None of it shows up on a transcript. There's no single place that tells the whole story of what I actually know how to do.

Then I became the case study

So when I showed up at Badge Summit as an intern for The Conquer Company, I planned to sit quietly and absorb sessions about "LERs" (Learner Employment Records — a term most people have never heard). In the first collaborative session, before we began the discussions, my boss turned to the room and said: this is Natalie, she's the learner we've been describing this whole time.

It was strange, being turned into an example mid-sentence like that.

But it was also the first time a room of people from state credentialing offices, universities, and ed-tech platforms actually seemed to listen to what it's like on the other side of the systems they build.

The energy in the room

The summit exceeded what I expected. States like Colorado and Alabama are each pulling their certificates and platforms into one unified system, instead of leaving everyone to run their own competing app. People talked about storytelling instead of "communication." Nobody rushed to a clean answer on hard questions. It didn't feel like a room selling software. It felt like a room that cared about getting this right.

The best conversation happened at lunch

Walking to lunch, a guy I'd just met asked quietly insightful questions about learning itself. We landed on this: learning never stops. Even someone with a PhD is still catching up to a field that moved on while they wrote their dissertation. That took the shame out of my stop-and-start path, out of years spent comparing myself to people who finished in four straight years. There's no line.

That's also where the harder question came up, and I haven't stopped thinking about it: do LERs, digital badges, verifiable credentials actually turn into something financial for someone? Does it move the needle on a paycheck or a job offer?

What the industry's doing right

Trevor Cox and Cami Cooper, from the University of Central Oklahoma, showed the room an uncomfortable finding: an AI feature on a client’s badge platform had mapped a Hispanic women's business badge to healthcare occupations, a stereotype baked into the tech, and they built a session around it instead of hiding it. Javier Motta-Mena of Learning Mechanics built simulations so instructors could feel what it's like to be asked to read aloud when you're dyslexic. Simone Ravaioli of Instructure pushed the room to question whether "evidence" and "assessment" even make sense outside a Western framework.

All three were willing to be uncomfortable in public, in front of their peers, to get closer to the truth.

Where it could do better

Nobody fully answered how a badge earned outside the traditional four-year path, while working, while raising kids, converts into real, spendable value. There was plenty of talk about trust and verification, less about connecting those ideas to a raise, a promotion, a job offer. Maybe that’s because it’s obvious to the experts, or maybe it’s because it’s still being answered.

I'd also love to see more learners in the room. I was one of the only actual learners in most sessions I sat in; everyone else represented an institution or a platform. Work like this report from Education Strategy Group and Third Sector, built off fifty stakeholder interviews, is genuinely important, and it did include learner voices through focus groups. I just think it gets even stronger the more learners are in the room for the whole process, not only interviewed at the start of it. There's something about being there in real time, reacting as ideas take shape, that a single conversation months earlier can't fully capture.

What I'd ask industry insiders to remember

Talk to an actual learner before you talk about one. Not a persona from a research study, a person currently living inside a system that isn't built for them yet. Most learners are beginners in some part of their journey, still figuring out which classes count, which platform to trust. That's not a flaw to design around. It's the condition most people are in.

I left with a folder full of new vocabulary, LER, Open Badges 3.0, interoperability, and one clear feeling: the problem of a mom from Lennox trying to prove, in one place, everything she actually knows how to do isn't solved yet, but there's a whole room of smart, caring people who've been working on it for years, and it was my first time getting to sit in that room and learn about it.

That's what genuinely excites me: an entire industry organizing itself around the idea that you shouldn't need a fancy degree from a fancy school to prove what you're capable of.

Somewhere out there is another version of me stacking Coursera certificates and community college credits between shifts and kids, and if this industry keeps at it, her scattered trail of learning won't get erased every time she moves states or switches majors.

It'll just be hers, portable, provable, and enough to get her in the door.

BY THE NUMBERS

The happiest jobs have one thing in common

Medical professionals report 85%+ job satisfaction, making healthcare one of the most satisfying career fields. Across the highest-rated professions, one pattern stands out: jobs with direct human impact, strong interpersonal connection, and visible outcomes consistently rank the highest.

_{Source: Job Satisfaction Statistic by Profession [2026]}

KNOWLEDGE

What to read, watch, and listen to this weekend

📚 Read: Addressing the Threat of Fake Job Candidates by the New York State Bar Association

AI is making it easier than ever to fake a resume, an interview, or even an entire candidate. This article explores the growing challenge of fraudulent applicants and why employers are rethinking how they verify identity, credentials, and skills during the hiring process.

📺 Watch: American Factory on Netflix

If you haven't seen this Oscar-winning documentary yet, add it to your list. American Factory follows the reopening of a former GM plant under Chinese ownership and offers a fascinating look at culture, labor, leadership, and the future of manufacturing. You'll walk away thinking differently about the people, systems, and values that shape the modern workplace.

🎧 Listen: The Data Is In on Work From Home from The Economics of Everyday Things

The debate over remote work isn't slowing down, but the data is getting clearer. This episode unpacks what researchers are learning about productivity, employee preferences, and why the future of work may be more nuanced than either side of the debate suggests.

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