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

Fexingo

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

  1. 13h ago

    How AI Translation Models Learn Bias from Bible Text

    Episode 129 explores a surprising source of gender bias in AI language models: parallel Bible translations. Lucas and Luna examine how training multilingual AI on translated scripture—where 'man' often defaults to 'human' and pronouns skew male—reinforces outdated gender roles. They cite a 2024 Stanford study that found 70 percent of occupation-to-pronoun associations in a popular translation model mirrored biblical gender distributions, with 'prophet' and 'priest' mapping to male pronouns 96 percent of the time. The episode also discusses how researchers are now using gender-neutral retranslations and synthetic parallel data to debias these models. And of course, they share how this ad-free podcast is supported by listeners at buy me a coffee dot com slash fexingo. #AIEthics #BiasInAI #LanguageModels #GenderBias #BibleTranslation #NLP #MachineTranslation #Stanford #Paratext #AITrainingData #MultilingualAI #GenderNeutral #EthicalAI #FexingoBusiness #BusinessPodcast #Technology #AIResearch #DataBias Keep every episode free: buymeacoffee.com/fexingo

  2. 1d ago

    How AI Art Generators Learn Bias from Training Images

    In this episode of AI Ethics with Fexingo, Lucas and Luna explore how AI art generators like DALL-E and Midjourney inherit bias from their training datasets, focusing on the LAION-5B dataset and a 2023 study that found 52% of generated images of 'CEO' depicted white men, while only 4% showed women of any race. They discuss the feedback loop between biased training data and user prompts, the challenge of fixing bias after thousands of images have been scraped from the internet, and whether companies like OpenAI have a responsibility to rebalance their datasets. The hosts also touch on the tension between creative freedom and fairness, and why simply adding 'diverse' to a prompt isn't a real fix. The episode ends with a call for listeners to support the ad-free show through buy me a coffee dot com slash fexingo. #AIEthics #GenerativeAI #ArtBias #DALLE #Midjourney #LAION5B #TrainingData #BiasInAI #Representation #TechEthics #OpenAI #MachineLearning #AISafety #FexingoBusiness #BusinessPodcast #Technology #Ethics #ArtificialIntelligence Keep every episode free: buymeacoffee.com/fexingo

  3. 1d ago

    How AI Models Learn Bias From Teacher Grading Data

    In this episode of AI Ethics with Fexingo, Lucas and Luna explore how AI models inherit bias from teacher grading data. They examine a specific 2025 study from Stanford's Center for Education Policy Analysis that found an AI grading system trained on historical teacher-assigned grades systematically penalized students from under-resourced schools. The system inferred that students from certain zip codes were 'lower performers' because their previous grades reflected inconsistent teaching quality, not actual ability. The hosts discuss how the model amplified subjective human bias baked into grading patterns, and what school districts are now doing to audit training data for fairness. They also touch on the broader implications for any AI trained on human judgment data — from employee evaluations to insurance claims. The episode includes a brief, organic donor segment near the end. #AIEthics #BiasInAI #EducationAI #TeacherGrading #StanfordStudy #AlgorithmicBias #DataAuditing #Fairness #ResponsibleAI #MachineLearning #GradingData #HumanJudgment #EducationPolicy #Technology #FexingoBusiness #BusinessPodcast #AIAccountability #EthicalTech Keep every episode free: buymeacoffee.com/fexingo

  4. 2d ago

    How AI Models Learn Bias from Employee Performance Reviews

    In this episode of AI Ethics with Fexingo, Lucas and Luna dive into a surprising source of algorithmic bias: employee performance reviews. They explore how language patterns in reviews — from gender-coded adjectives to recency bias — can teach AI models to replicate and amplify human prejudices. The hosts walk through a real-world example from a Fortune 500 tech company, where a performance-review-trained AI systematically rated women lower on leadership potential. They also discuss the feedback loop problem: when models are trained on biased reviews and then used to generate new reviews, the bias compounds. Lucas brings in data from a 2025 study by the AI Now Institute showing that performance-review-trained models are 30% more likely to associate 'assertive' with male employees and 'collaborative' with female employees, even after controlling for role and tenure. The episode unpacks what companies can do — from rethinking review prompts to auditing models for language bias — and why this matters as more firms automate talent decisions. #AIEthics #Bias #PerformanceReviews #HRTech #AlgorithmicBias #WorkplaceAI #GenderBias #NaturalLanguageProcessing #AICulture #TalentManagement #AIAuditing #Fortune500 #Tech #FexingoBusiness #BusinessPodcast #Podcast #AI #Ethics Keep every episode free: buymeacoffee.com/fexingo

