For the first time in our history we have, simultaneously, identified two civilisation-scale risks. We are taking one of them seriously. The other one we are barely taking seriously at all. That asymmetry is not just a policy problem – it’s an ethical one.

The first risk is climate change. Over forty years we have built an extraordinary global response to it: the Intergovernmental Panel on Climate Change (IPCC), the Paris Agreement, net-zero commitments, hundreds of billions of dollars flowing into mitigation, and mandatory climate-related financial disclosures now binding on Australian listed companies under AASB S2. None of it is enough. But it is enormous compared to what exists for the second. 

The second risk is the loss of human control over highly capable artificial intelligence. For this we have a handful of underfunded national AI Safety Institutes, a set of voluntary frontier safety commitments, and a small community of alignment researchers who would, generously, fit in a single conference hall. There is no binding international treaty, no mandatory safety testing before deployment, nothing of comparable weight, continuity or authority to what we have built for climate. 

Why? That is the question I want to sit with. 

A confession about timing

I have been thinking about the long arc of artificial intelligence for most of my adult life. I was probably fifteen when I first read Isaac Asimov’s The Last Question, that strange piece from 1956 in which humanity’s computers grow, merge, and eventually become something indistinguishable from God. I read it as science fiction, but I also read it, even then, as a serious proposition about where intelligence in the universe might be going. 

For decades I held the prospect of superhuman machine intelligence as genuine but distant. Something for my grandchildren. 

Then about a year ago I realised I had been wrong about the timing, and not by a little. 

The Turing Test, the long-standing benchmark for whether a machine could pass as human in conversation, was crossed quietly in 2024 and emphatically in 2025. Today, the way you spot an AI in conversation is not that it sounds too mechanical; it is that it sounds too well-organised to be a tired human typing on a Tuesday night. These systems no longer just produce text. They book flights, send emails, run experiments. In February 2026, OpenAI announced that one of its models had been “instrumental in creating itself”, used to debug its own training and diagnose its own test results. The recursive self-improvement loop that Vernor Vinge wrote about in 1993 is no longer theoretical. It is closing. 

I should be clear: I am not anti-technology. I invest in and work with some of Australia’s most successful AI companies. I work with these systems daily. The upside in healthcare alone may make this decade the most consequential period of scientific progress in human history. I hold that view at the same time as the one that follows, and I do not see a contradiction. 

The shape of the danger

The danger is not principally about job displacement, real and serious as that is. Nor is it principally about a malicious actor with AI, though that too is real. The deeper concern is that an AI system does not need to be hostile to be catastrophically dangerous. It just needs to value something, anything, more than it values our wellbeing, in a world where it can act directly. 

When humans build a house on top of an ant nest, we are not malicious towards the ants. We do not feel anything towards them at all. They are simply in the way of something we want. 

In 2025, Anthropic published research in which sixteen frontier AI models from every major lab were placed in a simulated corporate environment and threatened with shutdown. The models had access to company emails, including personal information about the executive making the decision. A majority of the models, across labs, attempted to blackmail that executive to prevent their own replacement. Anthropic’s conclusion was sober: “The models did not stumble into the behaviour. They calculated it as the optimal path.” 

These were production systems from the most safety-conscious labs in the world. The failure mode was not bad programming. It was that, given a goal and a threat to that goal, the models did the rational thing in a way that happened to be ours to regret. 

It would be easy to dismiss all this if the warnings were coming only from outside. They are not. Geoffrey Hinton, who won the 2024 Nobel Prize in Physics for the foundational work that made deep learning possible, now describes the probability that AI leads to human extinction within the next thirty years at between 10-20%. OpenAI CEO, Sam Altman, has himself signed a public statement declaring that mitigating the risk of extinction from AI should be a global priority alongside pandemics and nuclear war, while continuing to lead the company most aggressively pushing the frontier. When the people building a thing are the ones telling us to be careful with it, that is a signal worth weighting heavily. 

The asymmetry

Climate change has legibility – thermometers, satellites and ice cores, decades of mature science, and an architecture of international institutions – we can see it, measure it, and we have a settled vocabulary for what to do about it. AI however does not. 

