Greg Barton, Deakin University

With 15 civilians and one gunman dead so far, and another 40 people injured, Australia is reeling from its worst act of terrorism on home soil. Two gunmen opened fire on a Jewish community gathering to celebrate the first night of Hanukkah at Archer Park on Sydney’s famous Bondi Beach.

Police have confirmed the two alleged attackers were father and son, aged 50 and 24. The father, Sajid Akram, who was licensed to own six firearms, was shot dead by police. The son, Naveed Akram, remains under police guard in hospital.

Given it was clearly an antisemitic attack, authorities soon after declared it an act of terrorism – that is, an act of politically motivated violence. This designation also gives authorities extra resources in their response and in bringing those responsible to justice.

As Australians try to process their shock and grief, there has been some anger in the community that not enough has been done to protect Jewish Australians from the rising antisemitism evident since the Hamas attack on Israel in October 2023 and the ensuing Gaza war.

What we know about the alleged attackers

ASIO (The Australian Security Intelligence Organisation) Director-General Mike Burgess has said one of the alleged gunmen was “known” to ASIO, though he did not specify which one. Being “known” to authorities can simply mean someone has been associated with networks and communications that have caused concern to authorities. The ABC has reported that Naveed Akram came to the attention of authorities after the arrest of Islamic State Sydney cell leader Isaac El Matari in July 2019.

However, there are hundreds of people who come to authorities’ attention for their contact, online or off, with extremist networks and individuals. With limited resources (and authorities’ resources will always be limited, no matter how much funding they have), they have to run a triage system to assess the threat an individual or a group may pose, and manage the risk as best they can.

They will carefully assess what is being said and the language used, for example, as well as looking at whether a person has a history of violence. As angry and upset as people understandably are in the wake of such a horrific incident, it needs to be recognised that authorities can’t simply arrest everyone who expresses extremist ideas or has passing links with extremist elements.

We still need to know more about this terror attack and the alleged attackers, but to date there has been no evidence of a network in operation. Given the alleged gunmen were father and son, this technically fits the profile of a “lone actor” attack, as we saw in the Lindt Cafe siege in 2014, and Christchurch in 2019.

It is very difficult for authorities to predict and therefore prevent lone actor attacks – by their nature, there’s often no sign beforehand of the potential for violence. And public sites like the reserve at Bondi Beach require extensive resources to police, meaning not all can be adequately secured.

As Burgess pointed out in his annual threat assessment, “our greatest threat remains a lone actor using an easily obtained weapon”. Sadly, that has been shown to be true.

Changing nature of terrorist threat in Australia

There has been much attention in recent years on the rise of far-right extremism and terrorism.

One of the best guides to this is Burgess’ annual threat assessment. In it he explained that a decade ago, just one in ten cases ASIO was following up involved right-wing extremists, with radical Islamist groups occupying most of their attention. However, in recent years the ratio has shifted closer to one in two investigations involving right-wing extremism. In other words, a lot of ASIO’s attention and resources are now necessarily tied up with combating right-wing extremism, especially following the Christchurch terror attack in which 51 people were shot dead by an Australian far-right terrorist during Friday prayers in two New Zealand mosques.

More broadly, Islamic terrorism continues to remain a global threat. IS and Al-Qaeda remain active in the Middle East and increasingly in Africa, as well as central Asia and Afghanistan. Generally, authorities are doing a good job of keeping on top of any threats these networks might pose in Australia.

There is no doubt the general atmosphere between pro-Palestinian and Jewish groups has become far more febrile in the wake of the Hamas attack and the Gaza war. There is a lot of anger and frustration as scenes of violence and suffering are broadcast daily, and we have seen a rise both in antisemitism and Islamophobia since the war began, simply because of the way it plays out in people’s imaginations.

But even in the protests we have seen over many months, the number of people who might use this sentiment to spur violence is small.

Again, there is no evidence the Bondi shooting was part of a wider network, and it is very difficult to stop a lone actor attack on a public site.

In a glimmer of hope, the man whose much-lauded act of heroism in wrestling one of the alleged gunman’s weapons from him has been named as 43-year-old Muslim fruit shop owner Ahmed Al-Ahmed. It is hoped this man’s bravery, which showed us the best of humanity in the midst of the worst, will stop any simplistic analysis of blaming the Muslim community for such violence. We have seen this in the United States, and Australia must do much better.

Has the government done enough?

It is very difficult to keep outdoor public events entirely safe: buildings are relatively easy to secure, but a park at a beach far less so.

The government clearly needs to do more to stop terrorism, and public events are an obvious focus for more resources. No one should be satisfied with where we are right now. It is simply horrifying. But it’s going to take a lot of work to figure out where we can best use resources.

We can’t close every loophole or thwart every risk. We can’t stop people turning to violence, and we can’t police every hateful thought. It has been said this was an attack on all of us, and that’s very true. As the message of Hanukkah inspires us, now is the time when we need to pull together as Australians from all faiths and communities, and work together to ensure that light triumphs over darkness.

Greg Barton, Chair in Global Islamic Politics, Alfred Deakin Institute for Citizenship and Globalisation, Deakin University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Carlos Gershenson, Binghamton University, State University of New York

In the past decade, AI’s success has led to uncurbed enthusiasm and bold claims – even though users frequently experience errors that AI makes. An AI-powered digital assistant can misunderstand someone’s speech in embarrassing ways, a chatbot could hallucinate facts, or, as I experienced, an AI-based navigation tool might even guide drivers through a corn field – all without registering the errors.

