{"id":30137,"date":"2026-02-10T13:06:12","date_gmt":"2026-02-10T05:06:12","guid":{"rendered":"https:\/\/www.curtin.edu.au\/news\/?p=30137"},"modified":"2026-03-12T15:23:04","modified_gmt":"2026-03-12T07:23:04","slug":"tfo-human-ai-decision-making","status":"publish","type":"post","link":"https:\/\/www.curtin.edu.au\/news\/tfo-human-ai-decision-making\/","title":{"rendered":"How\u00a0much\u00a0can we trust AI?\u00a0Podcast insights"},"content":{"rendered":"\n<p>We like to believe our decisions are our own&nbsp;\u2013&nbsp;shaped by our values, interests and lived experience. But artificial intelligence&nbsp;is beginning to influence&nbsp;many of the choices&nbsp;we&nbsp;think&nbsp;we&nbsp;make independently.<\/p>\n\n\n\n<p>In&nbsp;<a href=\"https:\/\/curtin.edu\/brfjhg\" target=\"_blank\" rel=\"noreferrer noopener\"><em>The Future Of Human\u2013AI Decision-Making<\/em><\/a>,&nbsp;we&nbsp;were&nbsp;joined by Professor Billy Sung&nbsp;to&nbsp;explore&nbsp;how this shift&nbsp;to AI decision-making&nbsp;is unfolding in practice&nbsp;\u2013&nbsp;how much we should trust it and why being human still matters.<\/p>\n\n\n\n<p>Below is&nbsp;just a&nbsp;selection&nbsp;of&nbsp;insights&nbsp;from the discussion.&nbsp;You can&nbsp;listen to the&nbsp;full episode,&nbsp;<a href=\"https:\/\/curtin.edu\/brfjhg\" target=\"_blank\" rel=\"noreferrer noopener\"><em>The Future&nbsp;Of&nbsp;Human\u2013AI Decision-Making<\/em><\/a><em>,<\/em>&nbsp;on Apple Podcasts, Spotify and more.&nbsp;<\/p>\n\n\n\n<iframe data-testid=\"embed-iframe\" style=\"border-radius:12px\" src=\"https:\/\/open.spotify.com\/embed\/episode\/5RQJTpRYwggGx1gwj8fJPR?utm_source=generator\" width=\"100%\" height=\"152\" frameBorder=\"0\" allowfullscreen=\"\" allow=\"autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture\" loading=\"lazy\"><\/iframe>\n\n\n<h2 class=\"wp-block-heading\">\ud83c\udfa7&nbsp;Insight one:&nbsp;Predictive AI already shapes decisions \u2013 often without people realising&nbsp;&nbsp;<\/h2>\n\n\n\n<p>When people talk about artificial intelligence today,&nbsp;they\u2019re&nbsp;often referring to tools like ChatGPT. But that framing misses a much bigger picture.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Q. Billy, what do we mean when&nbsp;we\u2019re&nbsp;talking about AI?<\/strong>&nbsp;<\/h4>\n\n\n\n<p><strong>Billy:<\/strong>&nbsp;\u201cArtificial intelligence, or AI, is&nbsp;actually everywhere. But the rise of tools like ChatGPT \u2013 currently the largest consumer-facing generative AI platform \u2013 has led to a widespread generalisation of what AI is and how it works.&nbsp;<\/p>\n\n\n\n<p>\u201cFor many people, AI has become shorthand for generative AI.&nbsp;In reality,&nbsp;predictive&nbsp;AI&nbsp;has been embedded across society for years, well before&nbsp;we used&nbsp;generative tools like ChatGPT, Claude,&nbsp;BART&nbsp;or Gemini.&nbsp;<\/p>\n\n\n\n<p>\u201cSo, for instance, Google&nbsp;Maps uses AI. When you enter a destination, the system draws on traffic data, real-time&nbsp;conditions&nbsp;and historical patterns to predict the fastest route. That process \u2013 using data to predict an outcome \u2013 is artificial intelligence at work.&nbsp;<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p><em>\u201cAt its core, AI is not&nbsp;\u2018intelligence\u2019&nbsp;but instead&nbsp;it\u2019s&nbsp;a system designed to use data to better predict a particular goal or&nbsp;outcome.