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We like to believe our decisions are our own 鈥� shaped by our values, interests and lived experience. But artificial intelligence is beginning to influence many of the choices we think we make independently.
In , we were joined by Professor Billy Sung to explore how this shift to AI decision-making is unfolding in practice 鈥� how much we should trust it and why being human still matters.
Below is just a selection of insights from the discussion. You can listen to the full episode, , on Apple Podcasts, Spotify and more.
馃帶 Insight one: Predictive AI already shapes decisions 鈥� often without people realising
When people talk about artificial intelligence today, they鈥檙e often referring to tools like ChatGPT. But that framing misses a much bigger picture.
Q. Billy, what do we mean when we鈥檙e talking about AI?
Billy: 鈥淎rtificial intelligence, or AI, is actually everywhere. But the rise of tools like ChatGPT 鈥� currently the largest consumer-facing generative AI platform 鈥� has led to a widespread generalisation of what AI is and how it works.
鈥淔or many people, AI has become shorthand for generative AI. In reality, predictive AI has been embedded across society for years, well before we used generative tools like ChatGPT, Claude, BART or Gemini.
鈥淪o, for instance, Google Maps uses AI. When you enter a destination, the system draws on traffic data, real-time conditions and historical patterns to predict the fastest route. That process 鈥� using data to predict an outcome 鈥� is artificial intelligence at work.
鈥淎t its core, AI is not 鈥榠ntelligence鈥� but instead it鈥檚 a system designed to use data to better predict a particular goal or outcome.鈥�
鈥淭here are different types of AI that serve different purposes. Predictive AI forecasts outcomes, such as routes, recommendations or demand. Generative AI produces new content, whether that鈥檚 text, images or audio.
鈥淏eyond these visible tools, much of AI actually operates behind the scenes. Recommendation systems, such as those used by streaming platforms, are another long-standing example of artificial intelligence shaping everyday experiences.
鈥淚n general, AI is influencing decisions everywhere. It鈥檚 really about prediction: anticipating outcomes and guiding decisions toward a goal.鈥�
This shift in visibility matters 鈥� because once AI moves from the background to centre stage, expectations around trust and responsibility change.
馃帶 Insight two: We can (sometimes) trust (some of) AI鈥檚 decisions
Trust isn鈥檛 simple. With AI it depends on what the system is being asked to do, where it鈥檚 deployed and how much data it has to learn from.
Q. Can we trust AI?
Billy: 鈥淲hether we can really trust AI is a multi-billion-dollar question. And the answer depends on what kind of task the system is being asked to perform.
鈥淢any of today鈥檚 AI systems 鈥� particularly recommendation engines 鈥� are highly developed. Platforms like Netflix, search engines like Google, and e-commerce sites like Amazon rely on models trained on vast amounts of behavioural data to predict what users are most likely to watch, click or buy next.
鈥淚n marketing and consumer psychology, it鈥檚 well established that people鈥檚 choices can be predicted to a certain extent 鈥� not 100 per cent. AI systems can identify patterns that suggest which product, brand or option a person is more likely to choose based on past behaviour and the behaviour of similar users.
鈥淚n these consumer contexts, AI is doing what it does best: using existing data to predict a likely outcome.
鈥淧roblems arise when AI is asked to predict outcomes that are fundamentally unpredictable.
鈥淎 lottery is a useful example. Even if an AI system were allowed to generate lottery numbers, the output would still be meaningless 鈥� because the numbers are random. In those cases, trust is misplaced because prediction is impossible.
鈥淪o, whether we can trust AI鈥檚 decisions and predictions comes down to the model, the data it has access to, and the environment it operates in.鈥�
鈥淲ithout sufficient context, AI doesn鈥檛 fail dramatically. It fails quietly 鈥� by making plausible but suboptimal recommendations.
鈥淔rom a practical standpoint, current AI systems are best understood as partial contributors, not decision-makers. They can often deliver 50 to 60 per cent of what a person is looking for 鈥� surfacing options, narrowing choices, and processing information at scale.
鈥淏ut a human still needs to remain in the loop, crafting prompts, interpreting outputs and applying judgement.鈥�
Don鈥檛 miss out on the insights.
馃帶 Insight three: Trust in AI will grow where platforms are reliable, transparent and fair
As AI systems become more embedded in everyday decision-making, will users, industries and institutions come to trust AI more or less?