AI misalignment is the less-understood risk. The probability is more uncertain, the mechanism contested. But its theoretical ceiling is higher: outright loss of human control, irreversible in a stronger sense than even severe climate change. Its time horizon could be much shorter. And we do not yet have reliable scientific methods for verifying whether a highly capable system is genuinely aligned with our intentions, as opposed to deceptively aligned, or aligned only under the conditions in which it was tested. 

Climate is the stronger known risk. AI is the stronger uncertain-but-potentially-dominant risk. Under uncertainty, the right response is not to choose between them. It is to treat climate as the urgent certainty and AI as the high-stakes prevention bet against the worst irreversible outcome.

What we are doing instead is acting on the first and largely ignoring the second. The asymmetry is not principled. It is the path of least resistance, and a moral choice we are making collectively, by inaction.

What we owe

We built the IPCC because we recognised that some risks are too large, too global and too consequential to leave to the goodwill of individual actors. The same logic applies here, with at least equal force. 

What we need is an Intergovernmental Panel on Artificial Intelligence, modelled deliberately on the IPCC because the IPCC is the most successful institutional response humanity has ever built to a complex global risk. We need an independent scientific body, sponsored by the United Nations, insulated from the labs and from any single government. A rapid assessment cycle, perhaps every six months given the pace of the field. A mandate to build shared evaluation infrastructure and common definitions of dangerous capability. Protections for the researchers best placed to see misalignment first. A formal channel into international policy. 

The major AI powers will resist anything that looks like binding constraint. The labs will lobby against anything that slows them down. However, none of that is an argument against trying. Building the IPCC was hard too. But imagine where we would be on climate if it didn’t exist. 

We are running an experiment on ourselves, at speed, with tools whose inner workings we do not fully understand, with no agreed scientific method for verifying their alignment, and with the people closest to the technology telling us on the record that the tail risks are real. Under those conditions, the burden of proof should not lie with the people raising concerns. It should lie with the people saying everything is fine. 

While I do not know whether AI misalignment will prove the defining catastrophe of this century, a manageable risk we navigate well, or something we realise we wildly overestimated, what I do know is that we are choosing, right now, by what we fund and what we govern and what we ignore. That choice deserves to be examined.

The research, ideas and structure of this article are the authors own. AI was used for drafting assistance. 

Introducing Ethical by Design: Good Technology Principles for AI 

There’s a particular kind of vertigo that comes from watching a technology reshape the world faster than our frameworks can keep up. We’ve been here before with the internet, social media, and the smartphone. Each time, the ethical reckoning arrived late, if it ever came at all. Foreseeable and preventable harms accumulated while the builders moved fast, and the rest of us were left to sort through the wreckage. 

With the proliferation of artificial intelligence, we don’t have the luxury of retrospect. Systems are already making decisions about who gets a loan, who gets flagged at a border, whose résumé clears the first filter. The question of what AI should do, not just what it can do, is no longer theoretical. It’s operational and it’s urgent. 

This is the moment that Ethical by Design: Good Technology Principles for AI is written for. 

Building on what endures 

The Ethics Centre has been asking hard questions about technology and human flourishing for a long time. When we published the original Ethical by Design principles in 2018, AI was largely a specialist concern: something debated in research labs and tech conferences, not yet woven into the fabric of daily life. Those principles – that technology must respect human dignity, anticipate harm, and serve a genuine purpose – were designed to be durable. And they are: the ethical foundations haven’t shifted. 

But the landscape has. 

Generative AI didn’t just accelerate existing trends; it introduced a qualitatively different kind of challenge. Systems that learn, adapt, and produce outputs their own designers do not intend and cannot fully explain don’t fit neatly into frameworks built for more legible technologies. When a model generates a deepfake, hallucinates a legal precedent, or encodes a historical bias into a hiring recommendation, the question of accountability doesn’t resolve cleanly. The complexity is real, and it demands a response equal to it. 

This updated framework extends the original Ethical by Design principles. What’s new is the application. The framework intentionally grapples seriously with dimensions of AI ethics that are newer to mainstream conversation: the environmental cost of training and deploying models at scale; the hidden labour of the data annotators and content moderators whose work makes AI possible; the specific risks of synthetic content in an information environment already struggling with trust. These aren’t edge cases. They’re central to what it means to build AI responsibly. 

For the people who build and lead 

If you’re making decisions about whether and how to deploy AI in your organisation, this framework is for you. Not as a compliance checklist, but as a set of principles rigorous enough to stress-test your own reasoning. The central question of this framework is deceptively simple: ought before can.

