Episode 105 of AI Ethics with Fexingo: Bias, Safety, and Responsible Artificial Intelligence. Hosts Lucas and Luna examine how AI diagnostic tools absorb racial and demographic bias from medical imaging datasets. They focus on a 2025 Nature Medicine study showing that chest X-ray models trained on predominantly white populations misdiagnose pneumonia at 40 percent higher rates for Black patients. The conversation covers the root cause — historical disparities in healthcare access reflected in training data — and explores solutions like federated learning and demographic rebalancing that researchers are testing at institutions like MIT and Stanford. Lucas and Luna also discuss why the FDA's current approval framework for AI medical devices doesn't require demographic performance audits, and whether the 2026 AI Bill of Rights could close that gap. If today's discussion gave you a new way to think about responsible tech, support the show at buy me a coffee dot com slash fexingo.

#AIEthics #MedicalAI #AlgorithmicBias #HealthTech #ArtificialIntelligence #RacialBiasInMedicine #DataJustice #FDA #TechRegulation #NatureMedicine #FexingoBusiness #BusinessPodcast #Technology #Podcast #MachineLearning #HealthEquity #DiagnosticAI #FederatedLearning

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Wikipedia is the backbone of training data for many large language models. But behind those neutral-sounding articles are edit wars — heated battles between contributors with competing viewpoints. In this episode of AI Ethics with Fexingo, Lucas and Luna explore how the political and cultural biases embedded in Wikipedia's editing process get absorbed by AI models. They examine the 2025 controversy over the 'Israel-Palestine conflict' page, where algorithmic editing tools amplified majority viewpoints and suppressed minority corrections. They also look at how Wikipedia's 'neutral point of view' policy can paradoxically bake in a false balance — giving equal weight to fringe theories and scientific consensus. The hosts discuss a 2026 study from the University of Oxford that found Wikipedia-trained models are 18% more likely to reflect the bias of the most persistent editors, not the most accurate ones. The conversation offers a concrete look at how data provenance matters more than ever in responsible AI development.

#Wikipedia #AIEthics #TrainingData #EditWars #NeutralPointOfView #Bias #LargeLanguageModels #DataProvenance #OxfordStudy #ContentModeration #AlgorithmicBias #IsraelPalestine #FalseBalance #AIResponsibility #Technology #FexingoBusiness #BusinessPodcast #Podcast

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Lucas and Luna explore how upvote-driven platforms like Reddit train AI models to encode popularity bias. They discuss a 2025 study from the University of Washington showing that language models fine-tuned on Reddit comments amplify the most upvoted—not necessarily most accurate—viewpoints, skewing outputs on topics from politics to product reviews. The hosts dive into concrete examples: a model trained on r/askscience threads over-indexing on confidently wrong answers, and an AI chatbot for mental health advice learning to prioritize 'top comment' platitudes over clinical guidance. They touch on the broader implications for reinforcement learning from human feedback (RLHF), where the 'human' signal is often just a proxy for crowd consensus. Specific numbers include a 23% increase in false-positive safety warnings after RLHF with Reddit data, and a 12% drop in answer diversity across 50 benchmark tasks. The episode closes with a question about whether democratic AI is the right goal.

#Reddit #UpvoteBias #AIEthics #LanguageModels #RLHF #PopularityBias #CrowdConsensus #UniversityOfWashington #SocialMedia #Misinformation #AIbias #FexingoBusiness #BusinessPodcast #Technology #ArtificialIntelligence #ResponsibleAI #BiasDetection #DataQuality

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Episode 102 of AI Ethics with Fexingo examines how performance review language teaches AI to perpetuate gender stereotypes. Lucas and Luna dig into a 2025 study by Stanford and Anthropic that analyzed 1.5 million performance reviews from Fortune 500 companies, finding that words like 'aggressive' and 'confident' were disproportionately applied to men, while 'helpful' and 'collaborative' skewed female. The hosts trace how these biases flow into AI models trained on corporate HR data, then into resume screeners, internal promotion tools, and even chatbot personas. They discuss the feedback loop: as more companies use AI to write reviews, the bias compounds. The episode closes on what companies like Microsoft and Google are doing — and failing to do — to break the cycle.

#AIEthics #GenderBias #PerformanceReviews #Stanford #Anthropic #Fortune500 #HRTech #BiasInAI #CorporateBias #NLPBias #LanguageBias #ResponsibleAI #FexingoBusiness #BusinessPodcast #Technology #AIResearch #WorkplaceBias #MachineLearning

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Episode 101 of AI Ethics with Fexingo: Lucas and Luna dive into the emerging problem of AI systems inferring sensitive attributes like race, gender, and sexual orientation from seemingly innocent selfies. They explore a 2025 Stanford study showing that a standard facial-recognition model could predict political affiliation with 72% accuracy from a single photo. The hosts discuss the rise of 'visual profiling' in hiring, insurance, and lending, where companies use image analysis to make decisions without consent. They also examine a recent lawsuit against a property-insurance startup that allegedly used selfie analysis to deny policies in majority-Black neighborhoods. Lucas and Luna debate whether existing biometric privacy laws like Illinois' BIPA cover this use case, and what consumers can do to protect their visual data. The episode closes with a reflection on the gap between technical capability and ethical guardrails, and a brief mention of listener support to keep the show ad-free.