\u201d<\/em>&nbsp;<\/p>\n<\/blockquote>\n\n\n\n<p>\u201cThere are&nbsp;different types&nbsp;of AI that serve different purposes. Predictive AI forecasts&nbsp;outcomes, such as routes,&nbsp;recommendations&nbsp;or demand. Generative AI produces&nbsp;new content, whether that\u2019s text,&nbsp;images&nbsp;or audio.&nbsp;<\/p>\n\n\n\n<p>\u201cBeyond these visible tools, much of AI&nbsp;actually&nbsp;operates&nbsp;behind the scenes. Recommendation systems, such as those used by streaming platforms, are another long-standing example of artificial intelligence shaping everyday experiences.&nbsp;<\/p>\n\n\n\n<p>\u201cIn general, AI is&nbsp;influencing decisions&nbsp;everywhere.&nbsp;It\u2019s&nbsp;really about prediction:&nbsp;anticipating&nbsp;outcomes and guiding decisions toward a goal.\u201d&nbsp;<\/p>\n\n\n\n<p>This shift in visibility matters \u2013 because once AI moves from the background to centre stage, expectations around trust and responsibility change.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/curtin.edu\/z4t457 \"><img loading=\"lazy\" decoding=\"async\" width=\"1200\" height=\"500\" src=\"https:\/\/www.curtin.edu.au\/news\/wp-content\/uploads\/2026\/01\/How-much-can-we-trust-AI_Podcast-insights21-1200x500.jpg\" alt=\"Happy people in the driver and passenger seat of a car looking at their phones.\" class=\"wp-image-30144\"\/><\/a><figcaption class=\"wp-element-caption\">Google Maps is an example of AI at work, drawing on traffic data, real-time conditions and historical patterns to predict the fastest route. Image: Adobe Stock<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83c\udfa7&nbsp;Insight two:&nbsp;We can (sometimes) trust (some of) AI\u2019s decisions&nbsp;<\/h2>\n\n\n\n<p>Trust&nbsp;isn\u2019t&nbsp;simple. With AI it depends on what the system is being asked to do, where&nbsp;it\u2019s&nbsp;deployed and how much data it has to learn from.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Q. Can we trust AI?<\/strong>&nbsp;<\/h4>\n\n\n\n<p><strong>Billy:<\/strong>&nbsp;\u201cWhether we can really trust AI&nbsp;is&nbsp;a&nbsp;multi-billion-dollar question. And the answer depends on what kind of task the system is being asked to perform.&nbsp;&nbsp;<\/p>\n\n\n\n<p>\u201cMany of today\u2019s AI systems&nbsp;\u2013&nbsp;particularly recommendation engines&nbsp;\u2013&nbsp;are highly developed. Platforms like Netflix, search engines&nbsp;like Google, and e-commerce sites&nbsp;like Amazon&nbsp;rely on models trained on vast amounts of behavioural data to predict what users are most likely to watch, click or buy next.&nbsp;<\/p>\n\n\n\n<p>\u201cIn marketing and consumer psychology,&nbsp;it\u2019s&nbsp;well established that people\u2019s choices can be predicted to a certain extent \u2013 not 100 per cent. AI systems can&nbsp;identify&nbsp;patterns that suggest which product,&nbsp;brand&nbsp;or option a person is more likely to choose based on past behaviour and the behaviour of similar users.&nbsp;<\/p>\n\n\n\n<p>\u201cIn&nbsp;these&nbsp;consumer&nbsp;contexts, AI is doing what it does best: using existing data to predict&nbsp;a likely outcome.&nbsp;<\/p>\n\n\n\n<p>\u201cProblems arise when AI is asked to predict outcomes that are fundamentally unpredictable.&nbsp;<\/p>\n\n\n\n<p>\u201cA lottery is a useful example. Even if an AI system were allowed to generate lottery numbers, the output would still be meaningless \u2013 because the&nbsp;numbers are&nbsp;random. In those cases, trust is misplaced because prediction is impossible.&nbsp;<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p><em>\u201cSo,&nbsp;whether we can trust&nbsp;AI\u2019s decisions&nbsp;and predictions&nbsp;comes down to&nbsp;the model, the data it has access to, and the environment it&nbsp;operates&nbsp;in.\u201d&nbsp;<\/em>&nbsp;<\/p>\n<\/blockquote>\n\n\n\n<p>\u201cWithout sufficient context, AI&nbsp;doesn\u2019t&nbsp;fail dramatically. It fails quietly \u2013 by making plausible but suboptimal recommendations.&nbsp;<\/p>\n\n\n\n<p>\u201cFrom a practical standpoint, current AI systems are best understood as&nbsp;partial contributors<em>,&nbsp;<\/em>not decision-makers. They can often deliver 50 to 60 per cent of what a&nbsp;person is looking for \u2013 surfacing options, narrowing choices, and processing information at scale.&nbsp;<\/p>\n\n\n\n<p>\u201cBut a human still needs to remain in the loop, crafting prompts, interpreting outputs and applying judgement.\u201d&nbsp;<\/p>\n\n\n\n<p><a href=\"https:\/\/curtin.edu\/brfjhg\" target=\"_blank\" rel=\"noreferrer noopener\">In the full episode, we explore the skills needed&nbsp;to thrive in this new decision-making environment and the emerging&nbsp;context\u2013privacy paradox.<\/a>&nbsp;Don\u2019t&nbsp;miss out on&nbsp;the insights.&nbsp;<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/curtin.edu\/z4t457 \"><img loading=\"lazy\" decoding=\"async\" width=\"2048\" height=\"253\" src=\"https:\/\/www.curtin.edu.au\/news\/wp-content\/uploads\/2026\/01\/TFO-SEO-Article-Ad-1-scaled.jpg\" alt=\"\" class=\"wp-image-30140\" srcset=\"https:\/\/www.curtin.edu.au\/news\/wp-content\/uploads\/2026\/01\/TFO-SEO-Article-Ad-1-scaled.jpg 2048w, https:\/\/www.curtin.edu.au\/news\/wp-content\/uploads\/2026\/01\/TFO-SEO-Article-Ad-1-768x95.jpg 768w, https:\/\/www.curtin.edu.au\/news\/wp-content\/uploads\/2026\/01\/TFO-SEO-Article-Ad-1-1536x190.jpg 1536w\" sizes=\"auto, (max-width: 2048px) 100vw, 2048px\" \/><\/a><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\">\ud83c\udfa7&nbsp;Insight&nbsp;three:&nbsp;Trust&nbsp;in AI will grow where platforms are reliable, transparent and fair&nbsp;&nbsp;<\/h2>\n\n\n\n<p>As AI systems become more embedded in everyday decision-making, will users, industries and institutions come to trust AI more or less?&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Q.&nbsp;What do you think will&nbsp;determine&nbsp;whether trust in&nbsp;generative&nbsp;AI rises or falls in the future?<\/strong>&nbsp;<\/h4>\n\n\n\n<p><strong>Billy:<\/strong>&nbsp;\u201cThis is now a rapidly growing field of study. Academic research into AI trust has grown significantly in recent years.\u201d&nbsp;<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p><em>\u201cAcross literature, the same framework appears&nbsp;again and again: reliability,&nbsp;transparency&nbsp;and fairness.\u201d&nbsp;<\/em>&nbsp;<\/p>\n<\/blockquote>\n\n\n\n<p>\u201cReliability is the most basic requirement for trust. At a technical level, this refers to the accuracy and precision of an AI system\u2019s predictions. Can it consistently produce outcomes that align with real-world behaviour?&nbsp;<\/p>\n\n\n\n<p>\u201cPeople also want to understand how an AI system arrived at a particular recommendation or output.&nbsp;<\/p>\n\n\n\n<p>\u201cThis is where transparency \u2013 often called explainability in the technical world \u2013 becomes critical. Explainability refers to whether an AI system can communicate the reasoning behind its outputs in a way&nbsp;that&nbsp;humans can understand.&nbsp;<\/p>\n\n\n\n<p>\u201cResearch published in leading academic journals&nbsp;actually&nbsp;shows&nbsp;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.