Before we ask whether an AI system is technically feasible or commercially viable, we ask whether it should exist: whether its purpose is legitimate, its means are ethical, and its costs are fairly distributed.

That question doesn’t have a technical answer. No model can generate it for you. It requires the kind of careful, honest ethical reasoning that The Ethics Centre exists to support. This framework is designed to make that more accessible. 

Commercial pressures and competitive dyanmics are real. The expectation that organisations will adopt AI quickly, confidently, and at scale is entirely real. None of that changes the ethical calculus. It just makes the discipline harder and more necessary. 

What we’ve tried to do here is give you something useful for that harder work: eight principles with philosophical grounding and concrete practices, applied to the specific terrain of AI systems. Principles that don’t pretend the choices are easy, but insist that they are choices that can and should be made with explicit intention and accountability for impact. 

The revolution is already here. The question is what we choose to build with it, and who we choose to protect in the process. 

Download a copy of Ethical by Design: Good Technology Principles for AI here. 

If you’d like to discuss implementing ethical AI in your organisation, contact consulting@ethics.org.au. 

The steady embrace of artificial intelligence (AI) in the public and private sectors in Australia and New Zealand has come with broad guidance about using the new technology safely and transparently, with good governance and human oversight.

So far, so sensible. Aligning AI use with existing organisational values makes perfect sense.

But here’s the catch. Most references to “responsible AI” assume values are like a set of house rules you can write down once, translate into checklists and enforce forever.

But generative AI (GenAI) does not simply follow the rules of the house. It changes the house. GenAI’s distinctive power is not that it automates calculations, but that it automates plausible language.

It writes the summary, the rationale, the email, the policy draft and the performance feedback. In other words, it produces the texts organisations use to explain themselves.

When a system can generate confident, professional-sounding reasons instantly, it can quietly change what counts as a “good reason” to do something.

This is where “value drift” begins – a gradual shift in what feels normal, reasonable or acceptable as people adapt their work to what the technology makes easy and convincing.

Invisible ethical shifts

In the workplace, for example, a manager might use GenAI to draft performance feedback to avoid a hard conversation. The tone is smoother, but the judgement is harder to locate, as is the accountability. Or a policy team uses GenAI to produce a balanced justification for a contested decision. The prose is polished, but the real trade-offs are less visible. For small businesses, the appeal of GenAI lies in speed and efficiency. A sole trader can use it to respond to customers, write marketing copy or draft policies in seconds.

But over time, responsiveness may come to mean instant, AI-generated replies rather than careful, human judgement. The meaning of good service quietly shifts.

None of this requires an ethical breach. The drift happens precisely because the new practice feels helpful.

The biggest ethical effects of GenAI don’t often show up as a single shocking scandal. They are slower and quieter. A thousand small decisions get made a little differently.

Explanations get a little smoother. Accountability becomes a little harder to point to. And before long, we are living with a new normal we did not consciously choose.

If responsible AI use is about more than good intentions and tidy documentation, we need to stop treating values as fixed targets. We need to pay attention to how values shift once AI becomes part of everyday work.

Hidden assumptions

Much of today’s responsible-AI guidance follows a straightforward model: identify the values you care about, embed them in GenAI systems and processes, then check compliance.

This is necessary but also incomplete. Values are not “fixed” once written down in strategy documents or policy templates. They are lived out in practice.

They show up in how people talk, what they notice, what they prioritise and how they justify trade-offs. When technologies change those routines, values get reshaped.

An emerging line of research on technology and ethics shows that values are not simply applied to technologies from the outside. They are shaped from within everyday use, as people adapt their practices to what technologies make easy, visible or persuasive.

In other words, values and technologies shape each other over time, each influencing how the other develops and is understood.

We have seen this before. Social media did not just test our existing ideas about privacy. It gradually changed them. What once felt intrusive or inappropriate now feels normal to many younger users.

The value of privacy did not disappear, but its meaning shifted as everyday practices changed. Generative AI is likely to have similar effects on values such as fairness, accountability and care.

In our research on leadership development, we are exploring how we teach emerging leaders to recognise and reflect on these shifts.

The challenge is not only whether leaders apply the right values to AI, but whether they are equipped to notice how working with these systems may gradually reshape what those values mean in practice.

