Podcasts about algorithmic bias

Commencement season is here and, as many students are closing one chapter and stepping into the next, it's a nice moment to ask: what did learning really look like for these students, and how might it change for the next generation? With those questions in mind, we're re-releasing a conversation with Computer Science Professor Chris Piech on the future of computer-aided education. Chris studies how computers can and will help students learn. His message isn't that teachers are obsolete — far from it. He shares that the future of education certainly involves AI, but that we must never lose the human element. Whether you're a new grad, a lifelong learner, or an educator wondering what's coming next, this one is well worth another listen. Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu. Episode Reference Links: Stanford Profile: Chris Piech Connect With Us: Episode Transcripts >>> The Future of Everything Website Connect with Russ >>> Threads / Bluesky / Mastodon Connect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / Facebook Chapters: (00:00:00) Introduction Russ Altman introduces guest Chris Piech, a professor of computer science from Stanford University. (00:01:44) Teaching People to Code What programming is and why learning to code can be challenging. (00:02:54) Motivation in Learning Why joy and motivation are central challenges in education. (00:03:54) Recent Learners as Teachers How near-peer teachers helped scale a Stanford coding course to thousands  (00:07:10) AI and Computer Programming How generative AI is changing coding for students and professionals. (00:09:24) The Joy of Programming How AI tools can expand what learners are able to create. (00:12:41) Experiments with Teaching What experiments reveal about one-on-one teaching & AI support. (00:14:39) Rethinking Assessment The value Piech sees in computational assessment. (00:16:38) Fairness in Grading Why AI grading raises questions about bias, context, and real-world use. (00:20:59) Feedback & Assessment How computers can evaluate creative and less structured assignments. (00:22:21) Dream Grader A system that interacts with student projects to understand and assess them. (00:25:30) Beyond the Classroom How assessment tools can also support medical testing. (00:26:52) Measuring Vision More Precisely Using adaptive testing to improve eye exams and track subtle changes. (00:27:57) Generative Grading What is generative grading and how can it actually function and be useful? (00:29:44) Teachers and AI Together Why the future of grading may depend on combining teacher insight with AI support. (00:31:33) Conclusion   Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Gugs Mhlungu speaks with Dr Mark Nasila, Chief Data and Analytics Officer at First National Bank Risk, about the credibility of Google’s AI Overviews and how users can critically assess and verify information rather than relying on it at face value. The discussion also explores how AI labelling can help people identify generated content and make more informed judgments about what they read online. Gugs Mhlungu gets you ready for the weekend each Saturday and Sunday morning on 702. She is your weekend wake-up companion, with all you need to know for your weekend. The topics Gugs covers range from lifestyle, family, health, and fitness to books, motoring, cooking, culture, and what is happening on the weekend in 702land. Thank you for listening to a podcast from 702 Weekend Breakfast with Gugs Mhlungu. Listen live on Primedia+ on Saturdays and Sundays from 06:00 and 10:00 (SA Time) to Weekend Breakfast with Gugs Mhlungu broadcast on 702 https://buff.ly/gk3y0Kj For more from the show go to https://buff.ly/u3Sf7Zy or find all the catch-up podcasts here https://buff.ly/BIXS7AL Subscribe to the 702 daily and weekly newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio702 See omnystudio.com/listener for privacy information.

AI is being deployed across courts, police departments, and corrections systems. Without the right guardrails, it could amplify existing biases. But, with care and attention, there are opportunities to improve the experience of people within these same systems. Host Stephen Goldsmith speaks with Dr. Andrea Headley from Georgetown University's Evidence for Justice Lab about what governments need to know about AI in criminal justice, how to identify and reduce bias, why transparency matters for public trust, and the devastating consequences when humans aren't in the loop. Guest: Dr. Andrea Headley – Associate Professor, Georgetown University McCourt School of Public Policy; Director, Evidence for Justice Lab References: The Justice and Artificial Intelligence Tracker Listener Survey: bit.ly/datasmartpod Music credit: Summer-Man by Ketsa About Data-Smart City Solutions Data-Smart City Solutions, housed at the Bloomberg Center for Cities at Harvard University, is working to catalyze the adoption of data projects on the local government level by serving as a central resource for cities interested in this emerging field. We highlight best practices, top innovators, and promising case studies while also connecting leading industry, academic, and government officials. Our research focus is the intersection of government and data, ranging from open data and predictive analytics to civic engagement technology. We seek to promote the combination of integrated, cross-agency data with community data to better discover and preemptively address civic problems. To learn more visit us online and follow us on LinkedIn.