#AIEthics #Bias #FacialRecognition #Selfies #Privacy #StanfordStudy #VisualProfiling #Insurance #Hiring #Lending #BIPA #BiometricPrivacy #AlgorithmicBias #Technology #FexingoBusiness #BusinessPodcast #AI #MachineLearning

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Episode 100 asks a tough question: Who audits the auditors? We explore how the big four consulting firms and boutique AI ethics shops are simultaneously advising companies on responsible AI — and selling them the very systems those audits are supposed to catch. Using the June 2026 controversy at a major bank where the same firm that designed its lending algorithm also wrote the ethics report, we drill into the structural conflict that undermines third-party accountability. Lucas and Luna also discuss the new EU framework that would ban audit firms from holding both contracts, and whether the US will follow suit. If you've ever wondered why AI ethics feels like a rubber stamp, this episode shows you exactly where the paperwork gets sticky.

#AIEthics #AuditConflict #BigFour #ResponsibleAI #EULegislation #AlgorithmicBias #TechAudit #EthicsWashing #FexingoBusiness #BusinessPodcast #Technology #AIGovernance #ConsultingConflict #BankLending #June2026 #AuditorIndependence #AIAccountability #EthicsTheater

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Episode 99 explores a subtle but pervasive source of bias in large language models: the political slant embedded in Wikipedia itself. Lucas and Luna break down a 2025 Stanford study that found Wikipedia editors' ideological leanings—especially on contentious topics like US politics, climate policy, and historical figures—are measurably passed into models trained on the corpus. They discuss how Wikipedia's 'neutral point of view' policy doesn't eliminate bias, how this affects downstream AI tools from search to resume screening, and what transparency measures like model cards can—and can't—fix. The conversation stays grounded in a specific example: how an AI trained on Wikipedia might describe a controversial Supreme Court ruling differently depending on which version of the article it learned from.

#AI #Bias #Wikipedia #LargeLanguageModels #PoliticalBias #StanfordResearch #NeutralPointOfView #ModelCards #Technology #Ethics #ArtificialIntelligence #DataBias #ContentModeration #Truth #AITransparency #FexingoBusiness #BusinessPodcast #AIEthics

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Episode 98 of AI Ethics with Fexingo examines the quiet rise of algorithmic rent pricing. Lucas and Luna unpack a specific case from July 2026: a major property management firm using RealPage's AI to set rents across thousands of units, allegedly inflating them by double-digit percentages. We trace how these models ingest competitor data, simulate a soft housing market, and create what some economists call a virtual cartel. Along the way, we ask whether tenants have any legal recourse under existing antitrust law, and whether the FTC's recent algorithmic pricing guidance has any teeth. This is a focused look at one concrete mechanism — the black-box rent optimizer — and what it means for the 44 million renter households in the US.

#AIEthics #TechEthics #RentAlgorithm #RealPage #AlgorithmicPricing #HousingCrisis #Antitrust #FTC #TenantRights #RentGouging #PropertyTech #Proptech #BlackBox #PricingAlgorithms #AIandHousing #ConsumerProtection #FexingoBusiness #BusinessPodcast

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This episode of AI Ethics with Fexingo investigates a new frontier in algorithmic bias: voice analysis in job interviews. Lucas and Luna examine a 2025 study from Stanford's Digital Economy Lab, which found that AI tools assessing 'communication skills' penalized candidates with regional accents and vocal fry at a 40% higher rate — disproportionately affecting women and non-native English speakers. They discuss how companies like HireVue and Aspiring Minds are deploying these systems, the lack of regulatory oversight from the EEOC, and why audio-based profiling may be harder to audit than text-based screening. The hosts also explore whether voice analytics can ever be fair, or if they inherently encode social prejudice. Specific data from the study and a real case of a rejected call-center applicant in Arizona ground the conversation. This episode connects to broader themes of procedural justice, the limits of de-biasing training data, and what companies should consider before adopting voice analysis tools.

#AIEthics #VoiceBias #HiringDiscrimination #AlgorithmicBias #JobInterviews #VocalFry #AccentBias #StanfordStudy #DigitalEconomyLab #HireVue #AspiringMinds #EEOC #WorkplaceFairness #ResponsibleAI #TechPolicy #Discrimination #FexingoBusiness #BusinessPodcast

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Episode 96 of AI Ethics with Fexingo dives into a specific, troubling case: how modern AI models used in mortgage lending and insurance underwriting are absorbing discriminatory patterns from 1930s redlining maps. Lucas explains how the Home Owners' Loan Corporation graded neighborhoods by race, and how those grades now appear in training data for automated valuation models. Luna pushes back on the idea that we can simply 'de-bias' the data, pointing to a 2025 study from the Federal Reserve Bank of Philadelphia that found residual race proxies even in models trained without race labels. The episode explores why fairness metrics alone fail, and what regulators are actually demanding now. No broad history lesson — just one concrete feedback loop that keeps redlining alive in code.

#AIEthics #Redlining #AlgorithmicBias #HousingDiscrimination #MortgageLending #InsuranceUnderwriting #FederalReserve #PhiladelphiaFed #HOLC #FairLending #MachineLearning #Technology #BusinessPodcast #FexingoBusiness #Podcast #AI #DataBias #Regulation

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