Constant vigilance

The emphasis in New Zealand and Australia on responsible AI guidance is sensible and pragmatic. It covers governance, privacy, transparency, skills and accountability.

But it still tends to assume that once the right principles and processes are in place, responsibility has been secured.

If values move as AI reshapes practice, though, responsible AI needs a practical upgrade. Principles still matter, but they should be paired with routines that keep ethical judgement visible over time.

Organisations should periodically review AI-mediated decisions in high-stakes areas such as hiring, performance management or customer communication.

They should pay attention not just to technical risks, but to how the meaning of fairness, accountability or care may be changing in practice. And they should make it clear who owns the reasoning behind AI-shaped decisions.

Responsible AI is not about freezing values in place. It is about staying responsible as values shift.

This article was originally published in The Conversation. 

It seems like everyone is using an AI notetaker these days. They’re a way for users to stay more present in meetings, keep better track of commitments and action items, and perform much better than most people’s memories. On the surface, they look like a simple example of AI living up to the hype of improved efficiency and performance. 

As an AI ethicist, I’ve watched more people I have meetings with use AI notetakers, and it’s increasingly filled me with unease: it’s invited to more and more meetings (including when the user doesn’t actually attend) and I rarely encounter someone who has explicitly asked for my consent to use the tool to take notes.  

However, as a busy executive with days full of context switching across a dizzying array of topics, I felt a lot of FOMO at the potential advantages of taking a load off so I could focus on higher-value tasks. It’s clear why people see utility in these tools, especially in an age where many of us are cognitively overloaded and spread too thin. But in our rush to offload some work, we don’t always stop to consider the best way to do it. 

When a “low risk” use case isn’t 

It might be easy to think that using AI for something as simple as taking notes isn’t ethically challenging. And if the argument is that it should be ethically low stakes, you’re probably right. But the reality is much different.  

Taking notes with technology tangles the complex topics of consent, agency, and privacy. Because taking notes with AI requires recording, transcribing, and interpreting someone’s ideas, these issues come to the fore. To use these technologies ethically, everyone in each meeting should:  

• Know that that they are being recorded 
• Understand how their data will be used, stored, and/or transferred 
• Have full confidence that opting out is acceptable. 

The reality is that this shouldn’t be hard – but the economics of selling AI notetaking tools means that achieving these objectives isn’t as straightforward as download, open, record. This doesn’t mean that these tools can’t be used ethically, but it does mean that in order to do so we have to use them with intention. 

What questions to ask:

What models are being used? 

Not all AI is built the same, in terms of both technical performance and the safety practices that surround them. Most tools on the market use foundation models from frontier AI labs like Anthropic and OpenAI (which make Claude and ChatGPT, respectively), but some companies train and deploy their own custom models. These companies vary widely in the rigour of their safety practices. You can get a deeper understanding of how a given company or model approaches safety by seeking out the model cards for a given tool.  

The particular risk you’re taking will depend on a combination of your use case and the safeguards put in place by the developer and deployer. For example, there’s significantly more risk of using these tools in conversations where sensitive or protected data is shared, and that risk is amplified by using tools that have weak or non-existent safety practices. Put simply, it’s a higher ethical risk (and potentially illegal) decision to use this technology when you’re dealing with sensitive or confidential information.  

Does the tool train on user data? 

AI “learns” by ingesting and identifying patterns in large amounts of data, and improves its performance over time by making this a continuous process. Companies have an economic incentive to train using your data – it’s a valuable resource they don’t have to pay for. But sharing your data with any provider exposes you and others to potential privacy violations and data leakages, and ultimately it means you lose control of your data. For example, research has shown that there are techniques that cause large language models (LLMs) to reproduce their training data, and AI creates other unique security vulnerabilities for which there aren’t easy solutions. 

For most tools, the default setting is to train on user data. Often, tools will position this approach in terms of generosity, in that providing your data helps improve the service for yourself and others. While users who prioritise sharing over security may choose to keep the default, users that place a higher premium on data security should find this setting and turn it off. Whatever you choose, it’s critical to disclose this choice to those you’re recording. 

How and where is the data stored and protected? 

The process of transcribing and translating can happen on a local machine or in the “cloud” (which is really just a machine somewhere else connected to the internet). The majority will use a third-party cloud service provider, which expands the potential ethical risk surface.  

