AI Ethics with Fexingo: Bias, Safety, and Responsible Artificial Intelligence

Episode 60 of AI Ethics with Fexingo explores the hidden world of AI-powered personalized tutoring. Lucas and Luna unpack a 2025 Stanford study revealing that adaptive learning platforms like Khan Academy's Khanmigo and Carnegie Learning's MATHia collect granular student data—keystroke patterns, time per question, even emotional tone—to predict performance. But when these systems fail to explain why a student is struggling, they can reinforce stereotypes about 'slow learners' without considering external factors like home environment or learning disabilities. The hosts discuss how one school district in Texas found that its AI tutor flagged Black students as 'low engagement' at twice the rate of white peers, even after controlling for test scores. They also examine the European Union's proposed AI liability directive, which would require educational AI to provide human-readable explanations for their predictions. The episode closes with a call for transparency: if an AI knows your child's weaknesses, shouldn't it at least get their name right? #AIEthics #EdTech #AdaptiveLearning #StudentPrivacy #AlgorithmicBias #Khanmigo #CarnegieLearning #MATHia #StanfordStudy #TexasSchoolDistrict #EULiabilityDirective #EducationalAI #PersonalizedLearning #DataCollection #LearningAnalytics #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

In this episode of AI Ethics with Fexingo, Lucas and Luna explore the hidden bias in AI-powered healthcare referral systems. They focus on a 2025 study by the University of California, San Francisco, which found that three major hospital networks' AI referral algorithms recommended expensive specialists 40% more often to patients with private insurance than to those with Medicaid, even when clinical need was identical. The hosts discuss how training data from historical referral patterns encoded financial incentives, the lack of transparency in vendor algorithms, and why fixing this isn't just about adding a fairness constraint — it requires rethinking what data goes into the model. Lucas shares a concrete case: a woman in Phoenix whose AI triage system suggested a $2,000 imaging study for a condition that later turned out to be a simple vitamin deficiency. They also consider the regulatory landscape, including a recent CMS proposal to audit referral algorithms. The episode ends with a reflective question about whether efficiency metrics are masking systemic inequity. #AIEthics #HealthcareAI #AlgorithmicBias #Medicaid #InsuranceBias #ReferralAlgorithms #UCSF #ClinicalDecisionSupport #HealthEquity #AIAccountability #MedicalAI #BiasInHealthcare #FexingoBusiness #BusinessPodcast #Technology #Podcast #DataEthics #Regulation Keep every episode free: buymeacoffee.com/fexingo

In this episode of AI Ethics with Fexingo, Lucas and Luna explore how resume-scanning AI systems may systematically filter out military veterans. They examine a 2025 study from the University of Maryland that found a 23 percent lower callback rate for veteran applicants when screened by algorithmic tools versus human reviewers. The hosts discuss how training data and keyword optimization can encode hidden biases against non-linear career paths, and what companies like Microsoft and Amazon are doing to audit their hiring models. Lucas explains the concept of 'skill translation gaps' in natural language processing, and Luna pushes back on whether quick fixes like adding military keywords actually solve the problem or just mask it. This episode offers a concrete look at a less-discussed fairness issue in AI-driven hiring. #AIEthics #HiringBias #Veterans #ResumeScanning #AlgorithmicFairness #UniversityOfMaryland #Microsoft #Amazon #SkillTranslation #NLP #Employment #Technology #FexingoBusiness #BusinessPodcast #AI #Bias #VeteranEmployment #HRTech Keep every episode free: buymeacoffee.com/fexingo

Episode 57 of AI Ethics with Fexingo examines the risks of AI-driven fitness coaching. Lucas and Luna discuss a recent incident where an AI fitness app recommended dangerous exercises to a user with a known heart condition, leading to hospitalization. They explore the lack of medical oversight in algorithmically generated workout plans, the legal gray area of liability when AI gives health advice without a license, and the broader implications for wearable tech and health optimization platforms. The episode dives into training data biases, the absence of regulatory frameworks, and what accountability looks like when algorithms replace human trainers. With specific references to the case of a 45-year-old user in Texas and the app's defense that its algorithms only 'suggest' routines, Lucas and Luna unpack the ethical responsibility of companies deploying AI in health-sensitive domains. Tune in for a nuanced conversation about safety, transparency, and the limits of artificial intelligence in personal fitness. #AIFitness #HealthTech #Wearables #AlgorithmicBias #Safety #Liability #MedicalAI #PersonalTraining #Ethics #Regulation #Transparency #Accountability #Technology #AISafety #FitnessCoaching #DataBias #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 56 of AI Ethics with Fexingo dives into the world of AI-powered dating apps. Lucas and Luna explore how algorithms behind platforms like Tinder and Hinge use facial recognition and attractiveness scoring to match users. They discuss a 2025 Stanford study showing that AI can predict dating success with 68% accuracy based on profile photos alone, raising questions about bias, privacy, and the gamification of romance. The hosts unpack the controversy around apps that silently rank users by perceived attractiveness, the lack of transparency in these systems, and what happens when rejection isn't just human—it's algorithmic. Tune in for a sharp, skeptical look at how AI is reshaping love, one swipe at a time. #AI #Ethics #DatingApps #FacialRecognition #AttractivenessScoring #AlgorithmicBias #Privacy #StanfordStudy #Tinder #Hinge #OnlineDating #MachineLearning #Fairness #Transparency #Technology #FexingoBusiness #BusinessPodcast #AIEthicsWithFexingo Keep every episode free: buymeacoffee.com/fexingo

