『AI Ethics with Fexingo: Bias, Safety, and Responsible Artificial Intelligence』のカバーアート

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

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

著者： Fexingo
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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 Keep every episode free: buymeacoffee.com/fexingo© 2026 Fexingo. All rights reserved. 経済学
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  • How AI Models Hoard Energy

    How AI Models Hoard Energy
    2026/09/11
    We look at the hidden cost of artificial intelligence: energy consumption. Lucas and Luna examine how training large language models impacts data centers and power grids, using specific metrics on water usage and electricity demand to show why this matters for businesses and investors right now. #FexingoBusiness #BusinessPodcast #AIEthics #TechSustainability #DataCenterEnergy #GreenComputing #AIInfrastructure #CarbonFootprint #PowerGrids #WaterUsage #CorporateESG #ClimateTech #EnergyEfficiency #CloudComputing #TechPolicy #RenewableEnergy #AIModelTraining #SiliconValley Keep every episode free: buymeacoffee.com/fexingo
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    8 分
  • How AI Censorship Creates Dangerous Blind Spots

    How AI Censorship Creates Dangerous Blind Spots
    2026/09/09
    We examine the unintended consequences of aggressive content filtering in enterprise AI models, using a specific case where over-zealous safety guardrails caused a major logistics firm to miss critical supply chain risks. This episode explores the trade-off between safety and utility, discussing how companies are now implementing 'safety tuning' reviews that prioritize contextual nuance over blanket bans, and why the cost of false positives is becoming a boardroom issue as of September 2026. #AIEthics #ResponsibleAI #ContentFiltering #FalsePositives #EnterpriseAI #SafetyTuning #SupplyChainRisk #LucasAndLuna #FexingoBusiness #BusinessPodcast #TechPolicy #AIAlignment #CorporateStrategy #OperationalEfficiency #ModelBias #RiskManagement #DigitalTransformation #September2026 Keep every episode free: buymeacoffee.com/fexingo
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    11 分
  • How AI Hiring Tools Discriminate By Name

    How AI Hiring Tools Discriminate By Name
    2026/09/08
    We examine the mechanics of algorithmic hiring bias, specifically how resume-screening models penalize names associated with minority groups and non-standard spellings. Using recent academic findings on phonetic encoding errors, we explore why an AI trained on historical hiring data often rejects qualified candidates before a human ever sees their application. We also discuss practical steps companies can take to audit these systems for fairness before deployment. #AIHiringBias #ResumeScreening #AlgorithmicFairness #TechEthics #HRTechnology #MachineLearningBias #RecruitmentTech #DiversityAndInclusion #FexingoBusiness #BusinessPodcast #EmploymentLaw #AIAudit #PhoneticEncoding #WorkplaceEquity #TechPolicy #CorporateGovernance #DataPrivacy #FutureOfWork Keep every episode free: buymeacoffee.com/fexingo
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    9 分
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