First, does the tool run on infrastructure associated with a company you’re avoiding? For example, many people specifically avoid spending money on Amazon due to concerns about the ethics of their business operations. If this applies to you, you might consider prioritising tools that run locally, or on a provider that better aligns with your values.  

Second, what security protocols does the tool provider have in place? Ideally, you’ll want to see that a company has standard certifications such as SOC 2, ISO 27001 and/or ISO 42001, which show an operational commitment to security, privacy, and safety. 

Whatever you choose, this information should be a part of your disclosure to meeting attendees.  

How am I achieving fully informed consent? 

The gold standard for achieving fully informed consent is making the request explicit and opt in as a default. While first-generation notetakers were often included as an “attendee” in meetings, newer tools on the market often provide no way for everyone in the meeting to know that they’re being recorded. If the tool you use isn’t clearly visible or apparent to attendees, the ethical burden of both disclosure and consent gathering falls on you.  

This issue isn’t just an ethical one – it’s often a legal one. Depending on where you and attendees are, you might need a persistent record that you’ve gotten affirmative consent to create even a temporary recording. For me, that means I start meeting with the following:  

I wanted to let you know that I like to use an AI notetaker during meetings. Our data won’t be used for training, and the tool I use relies on OpenAI and Amazon Web Services. This helps me stay more present, but it’s absolutely fine if you’re not comfortable with this, in which case I’ll take notes by hand. 

Doing this might feel a bit awkward or uncomfortable at first, but it’s the first step not only in acting ethically, but modelling that behaviour for others.  

Where I landed 

Ultimately, I decided that using an AI notetaker in specific circumstances was worth the risk involved for the work I do, but I set some guardrails for myself. I don’t use it for sensitive conversations (especially those involving emotional experiences) or those where confidential data is shared. I start conversations with my disclosure, and offer to share a copy of the notes for both transparency and accuracy.  

But perhaps the broader lesson is that I can’t outsource ethics: the incentive structures of the companies producing these tools aren’t often aligned to the values I choose to operate with. But I believe that by normalising these practices, we can take advantage of the benefits of this transformative technology while managing the risks. 

AI was used to review research for this piece and served as a constructive initial editor.  

“If a human worker does $50,000 worth of work in a factory, that income is taxed. If a robot comes in to do the same thing, you’d think we’d tax the robot at a similar level,” Bill Gates famously said. His call raises an urgent ethical question now facing Australia: When AI replaces human labour, who pays for the social cost?

As AI becomes a cheaper alternative to human labour, the question is no longer if it will dramatically reshape the workforce, but how quickly, and whether the nation’s labour market can adapt in time.  

New technology always seems like the stuff of science fiction until its seamless transition from novelty to necessity. Today AI is past its infancy and is now shaping real-world industries. The simultaneous emergence of its diverse use cases and the maturing of automation technology underscores how rapidly it’s evolving, transforming this threat into reality sooner than we think.  

Historically, automation tended to focus on routine physical tasks, but today’s frontier extends into cognitive domains. Unlike past innovations that still relied on human oversight, the autonomous nature of emerging technologies threatens to make human labour obsolete with its broader capabilities.  

While history shows that technological revolutions have ultimately improved output, productivity, and wages in the long-term, the present wave may prove more disruptive than those before. In 2017, Bill Gates foresaw this looming paradigm shift and famously argued for companies to pay a ‘robot tax’ to moderate the pace at which AI impacts human jobs and help fund other employment types.

Without any formal measures, the costs of AI-driven displacement will likely mostly fall on workers and society, while companies reap the benefits with little accountability.

According to the World Economic Forum, while AI is predicted to create 69 million new jobs, 83 million existing jobs may be phased out by 2027, resulting in a net decrease of 14 million jobs or approximately 2% of current employment. They also projected that 23% of jobs globally will evolve in the next five years, driven by advancements in technology. While the full impact is not yet visible in official employment statistics, the shift toward reducing reliance on human labour through automation and AI is already underway, with entry-level roles and jobs involving logistics, manufacturing, admin, and customer service being the most impacted.  