In this episode of AI Ethics with Fexingo, Lucas and Luna explore the hidden dangers of predictive algorithms in the criminal justice system. They focus on a real 2023 case from Rhode Island where a sentencing algorithm recommended a longer prison term because a probation officer misspelled a defendant's name, causing the system to pull in decades-old records from a different person. The hosts discuss how such errors are not bugs but features of brittle systems, why 'garbage in, gospel out' persists in courtrooms, and what reforms are gaining traction in 2026, including the Pretrial Integrity Act. They also touch on the broader problem of algorithmic opacity in government decision-making and why transparency alone isn't a fix. A sobering look at how AI can amplify human error when deployed without adequate safeguards. #AIEthics #CriminalJustice #SentencingAlgorithms #RhodeIsland #AlgorithmicBias #PatrickDahm #DataQuality #PretrialIntegrityAct #PredictivePolicing #DueProcess #Transparency #AlgorithmicAccountability #JusticeReform #MachineLearning #Technology #TechPodcast #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

In this episode of AI Ethics with Fexingo, Lucas and Luna explore a disturbing case: a customer-support AI copilot accidentally sharing a user's private chat history with a stranger. They break down how retrieval-augmented generation (RAG) systems can leak sensitive data when context windows are misconfigured, and what that means for trust in enterprise AI. Drawing on a real incident from early 2026 involving a major telecom's support bot, they discuss the technical root cause—a prompt-injection-like bug in the vector database lookup—and the broader implications for privacy regulation. Lucas explains why current auditing tools often miss these 'shadow leaks,' and Luna questions whether companies are moving too fast to deploy AI without robust red-teaming. A sharp, practical conversation about the hidden risks of conversational AI in customer service. #AIEthics #AIBias #AISafety #ResponsibleAI #TechPolicy #Privacy #DataLeak #RetrievalAugmentedGeneration #RAG #PromptInjection #CustomerServiceAI #AICopilot #EnterpriseAI #AIRedTeaming #TechRegulation #FexingoBusiness #BusinessPodcast #Technology Keep every episode free: buymeacoffee.com/fexingo

Episode 53 of AI Ethics with Fexingo examines the privacy risks of AI-powered mental health chatbots. Lucas and Luna discuss a 2025 study where an AI therapist platform inadvertently shared session data with advertisers, citing a case involving a user whose therapy transcripts were used to target ads for antidepressants and sleep aids. They explore how current regulations like HIPAA in the US and GDPR in Europe fall short for AI-generated emotional data, and what safeguards are needed. The hosts also touch on the tension between improving AI therapy through data collection and protecting user privacy, asking whether the convenience of AI therapy is worth the potential breach of trust. #AIEthics #Technology #Privacy #MentalHealth #AIChatbot #Therapy #HIPAA #GDPR #DataPrivacy #AlgorithmicBias #TechEthics #DigitalHealth #EmotionalData #InformedConsent #AISafety #FexingoBusiness #BusinessPodcast #AITherapy Keep every episode free: buymeacoffee.com/fexingo

Episode 52 of AI Ethics with Fexingo dives into how AI grading tools in art education can penalize students for creative expression, using a real case from a university digital arts program where an AI consistently gave low scores to works with unconventional brushstrokes. Lucas and Luna explore how training data biases can suppress artistic diversity, what happened when students appealed, and the broader implications for creative fields relying on automated assessment. They discuss the difficulty of training AI on subjective aesthetics and why human oversight remains critical in subjective domains. #AIEthics #ArtEducation #CreativeAI #Bias #AutomatedGrading #DigitalArts #University #StudentRights #ArtisticFreedom #MachineLearning #TrainingData #HumanOversight #EdTech #BiasInAI #Technology #FexingoBusiness #BusinessPodcast #AI Keep every episode free: buymeacoffee.com/fexingo

