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Contents
Symptom Checkers & AI Diagnosis
Wearable Devices & Preventive Care
AI in Entertainment: Transforming How We Create and Consume Media
Introduction
Recap
Let’s begin with a quick review of what we’ve covered so far. In our previous sessions, we explored the basics of Artificial Intelligence, starting with the question, "What is AI?" We learned that AI refers to machines or software that can mimic human intelligence—such as recognizing speech, making decisions, or identifying patterns. We also touched on how AI learns, introducing concepts like machine learning, where systems improve over time by analysing large amounts of data rather than being explicitly programmed for every task.
It’s important to emphasize that AI is not some distant, futuristic technology, it’s already deeply embedded in our everyday lives. From the voice assistants on our phones to the recommendations we receive on Netflix or YouTube, AI plays a behind-the-scenes role in shaping our digital experiences. Even things like spam filters in email, navigation apps, and personalized ads are all powered by AI. Understanding this helps us realize that AI is not just a topic of study, it’s a practical and present force that impacts how we live, work, and interact with technology every day.
Agenda
Today’s focus: real-world AI applications in healthcare, finance, and entertainment.
Preview the hands-on activity Ada and My Fitness Pal.
For your amusement
Mr Trump, who is not a Catholic and does not attend church regularly, posted the image on his Truth Social platform on Friday local time, less than a week after attending the funeral of Pope Francis, who died at 88 last month.
The White House then reposted the image on its official X account.
AI in Healthcare
Key Applications
Symptom Checkers & AI Diagnosis
AI-powered symptom checkers, such as Ada Health and WebMD’s AI assistant, are becoming increasingly popular as first-line tools for patients seeking quick medical insights. These platforms use natural language processing (NLP) and machine learning to analyse user-reported symptoms, medical history, and risk factors before generating a list of possible conditions. For instance, a patient entering symptoms like "fever, cough, and fatigue" might receive suggestions ranging from the common cold to more serious conditions like pneumonia or COVID-19. The AI compares inputs against vast medical databases, clinical guidelines, and anonymized case histories to rank potential diagnoses by likelihood.
While these tools offer greater accessibility—especially for individuals in remote areas or those unable to immediately consult a doctor—they come with key limitations. AI symptom checkers cannot perform physical examinations, interpret imaging, or account for highly nuanced patient histories. Misdiagnosis risks are present, particularly for rare or complex conditions. Therefore, these tools should serve as supplements rather than replacements for professional medical advice. Doctors remain essential for confirming diagnoses, ordering tests, and developing treatment plans.
Wearable Devices & Preventive Care
Wearable health technology, such as the Apple Watch (with ECG and fall detection) and Fitbit (tracking activity, sleep, and heart rate), leverages AI to transform raw biometric data into actionable health insights. These devices continuously collect metrics like heart rate variability, blood oxygen levels, and step counts, feeding them into machine learning models that detect anomalies over time. For example, irregular heart rhythms may trigger an alert for possible atrial fibrillation, while sudden changes in movement patterns could indicate a fall, prompting emergency notifications.
AI enhances preventive care by identifying trends that might otherwise go unnoticed. For instance, gradual increases in resting heart rate could signal dehydration, stress, or an underlying cardiac issue. By analysing long-term data, AI can encourage users to adopt healthier habits or seek medical attention before minor issues escalate. However, while wearables provide valuable early warnings, they are not diagnostic tools—abnormal readings should always be verified by healthcare professionals to avoid false alarms or overlooked conditions.
Ethical Considerations
The rise of AI in healthcare brings critical ethical challenges, particularly regarding data privacy and algorithmic bias. Who owns health data? Wearables and symptom checkers collect highly sensitive information, yet users often have little control over how companies store, share, or monetize this data. Breaches could expose personal health details, while third-party data sales raise concerns about consent and transparency. Stronger regulations, like GDPR and HIPAA[1], are necessary to protect patient rights in the digital health era.
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Another major issue is bias in AI diagnostics. Many AI models are trained on datasets that underrepresent women, ethnic minorities, and older adults, leading to less accurate diagnoses for these groups. For example, skin cancer detection algorithms have historically performed worse on darker skin tones due to insufficient training data. Similarly, cardiovascular AI tools may overlook symptoms more common in women, delaying critical care. Addressing these disparities requires diverse datasets, rigorous bias testing, and ongoing model audits to ensure equitable healthcare outcomes for all populations.
Question: "Would you trust an AI tool for initial health advice? Why or why not?"
AI in Finance
Fraud Detection
AI is playing a crucial role in combating financial fraud across Australia, where scams cost consumers and businesses billions annually. Major Australian banks, such as Commonwealth Bank (CBA) and ANZ, use AI-driven fraud detection systems to monitor transactions in real time. For example, if an Australian credit card is used for an unusually large purchase overseas or a rapid series of transactions occurs at odd hours, AI flags it for review. These systems rely on machine learning models trained on vast datasets of past fraudulent transactions, including common scams like "Hi Mum" SMS fraud and invoice redirection scams targeting businesses.