&nbsp;<\/p>\n\n\n\n<p>\u201cThe third pillar of trust is fairness&nbsp;\u2013&nbsp;not just in terms of access to AI, but in how decisions are shaped behind the scenes.&nbsp;<\/p>\n\n\n\n<p>\u201cFairness raises ethical questions about whose interests an AI system&nbsp;ultimately serves. This becomes particularly important as advertising and commercial incentives increasingly intersect with generative AI platforms.&nbsp;<\/p>\n\n\n\n<p>Is it possible to still&nbsp;trust&nbsp;conversational&nbsp;AI&nbsp;to be fair when&nbsp;responses&nbsp;contain&nbsp;advertising?&nbsp;<a href=\"https:\/\/curtin.edu\/brfjhg\" data-type=\"link\" data-id=\"https:\/\/curtin.edu\/brfjhg\" target=\"_blank\" rel=\"noreferrer noopener\">In the full conversation, we explore this in detail.<\/a>&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83c\udfa7&nbsp;Insight&nbsp;four:&nbsp;The coming shift is \u201cshared agency\u201d&nbsp;with&nbsp;co-created human\u2013AI decision-making&nbsp;<\/h2>\n\n\n\n<p>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.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Q.&nbsp;What do you think is the&nbsp;most likely future&nbsp;for human-machine&nbsp;decision-making?<\/strong>&nbsp;<\/h4>\n\n\n\n<p><strong>Billy:<\/strong>&nbsp;\u201cThe&nbsp;most likely future&nbsp;of human\u2013AI decision-making&nbsp;isn\u2019t&nbsp;full automation&nbsp;\u2013&nbsp;and it&nbsp;isn\u2019t&nbsp;humans handing over control. Instead,&nbsp;it\u2019s&nbsp;what researchers describe as shared agency: a co-created decision-making process where humans and AI each play distinct roles.&nbsp;<\/p>\n\n\n\n<p>\u201cWe&nbsp;already&nbsp;share decisions&nbsp;with AI&nbsp;\u2013 through search engines, recommendation platforms, navigation&nbsp;tools&nbsp;and conversational AI. What\u2019s changing is not&nbsp;whether&nbsp;AI is involved, but how deeply it becomes embedded across the decision journey.&nbsp;<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p><em>\u201cRather than acting as a decision-maker, AI increasingly functions as a decision assistant \u2013 narrowing options, surfacing patterns, and reducing cognitive load \u2013 while humans&nbsp;retain&nbsp;responsibility for the final choice.\u201d<\/em>&nbsp;<\/p>\n<\/blockquote>\n\n\n\n<p>\u201cConsider a near-future version of a familiar decision: buying a car. Before visiting a dealership, a buyer might consult AI to clarify their needs \u2013 price range, vehicle&nbsp;type&nbsp;or key features \u2013 and quickly narrow the field. AI&nbsp;doesn\u2019t&nbsp;make the decision, but it shapes the&nbsp;<em>consideration set<\/em>&nbsp;by filtering options, comparing&nbsp;models&nbsp;and summarising large volumes of review data.&nbsp;&nbsp;<\/p>\n\n\n\n<p>\u201cThe appeal of shared agency is efficiency. AI excels at processing scale: hundreds of documents, thousands of reviews, years of behavioural data.&nbsp;&nbsp;<\/p>\n\n\n\n<p>\u201cOver the next five to six years, this pattern is expected to expand across everyday decisions.&nbsp;<\/p>\n\n\n\n<p>\u201cThe critical distinction is that shared agency preserves human accountability.\u201d&nbsp;<\/p>\n\n\n\n<p>For the immediate future, important decisions will remain human \u2013 even when informed by machines.\u201d&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/curtin.edu\/z4t457 \"><img loading=\"lazy\" decoding=\"async\" width=\"1200\" height=\"500\" src=\"https:\/\/www.curtin.edu.au\/news\/wp-content\/uploads\/2026\/01\/How-much-can-we-trust-AI_Podcast-insights20-1200x500.jpg\" alt=\"Close up of hands typing on a laptop.