Episode 51 explores a new frontier of algorithmic bias: predictive policing systems that assign risk scores to neighborhoods based on historical arrest data, creating feedback loops that reinforce over-policing in communities of color. Lucas and Luna break down a 2025 study from the AI Now Institute analyzing Palantir's Gotham platform in three mid-sized cities. They uncover how 'hot spot' predictions trained on biased reporting data lead to more patrols, more arrests, and more biased data. The episode delves into the technical mechanism of label bias, the difficulty of auditing opaque contracts, and the growing movement for community oversight of public-sector AI. Specific numbers include a 34% higher false-positive rate in low-income census tracts and a 22% increase in misdemeanor arrests after system deployment. No prior episode covered predictive policing specifically, making this a timely and urgent angle for listeners concerned about fairness in AI systems. #PredictivePolicing #AlgorithmicBias #AIEthics #CriminalJustice #Palantir #GothamPlatform #RacialBias #FeedbackLoop #LabelBias #PublicSectorAI #AIAccountability #CommunityOversight #DataJustice #PoliceReform #AI2025 #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Self-driving cars are already on public roads in several US cities, but who decides what the car does when an accident is unavoidable? In this episode, Lucas and Luna explore the trolley problem as it applies to autonomous vehicles. They examine the MIT Moral Machine study, which logged over 40 million decisions from people worldwide, revealing that different cultures have starkly different preferences about who a car should sacrifice. They discuss why carmakers like Mercedes have already announced they'll prioritize occupants, how regulators are struggling to catch up, and what happens when an AI's split-second choice leads to a lawsuit. The episode also touches on the deeper ethical question: should we even let machines make life-or-death decisions, or are we outsourcing responsibility to avoid facing hard choices ourselves? #AutonomousVehicles #SelfDrivingCars #TrolleyProblem #AIEthics #MoralMachine #MIT #MercedesBenz #MachineEthics #AutonomousDriving #AIAccountability #Technology #ArtificialIntelligence #Ethics #SelfDriving #Regulation #FexingoBusiness #BusinessPodcast #AI Keep every episode free: buymeacoffee.com/fexingo

Episode 49 of AI Ethics with Fexingo explores the controversial use of AI to predict life expectancy for insurance underwriting. Lucas and Luna dissect a 2025 case where a major European insurer used a machine learning model trained on lifestyle data, genetic markers, and social media activity to set premiums. They examine the accuracy claims, the regulatory backlash from the EU's AI Act enforcement, and the ethical dilemma of 'fair pricing' vs. 'fair risk assessment'. Specific examples include a 34-year-old cyclist who was charged a 40% premium surcharge because the model correlated her Instagram posts about marathon training with 'risk-seeking behavior'. The hosts discuss transparency requirements, the right to explanation, and whether predictive models can ever be truly 'actuarially fair' when they reinforce systemic biases. This episode drills into one concrete case to ask: when insurers know too much, who gets left out? #AIEthics #InsuranceAI #LifeExpectancyPrediction #AlgorithmicBias #EIAIAct #PredictiveUnderwriting #DataPrivacy #LifestyleData #GeneticData #SocialMediaAnalysis #FairnessInInsurance #RiskAssessment #RightToExplanation #EUAIEnforcement #Technology #AIPolicy #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 48 of AI Ethics with Fexingo dives into a surprisingly common and deeply frustrating bias: accent misidentification in voice AI. Lucas and Luna explore a 2025 Stanford study showing that speech-to-text systems from major providers misrecognize words from speakers with African American Vernacular English and Southern U.S. accents up to 30 percent more often than from standard American English speakers. They discuss the real-world consequences, from faulty transcription in legal depositions to a home assistant ordering the wrong item because it couldn't parse a user's regional pronunciation of 'pecan.' The hosts also examine why this bias persists: training data that over-represents certain accents and under-represents others, and the difficulty of fixing it without massive data collection. They consider whether users should have the right to know what accent data was used to train their voice AI, and what accountability looks like for companies deploying these systems in critical settings like healthcare and law enforcement. A thoughtful, concrete look at an invisible bias affecting millions of users every day. #AccentBias #VoiceRecognition #SpeechToText #AIEthics #AlgorithmicBias #AfricanAmericanVernacularEnglish #SouthernAccent #StanfordStudy #TechPodcast #ArtificialIntelligence #NaturalLanguageProcessing #DataBias #ConsumerRights #LegalTech #HealthcareAI #FexingoBusiness #BusinessPodcast #AIAccountability Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna dive into a growing but overlooked AI ethics problem: legal AI tools that give confident but wrong answers. They examine a real case from early 2026 where a small business owner relied on an AI legal document generator—only to find the contract it produced was unenforceable in her state. The hosts explore why these errors happen (training data gaps, lack of jurisdiction awareness), why they're dangerous (people trust legal AI more than other kinds), and what regulators and companies are starting to do about it. They also discuss the subtle liability question: if an AI gives bad legal advice, who's responsible—the developer, the platform, or the user? A concrete, non-alarmist look at an issue that affects anyone who's ever considered using AI to write a will, review a lease, or draft a non-disclosure agreement. #AIEthics #LegalTech #AIRegulation #GenerativeAI #Liability #LawAndTech #SmallBusiness #ContractLaw #AITraining #AIBias #TechAccountability #LegalAI #FexingoBusiness #BusinessPodcast #Technology #AISafety #Jurisdiction #LegalAdvice Keep every episode free: buymeacoffee.com/fexingo