A key strength of AI in fraud detection is its adaptive learning capability. As scammers evolve their tactics—such as the rise of deepfake voice scams impersonating bank staff—AI models continuously update to detect new patterns. The Australian Competition and Consumer Commission (ACCC) reports that AI has helped reduce losses from payment fraud, but false positives (legitimate transactions flagged as fraud) remain a challenge. For instance, an Aussie buying a last-minute flight to Bali might get their card blocked, highlighting the need for balance between security and convenience.
Australian fintechs are also innovating in this space. Airwallex[2], the Melbourne-born global payments platform, uses AI to detect cross-border payment fraud by analysing transaction patterns across its network. Their system can identify subtle anomalies like sudden changes in payment destinations or unusual invoice amounts that might indicate business email compromise scams.
However, challenges remain. The ACCC's Targeting Scams report shows that while AI helps detect traditional fraud, it struggles with sophisticated social engineering scams like investment scams promoted through social media ads - these still account for the highest financial losses.
Budgeting & Personal Finance
Australia's fintech sector has produced world-leading AI budgeting tools. Pocketbook, acquired by Zip Co, uses machine learning to categorize transactions with Australian-specific precision - it can distinguish between a Woolworths grocery run and BWS alcohol purchase. Their AI also provides hyper-local insights, like warning Sydney users about "double demerits" periods that might lead to unexpected fines.
Afterpay's AI-driven spending limits represent another Australian innovation. Their system analyses thousands of data points to determine real-time spending capacity, considering factors like:
- Regular income patterns
- Existing BNPL[3] commitments
- Even subtle behavioural cues like repayment speed
Case Study: The Commonwealth Bank's Goal Tracker feature uses predictive AI to help users save for Australian-specific goals like:
- "First Home Saver" accounts (projecting savings timelines based on property price trends)
- "School Fees Planner" accounting for private education costs
- "Rainy Day Fund" calculator based on local cost-of-living data
Algorithmic Trading
Australia's trading landscape presents unique AI challenges and opportunities:
Local Success Story: Sydney-based Stake uses AI to provide personalized investment recommendations tailored to Australian investors, considering factors like:
- Franking credit implications
- ASX sector weightings
- Local tax considerations
The ASX's[4] AI surveillance systems monitor for suspicious trading patterns, particularly important in Australia's resource-heavy market where mining stocks can be volatile. These systems track:
- Unusual trading in lithium/mining stocks before announcements
- Wash trading in small-cap ASX listings
- Ramping activities in penny stocks
Cautionary Tale: The 2020 ASX trade halt due to technical issues highlighted the risks of over-reliance on automated systems, prompting stricter AI governance frameworks.
Risks & Trust in AI Finance
Australian-specific concerns include
Buy Now Pay Later (BNPL) Risks:
- Afterpay's AI credit assessments face scrutiny for potentially encouraging over-leverage
- ASIC research shows 15% of BNPL users cut back on essentials to make payments
Superannuation Algorithms
- AI-driven robo-advice in super funds may not account for Australia's complex retirement system
- Case study: An AI recommended excessive switching between investment options during COVID volatility, triggering unexpected tax events
Regulatory Response
- APRA's CPG 234 mandates[5] explainability in financial AI systems
- The Treasury's "AI Ethics Framework" specifically addresses financial services applications
Conclusion
The Australian AI Finance Landscape
Australia is emerging as a global leader in responsible financial AI, with:
- Innovators: Afterpay, Airwallex, and local budgeting apps showing global potential
- Balanced Regulation: Progressive frameworks addressing both innovation and consumer protection
- Unique Challenges: From superannuation complexity to resource market volatility
"Given Australia's high BNPL adoption, should AI systems be more conservative in recommending these products to young consumers?"
Quick Poll: "Have you ever interacted with a banking chatbot? Was it helpful?"
AI in Entertainment: Transforming How We Create and Consume Media
Recommendation Systems
AI-powered recommendation engines have revolutionized media discovery, with Australian platforms leading innovative adaptations.
Australian Case Studies
- ABC iView's AI curator personalizes content suggestions based on viewing habits and even considers regional differences - suggesting bush poetry specials to rural viewers or multicultural content in urban areas
- SBS On Demand uses AI to adjust subtitle preferences and recommend foreign language films based on a user's cultural background
- Spotify's Aussie artist boost algorithmically promotes Australian musicians like Tones and I or Gang of Youths in local playlists
Technical Deep Dive
These systems use
- Collaborative filtering (Netflix: "Viewers who watched Bluey also enjoyed...")
