The Day the Algorithm Said “No”

Aisha had done everything right. A senior leader in her industry, she’d built a strong track record - delivering results, developing teams, and leading with integrity. Now, she was in the final stages of securing a high-profile executive role.

Then came the email: “We regret to inform you…”

Confused, she called the recruiter. The response? “The system flagged your application as a poor fit.”

What system? No one could say for sure. Somewhere behind the scenes, an AI-powered hiring algorithm had decided she wasn’t the right candidate.

Aisha pushed for answers. It turned out the algorithm had been trained on past hiring data—favoring certain leadership styles, prioritizing ‘cultural fit’ over diversity, and discounting non-traditional career paths. The result? A powerful but subtle bias that filtered out exactly the kind of future leader the company claimed it wanted.

And yet, no one took responsibility. “It’s just how the system works.”

This is the challenge with AI-driven decision-making. AI doesn’t make decisions—people do. And when accountability isn’t clear, unintentional biases and unintended consequences take hold.

Bias, AI, and International Women’s Day: Why This Matters Now

As we mark International Women’s Day, this isn’t just an AI challenge—it’s a workplace diversity challenge. Women, particularly those from underrepresented backgrounds, already face systemic barriers in hiring, promotions, and leadership.

AI should be a tool for progress, but it will only be as fair, inclusive, and effective as the leaders who design and govern it. This is where leaders can step up. By ensuring AI is used to unlock potential - not reinforce past biases - leaders can create stronger, more diverse, and more future-fit organizations.

And this isn’t about getting it perfect - it’s about getting it right. Accountability Done Right.

AI and Accountability

Kevin Werbach’s Knowledge at Wharton podcast highlights why accountability in AI development and governance is critical. But the real issue isn’t AI—it’s how we lead in an increasingly complex world.

AI is only as good as the decisions that shape it. And that means leaders have an opportunity - to set the standards, ask the right questions, and make sure their AI tools reflect the values and outcomes they actually want.

Accountability Done Right principles apply here too:

1. Accountability must be proactive, not reactive – AI governance needs to be built in from the start, just like a strong leadership culture.
2. Transparency builds trust – If people don’t understand how AI makes decisions, how can they trust it? The same goes for leadership.
3. Ethical leadership is non-negotiable – AI reflects the values embedded in it. Leadership decisions do the same.

The Leadership Imperative: AI Won’t Save Us from Accountability

One of the biggest myths about AI is that it removes human responsibility. It doesn’t. If anything, it raises the stakes.

If you want to lead well in a world of AI-driven complexity, ask yourself:

-?????? Where am I outsourcing responsibility?

-?????? Where am I tolerating ambiguity instead of requiring clarity?

-?????? How am I ensuring my decisions—human-led or AI-assisted—align with my values?

AI accountability is leadership accountability. The leaders who thrive will be the ones who lean in, ask better questions, and take accountability for the systems shaping their teams and organizations.

Your Move

AI isn’t going away. And neither is the need for leaders who do accountability right.

Where will you start? Let's talk.

#Leadership #AccountabilityDoneRight #AI #ResponsibleAI #Diversity #WomenInLeadership #IWD2025