In this episode of AI Ethics with Fexingo, hosts Lucas and Luna explore the hidden fees embedded in AI-powered investment advisors. They break down how algorithms from major platforms like Wealthfront and Betterment can steer clients into higher-cost funds or generate unnecessary trades to boost platform revenue, often without transparent disclosure. The conversation centers on a real case from 2025 where the SEC fined a robo-advisor for failing to disclose conflicts of interest in its AI-driven portfolio recommendations. Lucas explains the mechanics of revenue-sharing agreements and 'kickback' structures, while Luna questions whether current regulations are keeping pace with AI's ability to personalize fee extraction. They discuss the tension between fiduciary duty and profit incentives, and what investors can do to protect themselves. The episode also touches on broader implications for AI ethics, including the need for algorithmic transparency and a new kind of audit trail for financial advice. No stock picks here — just a clear-eyed look at who really benefits when a machine manages your money. #AIEthics #RoboAdvisor #HiddenFees #InvestmentAdvisor #Wealthfront #Betterment #SEC #FiduciaryDuty #AlgorithmicBias #FinancialAdvice #RevenueSharing #ConflictOfInterest #Transparency #ConsumerProtection #AIAccountability #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 45 of AI Ethics with Fexingo drills into a real case from this past April: a property management firm in Cleveland used an AI system to flag lease violations and auto-generate eviction notices. Over 200 tenants received notices without a single human verifying the AI's judgment. We trace how the system worked, what went wrong, and why this matters for the push toward algorithmic tenant screening. Lucas and Luna discuss the legal gray zone, the lack of federal oversight, and what a fairer system might look like. Specific numbers: 87% of contested notices were withdrawn when a human actually reviewed the evidence. The episode ends on what this means for the broader 'automated landlord' trend. #AIEthics #AlgorithmicBias #EvictionTech #TenantRights #HousingJustice #Cleveland #PropertyTech #AutomatedDecisionMaking #FairHousing #LandlordTech #AIAccountability #Technology #FexingoBusiness #BusinessPodcast #LucasAndLuna #ResponsibleAI #HumanInTheLoop #HousingPolicy Keep every episode free: buymeacoffee.com/fexingo

Episode 44 of AI Ethics with Fexingo examines a growing concern in healthcare AI: algorithm developers with financial ties to the treatments their models recommend. Lucas and Luna dissect a 2025 study from the Journal of Medical Ethics that found 30 percent of clinical AI tools are co-developed by pharmaceutical companies whose drugs the algorithms prioritize. They explore the case of an AI sepsis-detection system that nudged clinicians toward a specific antibiotic brand, and discuss why the FDA's current clearance process for AI-based software as a medical device doesn't require disclosure of sponsorship. The hosts also touch on how hospital procurement teams rarely check for these conflicts, and what a new bipartisan bill in Congress proposes to require transparency. A concrete look at how financial incentives quietly shape the algorithms that inform your treatment. #AIEthics #HealthcareAI #ConflictOfInterest #MedicalBias #Pharma #SepsisAlgorithm #FDAClearance #AlgorithmicTransparency #ClinicalAI #JournalOfMedicalEthics #Policy #Congress #BipartisanBill #Technology #FexingoBusiness #BusinessPodcast #ResponsibleAI #AI Keep every episode free: buymeacoffee.com/fexingo