Q. What do you think will determine whether trust in generative AI rises or falls in the future?
Billy: 鈥淭his is now a rapidly growing field of study. Academic research into AI trust has grown significantly in recent years.鈥�
鈥淎cross literature, the same framework appears again and again: reliability, transparency and fairness.鈥�
鈥淩eliability is the most basic requirement for trust. At a technical level, this refers to the accuracy and precision of an AI system鈥檚 predictions. Can it consistently produce outcomes that align with real-world behaviour?
鈥淧eople also want to understand how an AI system arrived at a particular recommendation or output.
鈥淭his is where transparency 鈥� often called explainability in the technical world 鈥� becomes critical. Explainability refers to whether an AI system can communicate the reasoning behind its outputs in a way that humans can understand.
鈥淩esearch published in leading academic journals actually shows that when systems provide clear explanations for why a recommendation was made, user acceptance can increase by 40 to 50 per cent. In other words, people are far more willing to trust AI when they can see the logic behind it.
鈥淭he third pillar of trust is fairness 鈥� not just in terms of access to AI, but in how decisions are shaped behind the scenes.
鈥淔airness raises ethical questions about whose interests an AI system ultimately serves. This becomes particularly important as advertising and commercial incentives increasingly intersect with generative AI platforms.
Is it possible to still trust conversational AI to be fair when responses contain advertising?
馃帶 Insight four: The coming shift is 鈥渟hared agency鈥� with co-created human鈥揂I decision-making
As AI systems move beyond isolated tools and into everyday workflows, the future of decision-making is less about automation and more about how human-machine connection.
Q. What do you think is the most likely future for human-machine decision-making?
Billy: 鈥淭he most likely future of human鈥揂I decision-making isn鈥檛 full automation 鈥� and it isn鈥檛 humans handing over control. Instead, it鈥檚 what researchers describe as shared agency: a co-created decision-making process where humans and AI each play distinct roles.
鈥淲e already share decisions with AI 鈥� through search engines, recommendation platforms, navigation tools and conversational AI. What鈥檚 changing is not whether AI is involved, but how deeply it becomes embedded across the decision journey.
鈥淩ather than acting as a decision-maker, AI increasingly functions as a decision assistant 鈥� narrowing options, surfacing patterns, and reducing cognitive load 鈥� while humans retain responsibility for the final choice.鈥�
鈥淐onsider a near-future version of a familiar decision: buying a car. Before visiting a dealership, a buyer might consult AI to clarify their needs 鈥� price range, vehicle type or key features 鈥� and quickly narrow the field. AI doesn鈥檛 make the decision, but it shapes the consideration set by filtering options, comparing models and summarising large volumes of review data.
鈥淭he appeal of shared agency is efficiency. AI excels at processing scale: hundreds of documents, thousands of reviews, years of behavioural data.
鈥淥ver the next five to six years, this pattern is expected to expand across everyday decisions.
鈥淭he critical distinction is that shared agency preserves human accountability.鈥�
For the immediate future, important decisions will remain human 鈥� even when informed by machines.鈥�
馃帶 AI-generated content in practice: the AI podcast case study
One of the clearest ways to understand both the potential and the limits of AI is to look at how it鈥檚 being used in practice. Billy鈥檚 is a fully AI-generated production 鈥� and a useful case study in what AI can do well, where it falls short, and why human insight and input still matter.
Q. Can you tell us about the Professor Insight Podcast?
Billy: 鈥淪o the podcast I鈥檝e been doing is actually a side project, and it started in a very unexpected way.
鈥淚 was overseas on extended carers leave and driving between hospitals every day. As an academic, I was still supervising students and reading a lot of material, but I didn鈥檛 have time to sit down and read hundreds of pages.
鈥淎t the time, Google released NotebookLM and I started feeding documents into it and getting summaries back in a broadcast-style format. I could listen while driving, and suddenly I鈥檇 covered 500 pages of material without sitting at a desk.
鈥淭hat鈥檚 when I realised I could generate podcast-style content focused on AI, neuroscience and decision-making 鈥� and make complex research more accessible.
鈥淭he podcast itself is fully AI-generated, but we disclose that clearly at the start of every episode.
鈥淚n practice, generating an episode still takes two to three hours. I read the source material, decide what鈥檚 interesting, prompt the AI carefully, listen to the output and then edit it.
鈥淚f everything is prompted well, AI can probably do about 70% of the work. The remaining 30% still needs human judgement.鈥�
鈥淚f you don鈥檛 prompt it properly and just let it run, it probably does about 30% of the job.
鈥淪o, I don鈥檛 think AI will replace human-to-human podcasts any time soon. You still need a human in the loop to shape the content and make it meaningful.鈥�
