In this episode of AI Ethics, Lucas and Luna explore the hidden biases embedded in AI medical chatbots and symptom checkers. They focus on a 2025 study that found these tools often misdiagnose conditions in women and people of color, echoing longstanding gaps in clinical research. The conversation drills into how training data from electronic health records and medical literature skews toward white male presentations, leading chatbots to miss heart attacks in women or downplay pain in Black patients. They also discuss the real-world stakes — from delayed treatment to eroded trust — and what developers like OpenAI and Google are doing to address the problem. Plus, the hosts consider whether regulation can keep pace with rapid deployment in healthcare settings. A thoughtful episode that unpacks one concrete example and its wider implications for responsible AI.

#AIEthics #MedicalAI #DiagnosticBias #HealthcareAI #Chatbots #HealthEquity #AlgorithmicBias #ClinicalAI #HealthTech #AIinMedicine #Technology #Business #DataEthics #ResponsibleAI #FexingoBusiness #BusinessPodcast #AIPodcast #AIandSociety

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In this episode of AI Ethics with Fexingo, Lucas and Luna explore the growing use of AI risk assessment tools in courtroom sentencing. They focus on the case of Paul Zilly, a man whose sentence was influenced by an algorithm's recidivism score, and the controversy surrounding COMPAS, the tool developed by Northpointe. The conversation unpacks how these algorithms learn bias from historical crime data, the impossibility of perfect fairness when the data itself is biased, and the profound implications of delegating moral judgment to a black box. The hosts discuss the need for transparency, the dangers of over-reliance on algorithmic predictions, and the human cost of automation in the justice system. This is essential listening for anyone concerned about the intersection of technology and civil liberties.

#AIEthics #CriminalJustice #COMPAS #Recidivism #AlgorithmicBias #RiskAssessment #Sentencing #Technology #LucasAndLuna #FexingoBusiness #BusinessPodcast #AI #MachineLearning #DataEthics #CivilLiberties #JusticeSystem #Northpointe #PaulZilly

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Ahead of the COP39 climate summit in November 2026, Lucas and Luna dig into a quiet but high-stakes corner of AI ethics: the simulation models governments use to set carbon budgets and adaptation policy. They focus on the ACCESS-OM2 ocean model and the CMIP6 ensemble, showing how a 2014 training-data quirk — a small patch of missing Atlantic salinity — silently skewed sea-level projections for the North Atlantic coast. They trace the same structural bias into AI downscaling tools like the popular CORDEX-derived neural networks, which extrapolate where they have no data, and ask whether a model that was tuned for a 1.5-degree world can be trusted at 3 degrees. With the IPCC's AR7 deadline shifting to March 2027, the episode asks who audits the auditors — and whether the rush to bigger models is trading transparency for accuracy. A sharp, accessible look at how the coldest data can hide the hottest bias.

#AIEthics #ClimateModels #BiasInAI #CMIP6 #ACCESSOM2 #SeaLevelRise #AtlanticSalinity #IPCC #COP39 #ClimatePolicy #MachineLearningBias #DataQuality #AIForGood #ClimateScience #EnvironmentalAI #Technology #FexingoBusiness #BusinessPodcast

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When an AI system reads handwritten doctor's notes, it doesn't just struggle with messy penmanship — it often fails on cursive scripts, particularly those used by older generations. This episode digs into the surprising reason: most training data for handwriting recognition comes from printed letters and neatly typed forms, leaving cursive scripts drastically underrepresented. We look at how models like Google's handwriting recognition and the UK's NHS digitisation project handle this challenge, and why the bias toward print over cursive can lead to misdiagnosis or lost medical history. Lucas and Luna explore the technical roots of this bias, from data collection to model architecture, and consider what it means for the millions of people whose handwritten records are being archived. A conversation about the hidden assumptions in AI training data, and why the way you write might matter more than you think.

#AIEthics #BiasInAI #HandwritingRecognition #CursiveScript #MedicalRecords #NHS #GoogleAI #MachineLearning #Technology #DataBias #AIHistory #DigitalArchives #AIInterviews #ResponsibleAI #FexingoBusiness #BusinessPodcast #TechPodcast #AIResearch

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A University of Texas study found that an AI admissions algorithm penalized applicants from lower-income zip codes at 2.7 times the rate of affluent areas. Lucas and Luna unpack how training data from legacy admissions and school rankings can encode class bias, why state law sometimes prevents inspecting the model, and whether fairness metrics can fix a fundamentally unequal dataset.

#AIEthics #AlgorithmicBias #UniversityAdmissions #HigherEdAI #SocioeconomicBias #FairnessMetrics #ResponsibleAI #EducationTechnology #DataBias #MachineLearning #AdmissionsAlgorithms #EquityInEducation #FexingoBusiness #BusinessPodcast #Technology #AIBias #BiasInAI #EthicalAI

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This episode of AI Ethics with Fexingo examines how AI-powered video interview platforms analyze facial expressions, tone, and word choice to screen job candidates—and the hidden biases those models absorb. We look at a 2025 study from the AI Now Institute that found a leading platform penalized candidates who paused before answering, a behaviour more common in certain cultures. Lucas and Luna discuss the training data problem, the risk of self-fulfilling feedback loops, and what responsible deployment would require. If you're job-hunting or building hiring tools, this episode offers a concrete look at a fast-growing technology with serious fairness implications.

#AIEthics #BiasInAI #VideoInterviews #HiringBias #ResponsibleAI #FexingoBusiness #BusinessPodcast #Technology #FacialRecognition #SpeechAnalysis #HireVue #AISafety #AlgorithmicBias #CandidateScreening #WorkplaceEquity #AIAccountability #FairHiring #DataBias

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In episode 138 of AI Ethics with Fexingo, Lucas and Luna examine how predictive policing algorithms amplify systemic racial bias through a dangerous feedback loop. They break down a 2024 University of Chicago study that found models trained on arrest data predict crime hotspots in majority-Black neighborhoods at rates 40% higher than models using victim reports. The conversation covers the recent cancellation of Geolitica's contract in Los Angeles, the difference between arrest data and calls for service, and why even well-meaning AIs can entrench over-policing. Lucas and Luna discuss potential fixes, including civilian complaint data and algorithmic auditing, but also question whether these systems can ever be truly fair given biased input. This episode offers a clear, data-driven look at one of the most urgent ethical challenges in AI deployment today.

#PredictivePolicing #RacialBias #AIEthics #MachineLearning #CriminalJustice #AlgorithmicBias #Geolitica #LosAngeles #UniversityOfChicago #DataBias #FeedbackLoop #PoliceReform #Technology #FexingoBusiness #BusinessPodcast #AI #BiasInAI #Safety

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Voice assistants like Amazon Alexa, Apple Siri, and Google Assistant are everywhere, but they consistently struggle with non-standard accents. This episode examines a 2025 study from Stanford's Center for Applied AI that found error rates for Indian English speakers are over 30% higher than for General American English. We break down why this happens—from training data demographics to tokenization choices—and what companies are doing to fix it. We also discuss the ethical implications: when voice interfaces become gatekeepers for jobs, healthcare, and banking, accent bias becomes a civil rights issue. Specific fixes like accent-inclusive data collection and dialect-aware language models are working, but progress is slow. Tune in to understand why your smart speaker might hear 'play music' as 'pay music' depending on how you say it.

#VoiceRecognition #AccentBias #AIEthics #Technology #ArtificialIntelligence #StanfordResearch #AmazonAlexa #AppleSiri #GoogleAssistant #AfricanAmericanVernacular #IndianEnglish #ChineseAccents #Tokenization #DataBias #InclusiveAI #FexingoBusiness #BusinessPodcast #NaturalLanguageProcessing

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In this episode, Lucas and Luna dive into the persistent problem of racial bias in facial recognition systems. Starting with a 2019 NIST study that found error rates up to 100 times higher for Black and Asian faces, they explore how training datasets like Labeled Faces in the Wild are overwhelmingly white, leading to biased outcomes. They discuss real-world consequences—false arrests, airport delays—and examine mitigation strategies like synthetic data and data augmentation, while questioning whether these fixes introduce new biases. The conversation touches on regulatory efforts and the responsibility of developers, ending with a reflective look at the trade-offs between accuracy and fairness. A donation segment ties the need for diverse data to listener support for the ad-free show.

#AI #FacialRecognition #Bias #Ethics #Technology #MachineLearning #DataBias #AlgorithmicBias #Discrimination #Privacy #Podcast #FexingoBusiness #BusinessPodcast #AIEthics #ComputerVision #NIST #MIT #SocialImpact

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In this episode of AI Ethics with Fexingo, Lucas and Luna explore how recommendation algorithms inherit bias from user behavior patterns. They anchor the discussion on a 2025 study from the Algorithmic Justice League, which found that YouTube's recommendation engine served 30% more political and conspiracy content to male user profiles than female ones. They dig into the feedback loops that amplify gender, race, and class biases, and discuss what platforms like YouTube are doing—and failing to do—to address it. A concrete look at how your clicks shape algorithmic fairness.

#AIEthics #Bias #RecommendationAlgorithms #YouTube #AlgorithmicJusticeLeague #GenderBias #FeedbackLoops #UserBehaviour #TechEthics #AlgorithmicAuditing #ContentModeration #PoliticalPolarization #Transparency #AIandSociety #FexingoBusiness #BusinessPodcast #Technology #ResponsibleAI

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