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 t
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What is AI Ethics with Fexingo: Bias, Safety, and Responsible Artificial Intelligence?
AI Ethics with Fexingo: Bias, Safety, and Responsible Artificial Intelligence is a business podcast hosted by Fexingo, with 154 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.
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Fexingo hosts AI Ethics with Fexingo: Bias, Safety, and Responsible Artificial Intelligence, a business show with 154 episodes published.
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Episode #158
How AI Facial Recognition Fails on Darker Skin Tones
Aug 18, 202611 minS4
In this episode, Lucas and Luna dig into a specific, persistent flaw in artificial intelligence: facial recognition systems that misidentify people with darker skin. They trace the problem back to a landmark 2018 study by Joy Buolamwini and Timnit Gebru, which found error rates of up to 34.7 percent for darker-skinned women, compared to under one percent for lighter-skinned men. The hosts explain why these errors happen — from skewed training datasets to technical limitations in camera sensors — and why the problem hasn't been fully solved by 2026, despite industry promises. They also discuss the real-world consequences, from false arrests to biased surveillance, and what researchers and regulators are doing about it. This episode offers a clear, accessible breakdown of a complex issue, grounded in specific numbers and examples that you can bring up in your next conversation about AI ethics. #AIEthics #FacialRecognition #AlgorithmicBias #JoyBuolamwini #TimnitGebru #GenderShades #Technology #ArtificialIntelligence #ResponsibleAI #Surveillance #DataBias #MachineLearning #CivilRights #ACLU #FexingoBusiness #BusinessPodcast #TechPolicy #EthicsInTech Keep every episode free: buymeacoffee.com/fexingo
When AI Hiring Tools Learn Bias from Employee Reviews
Aug 17, 20269 minS4
In this episode of AI Ethics with Fexingo, hosts Lucas and Luna explore a fresh angle on algorithmic bias: how AI systems trained on employee performance reviews can learn and perpetuate workplace biases. They dig into the case of a major retailer whose internal AI for promotion recommendations penalized women and minority employees because the training data—years of subjective manager evaluations—carried subtle biases. The hosts break down how language patterns like 'aggressive' versus 'assertive' get encoded, why the algorithm amplified existing skews, and what companies can do to mitigate these effects. With references to real-world studies and practical takeaways, this conversation offers a clear-eyed look at a blind spot in AI ethics. Tune in for a specific, data-driven discussion that connects the dots between human bias and machine learning. #AIEthics #AlgorithmicBias #HiringBias #EmployeeReviews #WorkplaceBias #PerformanceReviews #AIinHR #MachineLearning #BiasInAI #GenderBias #RacialBias #TechEthics #FexingoBusiness #BusinessPodcast #Technology #AI #DataBias #ResponsibleAI Keep every episode free: buymeacoffee.com/fexingo
In this episode of AI Ethics with Fexingo, Lucas and Luna explore the growing use of AI scribes in healthcare and the subtle biases that creep into their documentation. They anchor the discussion on a 2025 Stanford study that found AI-generated clinical notes frequently omit patients' expressions of uncertainty and cultural references, instead inserting confident, biomedical language that distorts the patient's own account. The hosts examine how these silent edits could affect diagnosis, treatment, and patient trust, and what it means for the physician-patient relationship. They also touch on the lack of transparency in these systems and the need for stronger oversight. With practical takeaways for both patients and clinicians, this episode cuts through the hype to ask: when AI takes the notes, who truly owns the story? A thought-provoking listen for anyone in healthcare, tech, or concerned about the future of medicine. #AIEthics #MedicalAI #HealthTech #ClinicalDocumentation #AIinHealthcare #PatientSafety #Bias #Stanford #Technology #AI #Health #Medicine #Ethics #Podcast #FexingoBusiness #BusinessPodcast #FutureofMedicine #DigitalHealth Keep every episode free: buymeacoffee.com/fexingo
In this episode of AI Ethics with Fexingo, Lucas and Luna dive deep into a growing concern: AI-powered triage systems used in emergency rooms and telehealth platforms. These systems, designed to prioritize patients based on symptoms, can inadvertently learn biases from historical healthcare data. The conversation anchors on a 2025 study from a major US hospital network, where the algorithm consistently under-triaged Black patients with cardiac complaints compared to white patients with identical symptoms. The hosts unpack the root cause: the algorithm was trained on historical data that reflected unequal access to care, not inherent biological differences. They discuss the ethical dilemma of using historical data to train systems that shape future care, the lack of transparency in commercial triage algorithms, and the urgent need for regulatory oversight. The episode also touches on the 'garbage in, garbage out' problem and proposes practical solutions like continuous auditing and diverse training datasets. By the end, listeners grasp why fixing triage AI isn't just a technical problem but a moral imperative for healthcare equity. #AIEthics #HealthcareAI #AlgorithmicBias #TriageSystems #EmergencyMedicine #HealthTech #MedicalAI #BiasInMedicine #ResponsibleAI #TechEthics #HealthEquity #ArtificialIntelligence #Podcast #FexingoBusiness #BusinessPodcast #Technology #AI #EthicsInTech Keep every episode free: buymeacoffee.com/fexingo
How AI Art Generators Learn Bias from Their Training Images
Aug 14, 20267 minS4
On this episode of AI Ethics with Fexingo, Lucas and Luna explore how AI image generators like DALL-E and Stable Diffusion inherit bias from their training data. They discuss a 2024 Stanford study that audited over 5,000 generated images and found that models exaggerate gender and racial stereotypes by up to 30 percent compared to real-world demographics. The hosts debate whether 'de-biasing' techniques actually help or merely replace one stereotype with another, and they examine the implications for advertising, journalism, and social media. Lucas and Luna also touch on the challenge of 'cultural homogenization' and why diverse training sets aren't a silver bullet. The conversation is anchored in specific examples and data, making a complex topic both accessible and thought-provoking. Tune in for a nuanced look at how AI sees the world—and how that vision can distort our own. #GenerativeAI #AIEthics #BiasInAI #ImageGeneration #StableDiffusion #DALLE #AlgorithmicBias #ResponsibleAI #MachineLearning #TechEthics #AIArt #DiversityInTech #FairnessInAI #DataEthics #Technology #FexingoBusiness #BusinessPodcast #AITechnology Keep every episode free: buymeacoffee.com/fexingo
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