Aubrey Blanche

Organizations everywhere are racing to automate cognitive work with AI, and most of the conversation sounds like either breathless optimism or dystopian panic. Neither is particularly useful — and neither helps teams actually harness AI for meaningful transformation.

The harder questions are the ones we're not yet asking loudly enough.

• What happens to creativity and professional judgment when expertise gets encoded into a model?
• How do we build the conditions where AI genuinely amplifies human capability rather than quietly eroding it?

Because here's the thing: the organizations that will get the most from AI aren't the ones moving fastest. They're the ones thinking clearly about which cognitive work to automate, how to preserve the human judgment that makes teams exceptional, and who gets a say in shaping that future.

This session gives you a more honest map of the ethical terrain — one that takes human dignity and creativity as seriously, and directly questions whether efficiency is a justifiable goal (or just one method of many to achieve more worthy outcomes).

Because building powerful technology and building it well aren't in conflict.

But that only happens if we're willing to ask the uncomfortable questions before the decisions are already made.

We're told AI notetakers are a productivity win — a way to stay present, capture commitments, and finally escape the cognitive overload of back-to-back meetings.

And honestly? The appeal is real.

But here's what we're not talking about: every time someone hits "record," they're making a decision that affects everyone in that conversation — usually without asking them first.

As an AI ethicist and busy executive who felt the pull of this technology herself, Aubrey Blanche takes us through what it actually looks like to use AI tools responsibly in the workplace. This isn't a talk about banning technology or moral panic — it's about the gap between "low risk" and actually thinking it through.

In this session, Aubrey breaks down the questions leaders and employees alike should be asking:

• What models power the tools we're using, and how safe are they?
• Is our data being used to train AI systems without our knowledge?
• Where does our most sensitive conversation data actually live?
• And fundamentally — have we ever genuinely asked the people we're recording for their consent?

Drawing on real-world practice and her own experience finding an ethical path forward, Aubrey makes the case that good intentions aren't enough — and that the companies selling us these tools don't always share our values.

The good news?

Ethical AI use at work isn't complicated. It just requires intention, transparency, and the willingness to have a slightly awkward sentence at the start of a meeting.

You can't outsource ethics. But you can learn to practice it — and model it for everyone around you.

Hiring is one of the highest-stakes decisions an organization makes — and increasingly, AI is in the room when we make it. From résumé screening to candidate scoring to interview analysis, the tools promising to make recruiting faster and more objective are everywhere. But faster isn't the same as fairer. And "objective" is often just bias with better branding.

As someone who has spent her career at the intersection of equity, technology, and organizational design, Aubrey Blanche has watched HR and recruiting become one of the most consequential — and under-examined — frontiers of AI ethics. This session is the foundation she wishes every people professional had before they opened a vendor demo.

Aubrey walks through what HR and recruiting teams actually need to understand:

• how AI systems learn from historical data that was never neutral to begin with
• why "validated" tools can still produce discriminatory outcomes
• what questions to ask vendors before you ever sign a contract
• how to build internal practices that centre the dignity and agency of candidates — not just the efficiency of your pipeline.

This isn't a session about being anti-technology. It's about being pro-accountability. Because the HR professionals in this room are often the last line of defence between a flawed algorithm and a real person's livelihood.

That's not a small thing. That's the whole thing.

You'll leave with a practical ethical framework for evaluating AI tools, a set of questions to pressure-test any vendor's claims, and the confidence to push back when something doesn't feel right — even when the ROI deck looks compelling.

Responsible AI isn't culturally neutral. American AI development embeds distinctly American values—individual liberty, technological solutionism, and winner-takes-all competition.

But what happens when these values clash with Australian cultural principles that prioritize collective welfare, egalitarianism, and the "fair go"?

Current AI governance frameworks—largely imported from Silicon Valley—often perpetuate values misaligned with Australian regulatory expectations and social norms.

The Mathpath and recovering American Aubrey Blanche draws on principles of mateship, pragmatic skepticism, and community-oriented thinking, in this presentation that introduces a distinctly Australian responsibility framework for AI implementation.

Rather than treating AI risks as individual consumer choices or market failures, this framework positions AI governance as a collective responsibility—where technology serves the common good, ensures equitable access, and earns trust through demonstrated fairness rather than assumed benevolence.

How many organizations, processes, and products are poorly designed or produce real-world harm because the right voices weren’t let into the room where the building…happens? How much money has been spent on “diversity & inclusion” while companies remain homogenous and underrepresented employees struggle to thrive?

Funding external partnerships and PR don’t address daily microaggressions underrepresented employees face, while unconscious bias training has been shown to potentially increase bias in the workplace. An almost singular focus on women in the workplace has left women of color behind, erased the experience of people with disabilities, and left almost everyone with “diversity fatigue”. Progressive business leaders are questioning many traditional “best practices” that haven’t shown to have a positive impact on employees, and are building new, data-informed, and intersectional strategies to truly move the needle. Join The Mathpath Aubrey Blanche, Director of Equitable Design & Impact at Culture Amp, to learn how you can evolve your programs to create fair experiences for every employee and build better, safer products.