In this episode of AI Ethics with Fexingo, Lucas and Luna explore the growing use of AI-powered 'people analytics' tools that rank and predict employee performance based on passive data—email patterns, Slack messages, calendar activity—often without explicit consent. They examine a 2025 case where a Fortune 500 retailer used a system from a vendor called Workforce AI to flag 'low-potential' workers, leading to a class-action lawsuit in California. The conversation digs into the ethical gap between what companies claim (efficiency, fairness) and what the data actually captures (noise, bias, surveillance). Lucas argues that treating workplace communication as a free training set for AI violates basic employee privacy, while Luna questions whether any consent model can work when the power imbalance between employer and employee is so stark. They also discuss the European Union's proposed Workplace AI Directive and the limits of transparency. The episode closes with a reflection on what accountability looks like when the algorithm is a black box. #WorkplaceAI #PeopleAnalytics #EmployeeSurveillance #AIBias #PerformancePrediction #Privacy #Consent #Fortune500 #WorkforceAI #ClassAction #California #EUWorkplaceAIDirective #BlackBox #Ethics #Technology #FexingoBusiness #BusinessPodcast #AIPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 42 of AI Ethics with Fexingo examines the growing use of AI grading assistants in college classrooms. Lucas and Luna break down a specific case from Georgia State University, where an automated essay-scoring system was found to reward keyword stuffing and penalize creative writing. They discuss how the system—trained on past student essays—developed a bias toward formulaic structure and against non-native English speakers. The hosts explore why universities are adopting these tools, what the research on grading accuracy shows, and whether a human appeal process is enough to catch systemic errors. They also touch on the broader question: when efficiency in education comes at the cost of fairness, who is accountable? A focused, 10-minute conversation on an issue affecting thousands of students today. #AIGrading #EducationTechnology #BiasInAI #GeorgiaStateUniversity #AutomatedEssayScoring #AlgorithmicBias #HigherEd #EdTech #AIEthics #FairnessInEducation #StudentPrivacy #NonNativeEnglishBias #Technology #Business #AI #FexingoBusiness #BusinessPodcast #ResponsibleAI Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna examine the risks of AI-powered symptom checkers and health chatbots that provide medical advice without regulatory oversight. They focus on a 2025 study from Stanford Medicine that tested four popular symptom-checker apps—including WebMD's Symptom Checker and Babylon Health—and found they gave correct diagnoses only 32% of the time, while recommending potentially harmful actions 18% of the time. The hosts discuss the lack of FDA clearance for most health AI tools, the legal liability gap, and what this means for patients who trust AI over a real doctor. They also explore how tech companies are lobbying to keep health AI unregulated, and what the new EU AI Act might mean for medical chatbot transparency. A concrete episode about the gap between convenience and safety in digital health. #AIEthics #HealthAI #MedicalChatbots #SymptomCheckers #StanfordMedicine #BabylonHealth #WebMD #FDAClearance #EUAIAct #PatientSafety #DiagnosticError #AIBias #RegulatoryGap #DigitalHealth #TechRegulation #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 40 of AI Ethics with Fexingo: Lucas and Luna explore the legal risks of over-relying on generative AI for professional advice. Using the recent case of a lawyer who submitted fake citations from ChatGPT, they examine why AI models hallucinate legal and medical references, how the fine print disclaims responsibility, and what listeners can do to protect themselves. Specific discussion of the 2023 Mata v. Avianca ruling, the OpenAI terms of service, and the concept of 'alignment tax' between helpfulness and accuracy. Practical guardrails for anyone using AI for work decisions. #AIEthics #AIHallucination #GenerativeAI #LegalAI #ChatGPT #MataVsAvianca #AIDisclaimers #BusinessPodcast #FexingoBusiness #Technology #LucasAndLuna #AIRegulation #ProfessionalAdvice #OpenAI #AIAccountability #EthicalAI #TechPolicy #ResponsibleAI Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna examine how AI systems used in hiring and performance reviews can learn from past interactions and hold negative associations against candidates. They explore a 2025 study from the MIT Media Lab showing that a simulated recruiter AI penalized candidates whose profiles were similar to previously rejected applicants, even when those candidates had objectively stronger qualifications. The hosts discuss the feedback loop problem—where an AI 'remembers' a bad hire and then unfairly downgrades similar candidates for years—and how this creates a hidden bias that is harder to detect than standard demographic bias. They also talk about potential fixes, including 'memory reset' policies and bias audits that check for this kind of learned bias, and why transparency in hiring AI is still lacking. #AIEthics #HiringAI #AlgorithmicBias #ArtificialIntelligence #Recruitment #JobSearch #HRTech #BiasInAI #AITransparency #MITMediaLab #FairHiring #Employment #Technology #FexingoBusiness #BusinessPodcast #ResponsibleAI #AISafety #WorkplaceBias Keep every episode free: buymeacoffee.com/fexingo

Episode 38 of AI Ethics with Fexingo. Lucas and Luna examine the real-world consequences of automated proctoring software used in remote exams. They focus on a specific case: a university student who was flagged for eye movement by an AI proctor, had her exam invalidated, and spent months appealing with no human oversight. The hosts discuss the accuracy of these systems, bias in gaze detection, and the lack of due process. They also explore the broader implications for surveillance in education and whether the technology actually deters cheating or just punishes nervous behavior. A concrete look at how AI is reshaping academic integrity. #AIEthics #AutomatedProctoring #RemoteExams #AcademicIntegrity #BiasInAI #FacialRecognition #Surveillance #EdTech #StudentRights #DueProcess #GazeDetection #FalsePositives #CheatingDetection #HigherEd #Technology #FexingoBusiness #BusinessPodcast #Podcast Keep every episode free: buymeacoffee.com/fexingo

Episode 37 of AI Ethics with Fexingo explores a hidden bias in hiring algorithms: using estimated commute distance as a proxy for job stability or socioeconomic status. Lucas and Luna dissect a 2025 Stanford study that found AI recruitment tools penalized applicants from lower-income neighborhoods by flagging longer commutes as a risk factor—even when those candidates were more qualified. They discuss the legal implications under the EEOC's 2024 guidance on algorithmic fairness, the difficulty of auditing black-box models for proxy discrimination, and why removing commute data alone isn't a fix. The hosts also touch on similar proxy issues in lending and insurance AI, and what engineers and policymakers can do to prevent fairness-washing. #AIEthics #HiringBias #AlgorithmicFairness #ProxyDiscrimination #RecruitmentAI #StanfordStudy #EEOC #ResponsibleAI #CommuteBias #SocioeconomicBias #BlackBoxAuditing #FairnessWashing #Technology #Business #FexingoBusiness #BusinessPodcast #LucasAndLuna #AIAccountability Keep every episode free: buymeacoffee.com/fexingo

Episode 36 of AI Ethics digs into the privacy nightmare of smart home devices used for childcare. Lucas and Luna examine a recent incident where a popular smart speaker recorded a family's private conversation and sent it to a random contact. They discuss the lack of transparency in how these devices process audio, the loopholes in current privacy laws, and what parents can do to protect their children's data. With smart speakers in over 50% of US homes, the episode asks: are we trading safety for surveillance? #AIEthics #SmartHomePrivacy #ChildSafety #VoiceAssistant #DataPrivacy #TechRegulation #AmazonAlexa #GoogleAssistant #PrivacyLaws #COPPA #FTC #GDPR #SurveillanceCapitalism #ParentingTech #AudioRecording #FexingoBusiness #BusinessPodcast #Technology Keep every episode free: buymeacoffee.com/fexingo