\" class=\"wp-image-30143\"\/><\/a><figcaption class=\"wp-element-caption\">Billy doesn&#8217;t see AI taking over decisions in the near future, but it will influence them by doing what it does best:&nbsp;filtering options, comparing&nbsp;versions&nbsp;and summarising large volumes of data.&nbsp;Image: Adobe Stock<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83c\udfa7&nbsp;AI-generated content in practice: the AI podcast case study&nbsp;<\/h2>\n\n\n\n<p>One of the clearest ways to understand both the potential and the&nbsp;limits&nbsp;of AI is to look at how&nbsp;it\u2019s&nbsp;being&nbsp;used in practice. Billy\u2019s&nbsp;<a href=\"https:\/\/open.spotify.com\/show\/5F1SK154kp07l2h5IuNCM1\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Professor Insight Podcast<\/em><\/a>&nbsp;is a fully AI-generated production \u2013 and a useful case study in what AI can do well, where it falls short, and why human&nbsp;insight and input&nbsp;still matter.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Q.&nbsp;Can&nbsp;you tell us about&nbsp;the&nbsp;Professor Insight Podcast?<\/strong>&nbsp;<\/h4>\n\n\n\n<p><strong>Billy:<\/strong>&nbsp;\u201cSo&nbsp;the podcast&nbsp;I\u2019ve&nbsp;been doing is&nbsp;actually a&nbsp;side project, and it started in a very unexpected way.&nbsp;<\/p>\n\n\n\n<p>\u201cI 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&nbsp;didn\u2019t&nbsp;have time to sit down and read hundreds of pages.&nbsp;<\/p>\n\n\n\n<p>\u201cAt the time, Google released&nbsp;NotebookLM&nbsp;and I started feeding documents into it and getting summaries back in a broadcast-style format. I could listen while driving, and suddenly&nbsp;I\u2019d&nbsp;covered 500 pages of material without sitting at a desk.&nbsp;<\/p>\n\n\n\n<p>\u201cThat\u2019s&nbsp;when I&nbsp;realised&nbsp;I could generate podcast-style content focused on AI,&nbsp;neuroscience&nbsp;and decision-making \u2013 and make complex research more accessible.&nbsp;<\/p>\n\n\n\n<p>\u201cThe podcast itself is fully AI-generated, but we&nbsp;disclose&nbsp;that clearly at the start of every episode.&nbsp;<\/p>\n\n\n\n<p>\u201cIn practice, generating an episode still takes two to three hours. I read the source material, decide&nbsp;what\u2019s&nbsp;interesting, prompt the AI carefully, listen to the output and then edit it.&nbsp;<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p><em>\u201cIf everything is prompted well, AI can&nbsp;probably do&nbsp;about 70% of the work. The remaining 30% still needs human judgement.\u201d<\/em>&nbsp;<\/p>\n<\/blockquote>\n\n\n\n<p>\u201cIf you&nbsp;don\u2019t&nbsp;prompt it properly and just let it run, it&nbsp;probably does&nbsp;about 30% of the job.&nbsp;<\/p>\n\n\n\n<p>\u201cSo,&nbsp;I&nbsp;don\u2019t&nbsp;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.\u201d&nbsp;<\/p>\n\n\n    <section class=\"block articles mimas-grid\" id=\"\" data-grid-type=\"full\" data-bg=\"gray\">\n            <div class=\"article\" >\n            \n            <img decoding=\"async\" src=\"https:\/\/www.curtin.edu.au\/news\/wp-content\/uploads\/2025\/12\/TFO-device-1000px.jpg\"\n                 alt=\"\">\n\n            <div class=\"article__content\">\n                                <h2>Get the\u00a0full story<\/h2>\n\n                <p>Discover how AI is reshaping human-machine\u00a0decision-making \u2013 from an expert in the field.