Workplace Fairness: Why Good Intentions Aren’t Enough

We've spent years talking about diversity, equity, inclusion, belonging, unconscious bias, representation, and workplace culture.

Organizations have launched initiatives.

Employees have attended training.

Leaders have made commitments.

Companies have issued statements.

And yet a much harder question remains:

Did any of it actually make the workplace fairer?

That's the question at the center of my conversation with strategist, consultant, and author Lily Zheng, whose book Fixing Fairness: 4 Tenets to Transform Diversity Backlash into Progress for All challenges some of the fundamental assumptions behind traditional workplace fairness efforts.

Lily isn't arguing that fairness doesn't matter.

She's arguing that if fairness matters, we should be able to tell whether we're actually creating it.

And that changes the conversation.

Good Intentions Aren't Outcomes

One of the biggest problems with traditional workplace initiatives is that we often measure what organizations do rather than what actually changes.

How many people completed the training?

How many employee resource groups were created?

How many programs were launched?

How much money was invested?

Those numbers tell us something about activity.

They don't necessarily tell us anything about impact.

Did hiring become fairer?

Did promotion gaps shrink?

Did employees have more equitable access to opportunity?

Did people experience greater transparency?

Did workplace outcomes actually improve?

Those are much harder questions.

But they're also the questions that matter.

Are We Afraid to Find Out?

Lily describes something particularly interesting in our conversation: "Fear of Finding Out."

Because measurement creates accountability.

If we don't define what success looks like, we can continue believing an initiative is working.

Once we measure outcomes, we may discover something uncomfortable.

The program we invested in didn't work.

The training didn't change behavior.

The hiring process still produces inequitable results.

Or, perhaps even more concerning, something designed to improve fairness actually made the problem worse.

That's where another important concept enters the conversation.

The Cobra Effect: When Good Intentions Backfire

The Cobra Effect describes what happens when an attempted solution creates unintended consequences that make the original problem worse.

And workplaces aren't immune.

An organization identifies a problem.

A program is created.

People participate.

Leadership checks the box.

But no one goes back and asks:

Did this intervention actually solve the problem?

That distinction matters enormously.

Because when we become emotionally attached to a solution because its intentions are good, we can become reluctant to examine whether the results are good too.

Good intentions should be the beginning of the work.

Not the measurement of whether the work succeeded.

The Limits of Bias Training

Unconscious bias training is a useful example.

For years, organizations have invested significant time and money teaching employees about bias.

The underlying assumption is understandable:

If people recognize their biases, they'll make fairer decisions.

But Lily challenges us to look deeper.

What if we designed systems that made it harder for individual bias to influence outcomes in the first place?

Instead of asking every individual to become perfectly unbiased, we could examine the structure around the decision.

How are candidates evaluated?

Who decides who advances?

Are criteria clearly defined?

Are decisions documented?

Can outcomes be measured?

That moves us from trying to perfect human beings to designing better systems.

"Hire the Best Candidate" Sounds Simple. It Isn't.

Almost every organization would say it wants to hire the best person for the job.

But who determines what "best" means?

And how?

Hiring decisions are often influenced by loosely defined ideas such as:

"They'd be a great fit."

"I really connected with them."

"They reminded me of someone who's successful here."

Those assessments may feel objective.

They're often anything but.

Lily discusses research around hiring discrimination and how more centralized and structured hiring practices can reduce opportunities for bias to influence decisions.

The lesson isn't simply about diversity.

It's about better decision-making.

If an organization wants the best talent, shouldn't it build a hiring system capable of identifying that talent as consistently and fairly as possible?

What Comes After DEI?

This conversation arrives during a complicated moment.

The language surrounding DEI has become increasingly polarized.

Some organizations have changed programs.

Others have changed terminology.

Some have reduced their efforts altogether.

But Lily raises a much more useful question than whether the acronym DEI survives:

What problems were we trying to solve in the first place?

Were we trying to create fair hiring?

Better access to opportunity?

More transparent promotion decisions?

Workplaces where people could contribute effectively?

Systems that produced better outcomes?

If those problems still exist, changing the language doesn't eliminate the need to solve them.

Perhaps this moment gives organizations an opportunity to stop defending programs and start examining outcomes.

From DEI to FAIR

Lily's work introduces the FAIR framework, a systems-oriented approach to workplace fairness built around four tenets:

Fairness

Access

Inclusion

Representation

What's compelling about the framework isn't simply the terminology.

It's the shift in orientation.

Rather than starting with:

"What DEI initiative should we implement?"

Organizations can start with:

"What outcome are we trying to create?"

Then they can design systems, processes, measurements, and accountability around achieving it.