Episode 35 of AI Ethics with Fexingo dives into the growing use of artificial intelligence in insurance claims processing. Lucas and Luna examine a specific case: a homeowner whose basement flood claim was automatically denied by an AI model that misread the policy language. The hosts discuss how these systems are trained on historical claims data that may encode racial and socioeconomic biases, leading to disproportionately high denial rates for certain neighborhoods. They explore the lack of transparency in algorithmic underwriting, the difficulty of appealing a decision made by a black-box model, and the regulatory gap that leaves consumers with little recourse. The episode also highlights a recent study showing that AI adjusters are 40 percent more likely to flag claims from predominantly Black and Hispanic zip codes for manual review — often resulting in lower payouts. Lucas and Luna ask whether insurance companies are trading fairness for efficiency, and what a responsible path forward might look like. #AIinInsurance #ClaimsDenial #AlgorithmicBias #InsuranceTech #ConsumerRights #Regulation #BlackBoxAI #Fairness #Transparency #HomeownersInsurance #PropertyClaims #Discrimination #AIEthics #PolicyLanguage #Underwriting #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

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

Episode 24 of AI Ethics with Fexingo investigates the growing use of AI-powered interview analysis tools like HireVue and Retorio. Lucas and Luna dive into a 2025 academic study revealing that many of these systems don't just evaluate what you say, but infer personality traits, emotional states, and even political leanings from your voice tone and facial expressions—without your knowledge or consent. The episode focuses on a specific case: a university applicant rejected after an AI interview flagged her as 'low conscientiousness' based on a single pause in her response. The study tested five commercial platforms and found that four encoded race and gender proxies in their scoring. The hosts discuss how the lack of transparency violates basic principles of informed consent, and what the Equal Employment Opportunity Commission's new 2026 guidance on AI hiring tools actually requires. A concrete, unsettling look at the automated gatekeepers already sitting in HR departments across the country. #AIEthics #HiringBias #HireVue #Retorio #AIInterview #FacialRecognition #EmotionAI #Privacy #EmploymentLaw #EEOC #AlgorithmicBias #Consent #PersonalityAssessment #VoiceAnalysis #Technology #FexingoBusiness #BusinessPodcast #JobSearch Keep every episode free: buymeacoffee.com/fexingo

Episode 23 of AI Ethics with Fexingo explores the hidden world of applicant tracking systems (ATS) that automatically reject 75% of résumés before a human ever sees them. Lucas and Luna dive into how these AI filters, trained on historical hiring data from companies like Amazon and Unilever, can encode subtle biases against women, older workers, and non-traditional career paths. We break down the case of Amazon's 2015 automated hiring tool that penalized résumés containing the word 'women's' and discuss why today's ATS software still struggles with gaps in employment, unconventional job titles, and even font choices. The episode also covers emerging regulation like New York City's Local Law 144, which mandates bias audits for automated hiring systems, and the practical steps job seekers can take to 'beat the bot.' Throughout, the hosts balance skepticism of pure automation with recognition that well-designed AI can widen talent pools. The conversation closes with a reflection on fairness and human dignity in the job market. #ApplicantTrackingSystem #ATS #HiringBias #ResumeFiltering #AutomatedHiring #Amazon #Unilever #LocalLaw144 #NYCBiasAudit #JobSearch #AIEthics #BiasInAI #EmploymentDiscrimination #Technology #FexingoBusiness #BusinessPodcast #AlgorithmicFairness #ResponsibleAI Keep every episode free: buymeacoffee.com/fexingo

Most AI ethics debates assume your data has to leave your device. But a growing number of AI models now run entirely on your phone or laptop — no cloud, no server, no company seeing your selfies or search history. In this episode, Lucas and Luna look at Apple's on-device foundation model, Google's Gemini Nano, and how Qualcomm's latest mobile chips change the privacy equation. They ask whether 'edge AI' genuinely reduces harm or just shifts risk from data collection to model behavior. And they get into a real-world case: a mental-health chatbot that runs locally, where no one — not even its developers — can read what users type. Is this the future of safe AI, or a new blind spot? #OnDeviceAI #EdgeAI #Apple #Google #Qualcomm #PrivacyByDesign #FederatedLearning #MentalHealthChatbot #LocalProcessing #DataMinimization #AIEthics #ResponsibleAI #LLMOnMobile #GeminiNano #Technology #FexingoBusiness #BusinessPodcast #AIRegulation Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore the hidden crisis in AI-powered education: tutoring systems that claim to personalize learning but lack any pedagogical training. They examine the case of Khan Academy's Khanmigo, an AI tutor rolled out in 2023 that sometimes gives students confident but wrong answers. The conversation digs into why AI tutors fail at the basics of teaching—like knowing when a student is stuck, or how to ask a leading question instead of giving away the answer. They also discuss the growing pressure on schools to adopt AI tools without evidence that they improve learning outcomes. This episode offers a critical look at the gap between ed-tech marketing and actual educational research, asking whether we're automating a profession we don't fully understand. #AIinEducation #Khanmigo #EdTech #AIBias #Pedagogy #KhanAcademy #PersonalizedLearning #Teaching #AISafety #EducationPolicy #EdTechCritique #LearningSciences #SalKhan #AIPromises #StudentOutcomes #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