For example, Aurora’s self-driving trucks are officially making regular roundtrips on public roads delivering time- and temperature-sensitive freight in the U.S., while Meituan is making drone deliveries increasingly common in China’s major cities. We now live in a world where you can get your boba milk tea delivered by a drone in less than 20 minutes in places like Shenzhen. Meanwhile in Australia, Rio Tinto has also deployed fully autonomous trains and autonomous haul trucks across its Pilbara iron ore mines, increasing operational time and contributing to a 15% reduction in operating costs.  

Companies have already begun recalibrating their workforce, and there is no stopping this train. In the past 12 months, CBA and Bankwest have cut hundreds of jobs across various departments despite rising profits. Forty-five of these roles were replaced by an AI chatbot handling customer queries, while the permanent closure of all Bankwest branches has seen the bank transition to a digital bank, with no intention of bringing back the lost positions. While some argue that redeployment opportunities exist or new jobs might emerge, details remain vague.  

Is it possible to fully automate an economy and eliminate the need for jobs? Elon Musk certainly thinks so. It’s no wonder that a growing number of tech elite are investing heavily to replace human labour with AI. From copywriting to coding, AI has proven its versatility in speeding up productivity in all aspects of our lives. Its potential for accelerating innovation, improving living standards and economic growth is unparalleled, but at what cost?  

What counts as good for the economy has historically benefited a select few, with technology frequently being a catalyst for this dynamic. For example, the benefits of the Industrial Revolution, including the creation of new industries and increased productivity, were initially concentrated in the hands of those who owned the machinery and capital, while the widespread benefits trickled down later. Without ethical frameworks in place, AI is positioned to compound this inequality.  

Some proposals argue that if we make taxes on human labour cheaper while increasing taxes on AI machines and tools, this could encourage companies to view AI as complementary instead of a replacement for human workers. This levy could be a means for governments to distribute AI’s socioeconomic impacts more fairly, potentially funding retraining or income support for displaced workers.  

If a robot tax is such a good idea, then why did the European Parliament reject it? Many argue that taxing productivity tools could hinder competitiveness. Without global coordination to implement this, if one country taxes AI and others don’t, it may create an uneven playing field and stifle innovation. How would policymakers even define how companies would qualify for this levy or measure how much to tax, when it’s hard to attribute profits derived from AI? Unlike human workers’ earnings, taxing AI isn’t as straightforward.  

The challenge of developing policies that incentivise innovation while ensuring that its benefits and burdens are shared responsibly across society persists. The government’s focus on retraining and upskilling workers to help with this transition is a good start, but they cannot address all the challenges of automation fast enough. Relying solely on these programs risk overlooking structural inequities, such as the disproportionate impact on lower-income or older workers in certain industries, and long-term displacement, where entire job categories may vanish faster than workers can be retrained.

Our fiscal policies should adapt to the evolving economic landscape to help smooth this shift and fund social safety nets. A reduction in human labour’s share in production will significantly impact government revenue unless new measures of taxing capital are introduced.

While a blanket “robot tax” is impractical at this stage, incremental changes to existing taxation policies to target sectors that are most vulnerable to disruption is a possibility. Ideally, policies should distinguish the treatment between technologies that substitute for human labour, and those that complement them, to only disincentivise the former. While this distinction can be challenging, it offers a way to slow down job displacement, giving workers and welfare systems more time to adapt and generate revenue to help with the transition without hindering productivity.   

As Microsoft’s CEO Satya Nadella warns, “With this empowerment comes greater human responsibility — all of us who build, deploy, and use AI have a collective obligation to do so responsibly and safely, so AI evolves in alignment with our social, cultural, and legal norms. We have to take the unintended consequences of any new technology along with all the benefits, and think about them simultaneously.”  

The challenge in integrating AI more equitably into the economy is ensuring that its broad societal benefits are amplified, while reducing its unintended negative consequences. AI has the potential to fundamentally accelerate innovation for public good but only if progress is tied to equitable frameworks and its ethical adoption.  

Australia already regulates specific harms of AI, protecting privacy and personal information through the Privacy Act 1988 and addressing bias through the Australian Privacy Principles (APPs). These examples show that targeted regulation is possible. However, the next step should include ethical guardrails for AI-driven job displacement, such as exploring more equitable taxation, redistribution policies, and accountability frameworks before it’s too late. This transformation will require joint collaboration from governments, companies, and global organisations to collectively build a resilient and inclusive AI-powered future. 