  5. 2d ago

    How AI Models Learn Bias from Insurance Claims Data

    Episode 125 of AI Ethics with Fexingo takes a hard look at how AI systems in the insurance industry can inherit and amplify bias from historical claims data. Lucas and Luna unpack a specific case from a major auto insurer that used decades of claims to build a risk-scoring model. The model penalized drivers from lower-income ZIP codes because past claims were more likely to be fraudulent in areas with higher poverty rates, creating a self-reinforcing cycle. They discuss the regulatory response from state insurance commissioners, the technical challenge of de-biasing training data that reflects systemic inequality, and whether 'fairness-aware' algorithms can truly solve the problem. The episode also explores why insurers are reluctant to change models that are profitable, and what proposed federal AI auditing standards could mean for the industry. A grounded, number-driven conversation about ethics in a trillion-dollar sector. #AIEthics #InsuranceBias #ClaimsData #AlgorithmicFairness #RiskScoring #AutoInsurance #Regulation #DataBias #MachineLearning #FairLending #InsuranceFraud #ZIPCodeDiscrimination #AuditTrail #StateInsuranceCommissioners #Technology #FexingoBusiness #BusinessPodcast #ResponsibleAI Keep every episode free: buymeacoffee.com/fexingo

  6. 3d ago

    How AI Learning Models Are Trapped by Their Own Data

    In this episode of AI Ethics with Fexingo, Lucas and Luna explore a surprising source of bias: when AI models are trained on data generated by older AI systems. They dive into a 2025 study from researchers at Oxford and MIT showing that language models fed synthetic text from earlier models develop 'model collapse'—becoming less diverse and more error-prone over generations. Lucas explains why this matters for everything from customer service chatbots to medical diagnostic tools, and Luna pushes him on whether this is a fixable technical problem or a fundamental limit. They also discuss the proposed 'data provenance' tagging system some companies are experimenting with to label AI-generated content in training sets. This episode ends with the hosts reflecting on how the solution might ironically require more human oversight, not less. #AIEthics #ModelCollapse #BiasInAI #SyntheticData #DataProvenance #OxfordStudy #MITStudy #LanguageModels #MachineLearning #AISafety #ResponsibleAI #Tech #ArtificialIntelligence #DataQuality #GenerativeAI #FexingoBusiness #BusinessPodcast #Technology Keep every episode free: buymeacoffee.com/fexingo

  7. 3d ago

    How AI Resume Screeners Learn Bias from Job Postings

    In this episode, Lucas and Luna explore how AI hiring tools don't just inherit bias from the resumes they screen—they learn it from the job postings themselves. They walk through a 2025 study from the University of Chicago that analyzed 10,000 job descriptions on LinkedIn and found that postings using masculine-coded language (like 'aggressive,' 'dominant,' or 'ninja') caused AI models to rank male candidates higher even when qualifications were identical. The hosts discuss how this 'posting bias' creates a feedback loop: biased postings attract biased applicants, which then reinforce the model's skewed scoring. They also talk about how companies like Unilever and Hilton have restructured their job ads to reduce gender-coded language, with Hilton seeing a 12% increase in female applicants for technical roles after a rewrite. Lucas brings in a startling stat: 88% of Fortune 500 companies now use some form of AI screening, yet fewer than 15% audit their job descriptions for language bias. Luna asks whether the solution is better training data or a fundamental redesign of how we write job ads. The episode closes on a forward-looking note: as more states pass algorithmic accountability bills, the real question may not be whether AI can be fair, but whether companies are willing to change the human inputs they feed it. #AIEthics #HiringBias #JobPostings #GenderBias #ResumeScreening #AlgorithmicBias #HRTech #LinkedInStudy #UniversityOfChicago #Unilever #Hilton #Fortune500 #AIandEmployment #FairHiring #Technology #FexingoBusiness #BusinessPodcast #AIAccountability Keep every episode free: buymeacoffee.com/fexingo

  8. 4d ago

    How AI Models Inherit Bias from Court Sentencing Data

    In this episode of AI Ethics with Fexingo, Lucas and Luna explore how AI models used in court sentencing can inherit racial and socioeconomic biases from historical sentencing data. They focus on the COMPAS algorithm, a risk assessment tool used in U.S. courts, which was found to falsely flag Black defendants as high-risk at nearly twice the rate of white defendants. The hosts discuss a 2024 study from the University of Chicago that showed how retraining the model on clean data reduced this disparity by 60 percent. They also examine the broader implications for AI in the justice system, including recent legislative efforts in California and the EU AI Act. The episode includes a brief, listener-supported funding segment that helps keep the show ad-free. #AIEthics #BiasInAI #CriminalJusticeAI #COMPAS #AlgorithmicFairness #RacialBias #SentencingAI #PredictivePolicing #EUAIAct #CaliforniaAI #UniversityOfChicago #RiskAssessment #MachineLearning #FairnessInAI #Technology #AIandLaw #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

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About

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

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