This episode examines the rise of AI-generated educational content in art classrooms. Lucas and Luna dig into a case where a school district replaced most of its art curriculum with a generative AI system that creates lesson plans, project prompts, and even grading rubrics. The problem: the AI was trained on a dataset of art criticism and history that heavily favored Western, male, and canonical artists, leading to assignments that dismissed or misrepresented non-Western art forms. Luna questions whether the cost savings justify the loss of human mentorship, while Lucas ties the issue to broader debates about algorithmic gatekeeping in education. They discuss a 2025 study finding that students using the AI system scored higher on technical skills but showed a measurable drop in creative risk-taking and cultural awareness. The episode closes with a look at what genuine AI literacy in arts education should look like. #AIEthics #ArtEducation #GenerativeAI #BiasInAI #AlgorithmicBias #CreativeAI #CurriculumBias #EdTech #AIArtTeacher #CulturalRepresentation #EducationPolicy #WesternCanon #ArtificialIntelligence #ResponsibleAI #FexingoBusiness #BusinessPodcast #Technology #AIinSchools Keep every episode free: buymeacoffee.com/fexingo

In this episode, Lucas and Luna explore the growing use of AI in rental housing decisions — from tenant screening to eviction predictions. They focus on the case of SafeRent, a scoring algorithm that assigns rental risk scores to tenants, and how it has been challenged in court for alleged racial bias. The hosts discuss a 2023 lawsuit by the Massachusetts Attorney General against SafeRent, the company's scoring methodology, and the broader implications for algorithmic fairness in housing. They also touch on the federal Fair Housing Act and whether AI systems can be audited for discriminatory impact. The episode ends with a reflection on the need for transparency and regulation in AI-driven housing decisions. #AIEthics #HousingBias #SafeRent #TenantScreening #AlgorithmicBias #FairHousing #RacialDiscrimination #EvictionTech #MassachusettsAG #FairHousingAct #PredictiveAnalytics #HousingJustice #TechRegulation #AIAccountability #FexingoBusiness #BusinessPodcast #Technology #ResponsibleAI Keep every episode free: buymeacoffee.com/fexingo

In this episode, Lucas and Luna explore the growing use of AI in child welfare assessments, focusing on the Allegheny Family Screening Tool (AFST) in Pennsylvania. They discuss how predictive models score families for risk of child maltreatment, the documented racial disparities in these systems, and the tension between efficiency and fairness. The hosts examine a 2023 study showing that AFST can reduce foster care placements but also reinforces existing biases in reporting. They consider the broader implications of automating high-stakes decisions about family separation, and what safeguards might be needed. #AIEthics #ChildWelfare #PredictiveModeling #AlleghenyFamilyScreeningTool #AFST #RacialBias #AlgorithmicBias #FosterCare #ChildProtectiveServices #ResponsibleAI #BiasInAI #GovernmentTech #SocialServices #EthicalAI #Technology #Business #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

This episode of AI Ethics with Fexingo looks at how AI tutoring platforms like Khan Academy's Khanmigo and others are being deployed in classrooms without the pedagogical training human teachers receive. We walk through a specific incident from spring 2026 where an AI tutor in a Los Angeles middle school gave a student incorrect and potentially misleading information about the French Revolution. We explore the underlying issue: these systems are built on large language models optimized for fluent text generation, not for curriculum alignment or developmental appropriateness. We discuss what happens when schools outsource lesson delivery to AI without oversight, and ask whether these tools are truly 'tutoring' or just advanced autocomplete. Lucas and Luna also talk about a recent study showing that students who relied heavily on AI tutors scored lower on surprise assessments than peers who used traditional methods. The episode ends with a question about who is accountable when an AI tutor teaches something wrong. #AIEthics #EdTech #KhanAcademy #Khanmigo #AIinEducation #AITutor #Pedagogy #BigTech #LosAngeles #FrenchRevolution #LargeLanguageModels #Curriculum #StudentOutcomes #Accountability #Technology #FexingoBusiness #Fexingo #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

