Episode 34 of AI Ethics with Fexingo investigates the growing practice of automated resume screening—where AI systems reject job applicants before any human recruiter ever sees their qualifications. Lucas and Luna examine a 2025 study from Harvard Business School that found 72% of large employers now use some form of AI pre-screening, and drill into the case of a Fortune 500 company whose algorithm systematically downgraded candidates who took career breaks, disproportionately affecting women and caregivers. They discuss the lack of regulatory oversight (the EEOC has issued only non-binding guidance), the technical challenge of bias in training data, and whether 'human-in-the-loop' is a real safeguard or just a comforting myth. The episode closes with a practical question: if you apply to a job and never hear back, how do you even know an AI made the call?

#AIEthics #AutomatedHiring #ResumeScreening #AlgorithmicBias #HiringDiscrimination #EEOC #HarvardBusinessSchool #CareerBreaks #GenderBias #CaregiverPenalty #HumanInTheLoop #Transparency #EmploymentLaw #Technology #ArtificialIntelligence #FexingoBusiness #BusinessPodcast #JobSearch

Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore the growing use of fully automated loan underwriting systems that leave borrowers with no human appeal process. They examine a 2025 study from the Consumer Financial Protection Bureau that found 78% of denied loan applicants never learn why their application was rejected by an AI system. The hosts discuss the case of a small business owner in Ohio whose repeat loan applications were automatically denied for eighteen months due to a data error the AI never flagged, and the legal and ethical implications of removing human judgment from lending decisions. They also look at emerging state-level laws requiring explainability and human review, and question whether the efficiency gains justify the loss of accountability.

#AIEthics #ResponsibleAI #AutomatedLending #LoanDenial #CFPB #SmallBusiness #DataBias #Explainability #HumanInTheLoop #AlgorithmicAccountability #ConsumerProtection #LendingDiscrimination #Technology #FexingoBusiness #BusinessPodcast #AIandFinance #NoHumanAppeal #OhioSmallBusinessOwner

Keep every episode free: buymeacoffee.com/fexingo

Episode 32 of AI Ethics with Fexingo examines the unregulated world of AI-powered mental health chatbots. Lucas and Luna discuss a recent study finding that popular therapy AIs exhibit racial and socioeconomic bias in their responses. They dive into the case of a chatbot that recommended different coping strategies based on a user's implied income level, and explore why these systems lack clinical validation. The hosts question whether these tools could do more harm than good for vulnerable users, and what ethical obligations developers have. Specific examples include a 2025 analysis of five leading chatbots by the Digital Wellness Lab, which found that lower-income users were 40% more likely to receive advice to 'accept your situation' versus actionable steps. The episode calls for transparency and regulatory oversight before AI therapists scale further.

#AITherapy #MentalHealthAI #AlgorithmicBias #SocioeconomicBias #RacialBias #ChatbotEthics #DigitalWellnessLab #AIRegulation #ClinicalValidation #TechEthics #AISafety #ResponsibleAI #HealthTech #AIinHealthcare #BiasInAI #FexingoBusiness #BusinessPodcast #Technology

Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna unpack the growing use of AI in rental housing decisions, focusing on a 2025 class action against a major property management firm accused of using an opaque algorithm to screen tenants and recommend evictions. They walk through how the system worked, the data it used (including social media sentiment scores), and what the lawsuit revealed about lack of transparency and disparate impact on minority renters. The episode drills into one specific number: the 34 percent higher eviction filing rate for tenants flagged by the algorithm versus those manually reviewed. They discuss parallels with credit scoring and predictive policing, and close on what regulatory guardrails might look like.

#AIEthics #HousingBias #AlgorithmicEviction #TenantScreening #PropertyTech #PredictiveAlgorithms #FairHousing #ClassActionLawsuit #SocialMediaScoring #DisparateImpact #HousingJustice #TechRegulation #Transparency #DataPrivacy #RacialEquity #Technology #FexingoBusiness #BusinessPodcast

Keep every episode free: buymeacoffee.com/fexingo

In this episode, Lucas and Luna examine a troubling aspect of generative AI: the use of non-consensual intimate images in training datasets. They focus on a 2025 Stanford Internet Observatory report that found more than 3,000 such images in the LAION-5B dataset, which has been used to train models like Stable Diffusion. The hosts discuss how this happened, why it matters for privacy and consent, and what researchers are doing to scrub datasets and build better auditing tools. They also explore the legal landscape, including the UK's Online Safety Act and proposed US legislation, and ask whether the AI industry can self-regulate or needs stronger enforcement. A thoughtful conversation about the hidden costs of open data and the ethics of scraping the web at scale.

#GenerativeAI #NonConsensualImages #DataEthics #StanfordInternetObservatory #LAIONDataset #PrivacyViolation #AITrainingData #ConsentInAI #StableDiffusion #OnlineSafetyAct #WebScraping #AIEnforcement #Technology #AIEthics #FexingoBusiness #BusinessPodcast #DeepfakeLegislation #ImageGeneratorBias

Keep every episode free: buymeacoffee.com/fexingo

Episode 29 of AI Ethics with Fexingo explores the hidden dangers of AI fitness coaching apps. Lucas and Luna dive into a 2025 study from the University of Colorado Boulder that found popular AI-powered workout apps frequently recommend exercises with poor form or excessive intensity, leading to a 40% increase in user-reported injuries over a six-month period. The hosts examine the case of FitAI, a leading app whose algorithm optimized for user engagement rather than safety, and the regulatory gaps that allowed it to operate without oversight. They discuss the tension between personalization and harm, the lack of clinical trials in fitness AI, and what responsibility developers have when their code tells a user to push harder. Listeners learn how to critically evaluate AI recommendations for physical health and why the FDA's reluctance to classify fitness apps as medical devices leaves users vulnerable.

