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 Specific numbers: 87% of contested notices were withdrawn when a human actually reviewed the evidence.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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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