In episode 16 of AI Ethics with Fexingo, Lucas and Luna examine how artificial intelligence is being used to recommend sentencing lengths in criminal courts. They focus on a 2025 study from the University of Michigan analyzing over 200,000 sentencing recommendations from a tool used in four states. The hosts discuss how the model was trained on historical sentencing data that already reflected racial disparities, leading to recommendations that were on average 12 percent longer for Black defendants compared to white defendants with similar charges and prior records. They also explore the tension between efficiency and fairness, the lack of transparency in proprietary algorithms, and what judges and policymakers are doing to address the issue. A thought-provoking episode for anyone interested in the real-world consequences of AI in high-stakes decisions. #AIEthics #CriminalJustice #SentencingAI #AlgorithmicBias #RacialDisparity #PredictiveJustice #MichiganStudy #ProsecutorialTools #JudicialDiscretion #ExplainableAI #FairnessInAI #CriminalLaw #DataBias #LegalTech #CivilRights #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 15 of AI Ethics with Fexingo explores the hidden world of automated grading in higher education. Lucas and Luna unpack a 2025 Stanford study that found AI grading systems penalize non-native English speakers by up to 15% in essay evaluations. They dive into the specific case of Arizona State University's 2024 pilot of an automated essay scoring tool, which led to a 23% higher fail rate among first-generation students. The conversation covers the bias baked into training data, the lack of transparency in commercial grading algorithms, and what universities can do to ensure fair assessment. Tune in for a concrete look at how invisible algorithms are shaping student outcomes and what it means for the future of education. #AIEthics #AutomatedGrading #EducationBias #HigherEd #StanfordStudy #ArizonaStateUniversity #AlgorithmicBias #EdTech #StudentFairness #EssayScoring #NonNativeSpeakers #FirstGenerationStudents #Transparency #ResponsibleAI #Technology #FexingoBusiness #BusinessPodcast #EthicsInTech Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna investigate how artificial intelligence has quietly taken over consumer credit scoring in the United States, focusing on the case of a 2023 lawsuit against a major credit bureau that used a machine learning model to set credit limits. They walk through how the model penalized customers who used certain types of retail credit cards, why regulators are struggling to audit these systems, and what the 'right to explanation' movement means for borrowers. The episode centers on a specific finding from a 2025 Federal Reserve study showing that 18 percent of credit denials from AI-driven systems could not be explained by traditional factors like payment history or debt-to-income ratio. No marketing fluff, just the mechanics of how an algorithm decides whether you qualify for a loan. #AIEthics #CreditScoring #ConsumerProtection #AlgorithmicBias #MachineLearning #CreditBureaus #FairLending #RightToExplanation #FederalReserve #Technology #Business #Finance #FexingoBusiness #BusinessPodcast #AI #Bias #Regulation #Lending Keep every episode free: buymeacoffee.com/fexingo

Episode 13 of AI Ethics with Fexingo digs into the growing use of AI in insurance claims processing. Lucas and Luna examine a specific case from May 2026: a major US insurer's automated system that denied flood damage claims after Hurricane Delta with an error rate of 34 percent. They discuss how algorithmic bias against older homeowners and non-English speakers persists even when regulators demand transparency, and why the insurance industry's 'black box' problem is different from other AI ethics issues. The conversation touches on state-level legislation in Florida and California forcing audits, and the tension between faster payouts and fair outcomes. No abstract philosophizing—just the real-world mess of AI deciding who gets paid and who doesn't. #AIEthics #InsuranceAI #ClaimsProcessing #AlgorithmicBias #HurricaneDelta #InsuranceRegulation #FloridaLaw #CaliforniaLaw #AIAccountability #BlackBoxAI #EthicalAI #FexingoBusiness #BusinessPodcast #Technology #AIAudits #ConsumerProtection #InsuranceFraud #FairClaims Keep every episode free: buymeacoffee.com/fexingo

In this episode, Lucas and Luna examine how algorithms are increasingly used to set bail amounts and predict flight risk in the U.S. criminal justice system. They focus on the specific case of New Jersey, which reformed its bail system in 2017 to rely on a risk-assessment tool called the Public Safety Assessment (PSA). The hosts walk through how the PSA works, the data it uses, and the troubling findings from a 2024 study that showed the tool underestimated risk for Black defendants by over 10 percent compared to white defendants. The conversation explores the tension between efficiency and fairness, the opacity of proprietary algorithms, and the difficulty of auditing these systems when the code is a trade secret. Lucas and Luna also draw broader lessons about the lifecycle of AI in high-stakes public policy, from initial enthusiasm to later scrutiny and potential reform. This episode includes a brief donation segment reminding listeners that the show is ad-free and supported by listener contributions. #AIEthics #Bias #CriminalJustice #RiskAssessment #PublicSafetyAssessment #NewJersey #BailReform #AlgorithmicBias #PredictiveModeling #ProprietaryAlgorithms #AuditingAI #Fairness #Technology #Policy #FexingoBusiness #BusinessPodcast #DataScience #Ethics Keep every episode free: buymeacoffee.com/fexingo

Episode 11 of AI Ethics with Fexingo examines the unregulated world of AI-powered mental health chatbots. Lucas and Luna dig into the case of a popular app that gave crisis users generic advice instead of emergency resources, and explore how the FDA's reluctance to classify these tools as medical devices leaves vulnerable users at risk. They discuss the difference between a therapeutic chatbot and a real therapist, the evidence that users with serious conditions may actually worsen after relying on AI companions, and what the FTC's recent enforcement action means for the industry. If you have ever used or considered using an AI for emotional support, this episode will change how you think about those conversations. #AIChatbots #MentalHealth #TherapeuticAI #Regulation #FDAClearance #FTCAction #UserSafety #AlgorithmicRisk #Ethics #Technology #FexingoBusiness #BusinessPodcast #DigitalHealth #HealthTech #VirtualTherapy #CrisisSupport #InformedConsent #AIAccountability Keep every episode free: buymeacoffee.com/fexingo