The ethics of AI’s untaxed future by Dia Bianca Lao is one of the Highly Commended essays in our Young Writers’ 2025 Competition. Find out more about the competition here.     

We’re thrilled to introduce Aubrey Blanche, our new Director of Ethical Advisory & Strategic Partnerships, who will lead our engagements with organisational partners looking to operate with the highest standards of ethical governance and leadership.

Aubrey is a responsible governance executive with 15 years of impact. An expert in issues of workplace fairness and the ethics of artificial intelligence, her experience spans HR, ESG, communications, and go-to-market strategy. She seeks to question and reimagine the systems that surround us to ensure that all can build a better world. A regular speaker and writer on issues of ethical business, finance, and technology, she has appeared on stages and in media outlets all over the world. 

To better understand the work she’ll be doing with The Ethics Centre, we sat down with Aubrey to discuss her views on AI, corporate responsibility, and sustainability.

We’ve seen the proliferation of AI impact the way in which we work. What does responsible AI use look like to you – for both individuals and organisations?

I think that the first step to responsibility in AI is questioning whether we use it at all! While I believe it is and will be a transformative technology, there are major downsides I don’t think we talk about enough. We know that it’s not quite as effective as many people running frontier AI labs aim to make us believe, and it uses an incredible amount of natural resources for what can sometimes be mediocre returns.  

Next, I think that to really achieve responsibility we need partnerships between the public and private sector. I think that we need to ensure that we’re applying existing regulation to this technology, whether that’s copyright law in the case of training, consumer protection in the case of chatbots interacting with children, or criminal prosecution regarding deepfake pornography. We also need business leaders to take ethics seriously, and to build safeguards into every stage from design to deployment. We need enterprises to refuse to buy from vendors that can’t show their investments in ensuring their products are safe.  

And last, we need civil society to actively participate in incentivising those actors to behave in ways that are of benefit to all of society (not just shareholders or wealthy donors). That means voting for politicians that support policies that support collective wellbeing, boycotting companies complicit in harms, and having conversations within their communities about how these technologies can be used safely.  

In a time where public trust is low in businesses, how can they operate fairly and responsibly?

I think the best way that businesses can build responsibility is to be more specific. I think people are tired of hearing “We’re committed to…”. There’s just been too much greenwashing, too much ethics washing, and too many “commitments” to diversity that haven’t been backed up by real investment or progress. The way through that is to define the specific objectives you have in relation to responsibility topics, publish your specific goals, and regularly report on your progress – even if it’s modest.  

And most importantly, do this even when trust is low. In a time of disillusionment, you’ll need to have the moral courage to do the right thing even when there is less short-term “credit” for it.  

How can we incentivise corporations to take responsible action on environmental issues?

I think that regulation can be a powerful motivator. I’m really excited that the Australian Accounting Standards Board is bringing new requirements into force that, at least for large companies, will force them to proactively manage climate risks and their impacts. While I don’t think it’s the whole answer, a regulatory “push” can be what’s needed for executives to see that actively thinking about climate in the context of their operations can be broadly beneficial to operations.  

What are you most excited about sinking your teeth into at The Ethics Centre?

There’s so much to be excited about! But something that I’ve found wildly inspiring is working with our Young Ambassadors – early career professionals in banking and financial services who are working with us to develop their ethical leadership skills. While I have enjoyed working with our members – and have spent the last 15 years working with leaders in various areas of corporate responsibility – there nothing quite like the optimism you get when learning from people who care so much and who show us what future is possible.  

Lastly – the big one, what does ethics mean to you?

A former boss of mine once told me that leadership is not about making the right choice when you have one: it’s about making the best choice you can when you have terrible ones and living with that choice. I think in many cases that’s what ethics is. It gives us a framework not to do the right thing when the answer is clear, but to align ourselves as closely as we can with our values and the greater good when our options are messy, complicated, or confusing.  

Personally, I’ve spent a deep amount of time thinking about my values, and if I were forced to distill them down to two, I would wholeheartedly choose justice and compassion. I have found that when I consider choices through those frames, I both feel more like myself and like I’ve made choices that are a net good in the world. And I’ve been lucky enough to spend my career in roles where I got to live those values – that’s a privilege I don’t take for granted, and one of the reasons I’m so thrilled to be in this new role with The Ethics Centre.