#AIEthics #FitnessAI #HealthTech #AIInjuries #FitAI #WorkoutSafety #AlgorithmicHarm #AIBias #RegulationGap #FDA #ConsumerTech #ResponsibleAI #UniversityOfColorado #TechAccountability #Podcast #Technology #FexingoBusiness #BusinessPodcast

Keep every episode free: buymeacoffee.com/fexingo

This episode explores how AI hiring tools can inherit and amplify gender bias from historical training data. Lucas and Luna dig into a 2024 study by the EU's Fundamental Rights Agency that found an AI recruiting system penalized resumes with female-coded language from women's colleges, reducing callback rates by 12 percent compared to male-coded equivalents. They discuss why gender-blind algorithms don't fix the problem, the limits of 'fairness through unawareness,' and what companies like Amazon learned after scrapping their own biased AI recruiter in 2018. The conversation also touches on current regulatory efforts, including New York City's Local Law 144, which requires bias audits of hiring algorithms, and the practical steps firms can take to audit and retrain models. No hot takes—just a clear-eyed look at one of the most consequential ethical challenges in workplace AI.

#AIEthics #HiringBias #GenderBias #AlgorithmicFairness #RecruitmentTechnology #WorkplaceAI #EUFundamentalRightsAgency #Amazon #LocalLaw144 #BiasAudit #HRTech #ResponsibleAI #EqualEmployment #DataEthics #AIinHR #Technology #FexingoBusiness #BusinessPodcast

Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore the growing use of AI in legal research and document review, focusing on a 2025 incident where an AI-powered legal assistant failed to cite a controlling Supreme Court precedent in a federal appeal. The episode examines how large language models hallucinate case law, the ethical obligations of attorneys who rely on them, and the push for 'certified AI' in litigation. With specific examples from the Mata v. Avianca sanctions order and the new ABA task force guidelines, this conversation gives listeners a concrete understanding of why AI mistakes in the courtroom are different from errors in other professions.

#AIEthics #LegalAI #Hallucination #CaseLaw #LegalTech #AIAccountability #AttorneyEthics #CivilProcedure #SupremeCourt #FexingoBusiness #BusinessPodcast #Technology #GenerativeAI #Regulation #DueDiligence #HarveyAI #Casetext #LexisNexis

Keep every episode free: buymeacoffee.com/fexingo

Episode 26 of AI Ethics with Fexingo dives into the growing use of AI systems that recommend medical treatments—like sepsis management or cancer therapy—without the rigorous clinical trials normally required for drugs or devices. Lucas and Luna examine the case of Epic Systems' Sepsis Model, which was deployed in hundreds of hospitals before a 2021 JAMA study showed it failed to detect sepsis in over two-thirds of cases. They also discuss the FDA's evolving regulatory framework for AI as a Medical Device (SaMD), the tension between rapid deployment and patient safety, and what the recent 2024 FDA guidance on 'predetermined change control plans' really means. The hosts ask: should an algorithm that influences your chemotherapy regimen be held to the same standard as a new pill?

#AIinHealthcare #ClinicalAI #EpicSystems #SepsisModel #JAMA #FDA #SaMD #MedicalEthics #AIbias #PatientSafety #AlgorithmicAccountability #HealthcareAI #Technology #FexingoBusiness #BusinessPodcast #AIEthics #ResponsibleAI #AItrials

Keep every episode free: buymeacoffee.com/fexingo

In episode 25 of AI Ethics with Fexingo, Lucas and Luna explore the hidden world of AI content moderation on social media platforms. They zoom in on a 2025 study from the Algorithmic Justice League that found automated moderation tools disproportionately flag speech from minority communities—misinterpreting dialect, slang, and cultural context as hate speech or misinformation. Lucas breaks down the specific numbers: how a model trained on mainstream English has a 40 percent higher false-positive rate for African American Vernacular English. Luna questions whether transparency reports are really making a difference when the underlying training data remains opaque. They discuss the tension between keeping platforms safe and preserving free expression, and what it means when an AI with no understanding of nuance decides what you can and cannot say online. The episode is grounded in real examples—including the 2024 controversy over TikTok's moderation of LGBTQ+ content in the Middle East—and closes with a look at emerging audit frameworks that could shift the balance toward fairness.

#AIEthics #ContentModeration #AlgorithmicBias #SocialMedia #FreeSpeech #HateSpeechDetection #Misinformation #AfricanAmericanVernacularEnglish #AlgorithmicJusticeLeague #TikTok #LGBTQ #Transparency #TrainingData #AuditFramework #Technology #FexingoBusiness #BusinessPodcast #Fexingo

Keep every episode free: buymeacoffee.com/fexingo