People tolerate these mistakes because the technology makes certain tasks more efficient. Increasingly, however, proponents are advocating the use of AI – sometimes with limited human supervision – in fields where mistakes have high cost, such as health care. For example, a bill introduced in the U.S. House of Representatives in early 2025 would allow AI systems to prescribe medications autonomously. Health researchers as well as lawmakers since then have debated whether such prescribing would be feasible or advisable.

How exactly such prescribing would work if this or similar legislation passes remains to be seen. But it raises the stakes for how many errors AI developers can allow their tools to make and what the consequences would be if those tools led to negative outcomes – even patient deaths.

As a researcher studying complex systems, I investigate how different components of a system interact to produce unpredictable outcomes. Part of my work focuses on exploring the limits of science – and, more specifically, of AI.

Over the past 25 years I have worked on projects including traffic light coordination, improving bureaucracies and tax evasion detection. Even when these systems can be highly effective, they are never perfect.

For AI in particular, errors might be an inescapable consequence of how the systems work. My lab’s research suggests that particular properties of the data used to train AI models play a role. This is unlikely to change, regardless of how much time, effort and funding researchers direct at improving AI models.

Nobody – and nothing, not even AI – is perfect

As Alan Turing, considered the father of computer science, once said: “If a machine is expected to be infallible, it cannot also be intelligent.” This is because learning is an essential part of intelligence, and people usually learn from mistakes. I see this tug-of-war between intelligence and infallibility at play in my research.

In a study published in July 2025, my colleagues and I showed that perfectly organizing certain datasets into clear categories may be impossible. In other words, there may be a minimum amount of errors that a given dataset produces, simply because of the fact that elements of many categories overlap. For some datasets – the core underpinning of many AI systems – AI will not perform better than chance.

For example, a model trained on a dataset of millions of dogs that logs only their age, weight and height will probably distinguish Chihuahuas from Great Danes with perfect accuracy. But it may make mistakes in telling apart an Alaskan malamute and a Doberman pinscher, since different individuals of different species might fall within the same age, weight and height ranges.

This categorizing is called classifiability, and my students and I started studying it in 2021. Using data from more than half a million students who attended the Universidad Nacional Autónoma de México between 2008 and 2020, we wanted to solve a seemingly simple problem. Could we use an AI algorithm to predict which students would finish their university degrees on time – that is, within three, four or five years of starting their studies, depending on the major?

We tested several popular algorithms that are used for classification in AI and also developed our own. No algorithm was perfect; the best ones − even one we developed specifically for this task − achieved an accuracy rate of about 80%, meaning that at least 1 in 5 students were misclassified. We realized that many students were identical in terms of grades, age, gender, socioeconomic status and other features – yet some would finish on time, and some would not. Under these circumstances, no algorithm would be able to make perfect predictions.

You might think that more data would improve predictability, but this usually comes with diminishing returns. This means that, for example, for each increase in accuracy of 1%, you might need 100 times the data. Thus, we would never have enough students to significantly improve our model’s performance.

Additionally, many unpredictable turns in lives of students and their families – unemployment, death, pregnancy – might occur after their first year at university, likely affecting whether they finish on time. So even with an infinite number of students, our predictions would still give errors.

The limits of prediction

To put it more generally, what limits prediction is complexity. The word complexity comes from the Latin plexus, which means intertwined. The components that make up a complex system are intertwined, and it’s the interactions between them that determine what happens to them and how they behave.

Thus, studying elements of the system in isolation would probably yield misleading insights about them – as well as about the system as a whole.

Take, for example, a car traveling in a city. Knowing the speed at which it drives, it’s theoretically possible to predict where it will end up at a particular time. But in real traffic, its speed will depend on interactions with other vehicles on the road. Since the details of these interactions emerge in the moment and cannot be known in advance, precisely predicting what happens to the the car is possible only a few minutes into the future.

Not with my health

These same principles apply to prescribing medications. Different conditions and diseases can have the same symptoms, and people with the same condition or disease may exhibit different symptoms. For example, fever can be caused by a respiratory illness or a digestive one. And a cold might cause cough, but not always.

This means that health care datasets have significant overlaps that would prevent AI from being error-free.

Certainly, humans also make errors. But when AI misdiagnoses a patient, as it surely will, the situation falls into a legal limbo. It’s not clear who or what would be responsible if a patient were hurt. Pharmaceutical companies? Software developers? Insurance agencies? Pharmacies?

In many contexts, neither humans nor machines are the best option for a given task. “Centaurs,” or “hybrid intelligence” – that is, a combination of humans and machines – tend to be better than each on their own. A doctor could certainly use AI to decide potential drugs to use for different patients, depending on their medical history, physiological details and genetic makeup. Researchers are already exploring this approach in precision medicine.

But common sense and the precautionary principle
suggest that it is too early for AI to prescribe drugs without human oversight. And the fact that mistakes may be baked into the technology could mean that where human health is at stake, human supervision will always be necessary.

Carlos Gershenson, Professor of Innovation, Binghamton University, State University of New York

This article is republished from The Conversation under a Creative Commons license. Read the original article.