- Content-based filtering (YouTube analysing video metadata)
- Context-aware models (adjusting recommendations for school holidays or AFL Grand Final weekend)
Ethical Considerations
- The 2023 ACCC inquiry found streaming algorithms may unfairly favour platform-owned content
- Case study: When Netflix's AI overwhelmingly recommended "Boy Swallows Universe," limiting exposure to other Australian dramas
Photo & Video Editing
Australia has become a hotspot for ethical AI media innovation:
Local Innovations.
- Canva's Magic Studio (developed in Sydney) uses AI for:
- Automatic Aussie slang translation in designs ("arvo" vs "afternoon")
- AI-generated kangaroo/koala stock images to avoid copyright issues
- Flintfox's AI video editor (Melbourne-based) helps content creators:
- Auto-edit landscape shots to comply with Parks Australia guidelines
- Detect and flag culturally insensitive imagery in Indigenous content
Deepfake Developments
Positive Use
SA Film Corporation's AI de-aging tech helped recreate young David Gulpilil for archival projects
Controversy
2023's "Fake Shane Warne" ads sparked debates about posthumous digital likeness rights
Regulation: Australia's proposed "Deepfake Disclosure Law" would require clear labelling of synthetic media
AI-Generated Content
The Australian creative industry presents unique case studies.
Landmark Examples
1. Music
- Universal Music forced TikTok to remove an AI-generated Kylie Minogue track
- Controversial "AI Slim Dusty" tracks circulating on SoundCloud
2. Literature
- Allen & Unwin's AI-assisted "Aussie Bush Tales" series (human-AI collaboration)
- The 2024 Miles Franklin Award controversy over an AI-assisted submission
3. Visual Arts:
- NGV's 2023 AI art exhibition drew protests from traditional artists
- Indigenous artists using AI to recreate lost cultural patterns (with Elder oversight)
Technical Workflow
Australian creators typically use
- Localized AI Models: Trained on Aussie datasets (avoiding northern hemisphere biases)
- Hybrid Creation: AI generates drafts which humans refine with local nuance
- Cultural Verification: Especially important for First Nations content
Regulatory Landscape
- Screen Australia's new AI guidelines require disclosure in funding applications
- APRA AMCOS[6] developing royalty structures for AI music
- Proposed "Australian Content Quotas" for streaming platforms may include AI provisions
Conclusion: Australia's AI Entertainment Crossroads
Key discussion points for students:
- Should AI content count toward Australian content quotas?
- How can we protect Indigenous knowledge in AI training sets?
- Does AI help or hinder our cultural identity?
[i]Activity Idea
Fun Demo: Show a before/after of an AI-enhanced photo (e.g., Remini or Lensa).
Hands-On Activity
Option 1: Try an AI health app (e.g., Ada or MyFitnessPal).
Option 2: Experiment with an AI photo tool (e.g., Remove.bg or Canva’s AI features).
Task: Spend 5-7 minutes exploring, then note observations.
Group Discussion
"Which AI application would you find most useful in your daily life?"
"What concerns do you have about relying on AI in these areas?"
Wrap-Up & Q&A
Here’s a concise recap of AI’s ubiquity, benefits, and ethical challenges.
1. AI’s Ubiquity
· AI is now foundational across industries like healthcare, finance, criminal justice, and retail, with global business spending projected to reach $110 billion annually by 2024 .
· Small businesses leverage AI for real-time financial insights and lending, while large enterprises use it for strategic decision-making and automation.
· Generative AI (e.g., ChatGPT) and foundation models have expanded AI’s reach into creative fields, law, and even art, raising questions about authorship and originality.
2. Benefits of AI
· Efficiency: Accelerates R&D (e.g., drug development) and reduces costs (e.g., automating billing in healthcare) .
· Enhanced Decision-Making: AI analyses vast datasets for diagnostics, credit scoring, and hiring, though biases remain a risk.
· Job Augmentation: Hybrid roles emerge where AI handles technical tasks (e.g., delivery routing), freeing humans for creative or empathetic work.
3. Ethical Questions
· Bias & Discrimination: AI replicates societal biases in hiring (e.g., Amazon’s resume tool) and criminal justice (e.g., COMPAS algorithm).
· Transparency: "Black box" systems lack explainability, critical in healthcare or autonomous vehicles.
· Privacy & Surveillance: Facial recognition and data collection risk misuse (e.g., China’s surveillance).
· Accountability: No universal regulations exist; the EU’s AI Act leads in risk-based oversight, while the U.S. lags.
· Existential Risks: Debates persist about superintelligence, though some argue current harms (e.g., job displacement) demand more attention.
Key Takeaways
AI’s transformative potential is tempered by ethical pitfalls. While it drives innovation, proactive governance (e.g., bias audits, multidisciplinary teams) and literacy are vital to ensure equitable outcomes. The EU’s regulatory approach offers a model, but global collaboration is needed to align AI with human values.
Closing Thought: "AI isn’t just the future—it’s already here. How we use it is up to us."
