About AI Ethics with Fexingo: Bias, Safety, and Responsible Artificial Intelligence
Every week, Lucas and Luna sit down at the library table to examine the real-world consequences of artificial intelligence — not the sci-fi futures, but the decisions being coded into systems today. This show is about bias in hiring algorithms that screen out qualified candidates before a human sees a résumé; safety failures in autonomous vehicles that misclassify pedestrians; and the regulatory scramble to define fairness when no one agrees on what 'fair' means. Lucas brings the research: the 2023 AI Incident Database report, the EU AI Act's tiered risk framework, the ProPublica investigation into recidivism algorithms. Luna pushes back with the practical questions: who audits these systems, what happens when an AI's training data contains centuries of systemic prejudice, and whether a code of ethics matters if it can't be enforced. Together, they avoid the hype and the panic, focusing instead on the specific trade-offs engineers and policymakers face. This is for listeners who want to understand why a self-driving car struck a pedestrian in Tempe, Arizona, or why Amazon scrapped its AI recruiting tool, or how facial recognition errors disproportionately affect certain communities — and who are looking for the nuance behind the headlines. You'll leave each episode with a clearer sense of what responsible AI actually requires, and why the hardest problems aren't technical but human.
#AIEthics #BiasInAI #AIandSociety #ResponsibleAI #AlgorithmicBias #AISafety #AIPolicy #EUSAI #AIGovernance #Fairness #Discrimination #MachineLearning #AIPodcast #Technology #BusinessPodcast #FexingoBusiness #DailyBusinessPodcast #TechEthics
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We explore the invisible human workforce behind generative AI, focusing on the specific case of Scale AI and the $1.50 per hour wage floor controversy. Lucas breaks down how data labeling creates a new class of digital workers in Kenya and the Philippines, while Luna examines the ethical gap between model accuracy claims and worker welfare. We discuss the real cost of clean data, the tension between automation promises and manual labor demands, and what this means for the future of tech employment.
#AI Ethics #HumanInTheLoop #DataLabeling #ScaleAI #DigitalLabor #TechSupplyChain #ArtificialIntelligence #BusinessPodcast #FexingoBusiness #EthicalAI #GlobalSouth #Outsourcing #MachineLearning #WorkerWelfare #Transparency #TechIndustry #CorporateResponsibility #FutureOfWork
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13 Oct 2026
How AI Audits Create False Confidence
We are talking about the illusion of safety in modern enterprise AI. Most companies rely on static compliance checklists to prove their models are safe, but these audits measure only what was asked, not how the model behaves under pressure. We look at a specific case where a major financial services firm passed every pre-deployment test yet failed catastrophically when real-world users found loopholes in its guidance system. The problem is that audit frameworks are designed for stability, but AI is probabilistic and adaptive. If you are building or buying AI today, you need to understand why passing an audit does not mean your system is secure. This episode breaks down the difference between procedural compliance and actual operational resilience, using concrete examples from recent regulatory submissions to show why the current standard is broken.
#AI Ethics #Responsible AI #Tech Regulation #Enterprise Safety #Algorithmic Bias #Model Risk Management #Financial Services Tech #Compliance Failure #FexingoBusiness #BusinessPodcast #LucasAndLuna #AI Governance #Risk Management #Corporate Strategy #Tech Industry #Audit Frameworks #Operational Resilience #AI Safety
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12 Oct 2026
How AI Safety Labels Mislead Consumers
We look at the new wave of 'safe' AI certifications and why they might be giving users a false sense of security. With over eighty percent of major tech firms now displaying trust badges, we examine the gap between marketing promises and actual model behavior. The conversation centers on the specific case of a recent healthcare diagnostic tool that passed all ethical audits but still produced biased recommendations for minority patients. We break down how these labels are created, who funds them, and what that means for your daily digital interactions in October twenty twenty six.
#FexingoBusiness #BusinessPodcast #AIEthics #TechPolicy #ConsumerTrust #AIRegulation #DigitalSafety #ResponsibleAI #TechTransparency #AlgorithmicBias #CorporateAccountability #HealthcareTech #DataPrivacy #Innovation #MarketTrends #TechIndustry #FutureOfWork #LunaAndLucas
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