\u00a0\u00a0<\/p>\n\n                                                                                                        <div class=\"article__links\" data-link-style=\"default\">\n                                                                                                                                                        <a href=\"https:\/\/curtin.edu\/7z1w5z \" target=\"\"\n                                   class=\"default\" aria-label=\"Listen now\">Listen now<span class=\"ico ico-arrow ico--deep-blue ico--small\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 32 32\"><path d=\"M15.71 2.29l-1.42 1.42L25.59 15H3v2h22.59l-11.3 11.29 1.42 1.42L29.41 16 15.71 2.29z\"\/><\/svg><\/span><\/a>\n                                                    <\/div>\n                    \n                            <\/div>\n        <\/div>\n    <\/section>\n","protected":false},"excerpt":{"rendered":"<p>We like to believe our decisions are our own&nbsp;\u2013&nbsp;shaped by our values, interests and lived [&hellip;]<\/p>\n","protected":false},"author":4477,"featured_media":30148,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_oasis_is_in_workflow":0,"_oasis_original":0,"_oasis_task_priority":"","_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"29060,4640,4815,6067,5201,4807","_relevanssi_noindex_reason":"","wds_primary_category":116,"wds_primary_research-areas":0,"footnotes":""},"categories":[116],"tags":[383,306,1280,211,1357],"research-areas":[],"class_list":["post-30137","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-and-innovation","tag-business","tag-innovation","tag-marketing","tag-research","tag-the-future-of"],"acf":{"post_components":false,"post_options":{"":null,"additional_content":{"title":"","content":"","image":false},"related_courses":[{"title":"Master of Artificial Intelligence","qualification":"Master of Artificial Intelligence","link":"https:\/\/curtin.edu\/gthvh9","description":"Understand machine learning, neural networks and ethical AI, before immersing yourself in an industry-based project that prepares you to build responsible, real-world AI solutions.","faculty":"Science and Engineering"},{"title":"Master of Predictive Analytics","qualification":"Master of Predictive Analytics","link":"https:\/\/curtin.edu\/9b8tda","description":"Learn how to correlate probability assessments, make informed decisions in your business or industry and handle the big data issues of the future.","faculty":"Science and Engineering"}],"credits":{"author":"","photographer":"","media":false},"display_author":true,"banner":{"image":false}}},"featured_image":"https:\/\/www.curtin.edu.au\/news\/wp-content\/uploads\/2026\/02\/How-much-can-we-trust-AI_Podcast-insights-1000x500.jpeg","author_meta":{"first_name":"Caitlin","last_name":"Crowley","display_name":"Caitlin Crowley"},"publishpress_future_action":{"enabled":false,"date":"2026-04-26 16:50:36","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/posts\/30137","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/users\/4477"}],"replies":[{"embeddable":true,"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/comments?post=30137"}],"version-history":[{"count":0,"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/posts\/30137\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/media\/30148"}],"wp:attachment":[{"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/media?parent=30137"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/categories?post=30137"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/tags?post=30137"},{"taxonomy":"research-areas","embeddable":true,"href":"https:\/\/www.curtin.edu.au\/news\/wp-json\/wp\/v2\/research-areas?post=30137"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}