That's a very different way of approaching organizational change.

Systems Matter More Than Self-Help

This idea extends far beyond DEI.

We have a tendency in workplaces to turn systemic problems into individual responsibilities.

Employees need more resilience.

Managers need more training.

Women need more confidence.

Candidates need better interviewing skills.

Employees need to understand their biases.

Sometimes those things help.

But sometimes the system itself is producing the outcome.

And no amount of self-improvement can compensate for a poorly designed system.

If promotion criteria are unclear, telling employees to advocate for themselves won't create transparency.

If hiring processes are inconsistent, another bias workshop won't necessarily create fairness.

If access to opportunities depends primarily on who someone knows, encouraging people to "network more" doesn't address the underlying structure.

Eventually, organizations have to examine the architecture of work itself.

Fairness Doesn't Have to Be a Zero-Sum Game

One of the most important ideas in Lily's work is that fairness shouldn't require creating winners and losers.

That's especially important in a climate where workplace fairness efforts can quickly become framed as one group gaining something at another group's expense.

Better systems can benefit everyone.

Clearer hiring criteria help every candidate understand expectations.

Transparent promotion processes help every employee understand how advancement works.

Consistent performance standards help every manager make better decisions.

Accessible opportunities expand the talent organizations can reach.

Accountability helps everyone trust the system.

That's the opportunity:

Move away from blame and shame and toward solutions people can see themselves benefiting from.

Fairness Is a Design Problem

This may be the idea I kept coming back to after my conversation with Lily.

What if we stopped treating workplace fairness primarily as a values statement and started treating it as a design challenge?

Values still matter.

Intentions still matter.

Leadership still matters.

But systems determine what happens when those intentions meet reality.

And systems can be examined.

Measured.

Redesigned.

Tested.

Improved.

That's incredibly hopeful because it means workplace fairness isn't an unsolvable cultural problem.

It's something organizations can actually work on.

AI Will Multiply Whatever Systems We Give It

There's another reason this conversation matters right now.

Artificial intelligence is rapidly becoming embedded in hiring, performance management, workforce planning, employee development, and organizational decision-making.

AI doesn't arrive in a vacuum.

It operates within the systems we create.

That means technology can become an extraordinary multiplier.

But the question is:

What exactly are we multiplying?

If the underlying system is fair, transparent, and well-designed, technology may help scale those strengths.

If the underlying system contains inequities, inconsistencies, or poorly defined decision criteria, AI may allow us to reproduce those problems faster and at greater scale.

The future of fairness and the future of AI aren't separate conversations.

Increasingly, they're the same conversation.

Stop Asking Whether the Initiative Sounds Good

Maybe organizations need to start asking a different set of questions.

Not:

"Did we launch the program?"

But:

Did anything change?

Not:

"Did everyone complete the training?"

But:

Did decisions improve?

Not:

"Do we have a DEI strategy?"

But:

Do employees experience a fair workplace?

And perhaps most importantly:

How would we know?

Because if we can't answer that question, we may be measuring our intentions instead of our impact.

The Future of Workplace Fairness

The DEI debate has become loud.

Often political.

Sometimes deeply personal.

But underneath all of that noise is a remarkably practical question:

Can we build workplaces where people have fair access to opportunity and where decisions are transparent, consistent, and accountable?

Lily's answer is yes.

But getting there may require letting go of some of the approaches we've become comfortable defending.

It requires measurement.

Better systems.

Stronger coalitions.

Transparency.

Accountability.

And the willingness to discover that something we believed was helping may not actually be working.

That's not failure.

That's information.

And information gives us the opportunity to build something better.

Because ultimately, workplace fairness shouldn't be judged by how strongly we believe in it.

It should be judged by whether people actually experience it.

🎙 Listen to the Conversation with Lily Zheng

In this episode of the Her Resources Podcast, Lily Zheng and I go deeper into Fixing Fairness, the FAIR framework, unconscious bias, hiring discrimination, DEI backlash, the Cobra Effect, AI, and what leaders can do now to build workplaces that are more transparent, accountable, and fair.

Explore Lily's work:
LilyZheng.co

Learn about the FAIR Framework:
The FAIR Framework

Explore Fixing Fairness and Lily's other books:
Lily Zheng — Authorship

Take the workplace diagnostic:
How FAIR Is Your Workplace?

Read Lily's Harvard Business Review article:
What Comes After DEI?

If this conversation challenged the way you think about fairness, share it with a leader who needs to hear it and subscribe to the Her Resources Podcast for more conversations about leadership, work, systems, and the forces reshaping our workplaces.

Because the future isn't something we inherit. It's something we create.