We watched the chilling new sci-fi thriller, Mercy (2026) on Amazon Prime MGM. Honestly? It is the most realistic and genuinely scary depiction we have ever seen of how AI might completely dismantle or "fix" our communities. We discuss how Mercy moves past generic tech-horror and takes a nuanced approach. It shows the obvious positives: a 90% drop in capital crime, unparalleled efficiency, and an objective justice system. But it simultaneously explores the terrifying grey area: the end of privacy, automated executions based on probability, and how easily a perfect algorithm can be weaponized by imperfect humans. We're breaking down why the movie argues that humanity's "judgement" requires intuition that can never be programmed, and why this is an essential watch for 2026.

Comment on the Show by Sending Mark a Text Message.This episode is about the show itself, the Employee Survival Guide and why Mark Carey has made it so subversive to mainstream corporate america.  Tune into discover why Mark created this podcast only for employees. What happens when the illusion of corporate benevolence meets the stark reality of at-will employment? In this eye-opening episode of the Employee Survival Guide®, Mark Carey and his co-host expose the hidden truths of the workplace that every employee must confront. With insights drawn from his extensive experience as an employment attorney, Carey reveals how companies often manipulate legal frameworks to serve their interests, leaving employees vulnerable to termination for almost any reason. This episode is not just a discussion; it's a crucial resource for understanding your rights and navigating the complexities of employment law. As the conversation unfolds, listeners will learn about the psychological myths surrounding corporate culture, including the dangerous belief that employers act in their employees' best interests. The Employee Survival Guide® empowers you to arm yourself with the knowledge necessary to survive in a system that often views you as disposable. Discover the traps that employers set, especially through tools like Performance Improvement Plans (PIPs) and the growing issue of algorithmic bias in performance evaluations. This episode is a wake-up call for anyone who has ever felt the weight of a hostile work environment or faced discrimination in the workplace. From severance negotiations to understanding employment contracts, this episode covers vital topics that can make or break your career. Mark Carey emphasizes the importance of documenting your experiences and protecting yourself legally, especially in the face of workplace retaliation or discrimination. Whether you're dealing with age discrimination, sexual harassment, or navigating remote work challenges, this episode offers invaluable insights and strategies to help you advocate for your rights and well-being. As we critique the mainstream business media's portrayal of workplace dynamics, we challenge you to rethink your approach to employment. This isn't just about surviving; it's about thriving and taking control of your career. Tune in to the Employee Survival Guide® for insider tips on navigating employment law issues, and learn how to negotiate severance packages that truly reflect your worth. Join us on this journey of employee empowerment, and transform your understanding of workplace culture and employee rights. Don't let yourself become just another statistic in the world of employment disputes. Equip yourself with the tools and knowledge you need to navigate the complexities of your job with confidence. Listen now and take the first step towards a more informed and empowered career journ If you enjoyed this episode of the Employee Survival Guide please like us on Facebook, X and LinkedIn.  We would really appreciate if you could leave a review of this podcast on your favorite podcast player such as Apple Podcasts and Spotify. Leaving a review will help other employees find the Employee Survival Guide.  For more information, please contact our employment attorneys at Carey & Associates, P.C. at 203-255-4150, www.capclaw.com.Disclaimer:  For educational use only, not intended to be legal advice. 

Comment on the Show by Sending Mark a Text Message.Are you tired of the AI hiring bias? Join Mark Carey in this eye-opening episode of the Employee Survival Guide® as he unpacks the complexities of the modern hiring landscape, dominated by AI and automated employment decision tools (AEDTs). The emotional rollercoaster of the AI hiring bias and job applications—from the thrill of discovering the ideal job posting to the crushing disappointment of rejection—is all too familiar. But what if we told you that many of those rejections come not from a human, but from an algorithm? As we delve into the alarming reality of how the AI hiring bias and how algorithms filter applications without any human oversight, it's crucial for job seekers to understand the mechanics behind these systems. Carey introduces the concept of the 'black box' in hiring, where opaque algorithms can lead to discriminatory outcomes, AI hiring bias, leaving applicants in the dark about why they were overlooked. With the rise of AI hiring bias and the potential for discrimination based on race, age, gender, and more, knowledge is power in navigating this treacherous terrain. But fear not! Carey shares invaluable strategies for job seekers to enhance their resumes and beat the bots. Learn how to effectively mirror job descriptions and utilize simple formatting to increase your chances of getting noticed. This episode is packed with practical tips that empower you to take control of your job search process, transforming you from a passive candidate into an active participant in your career development. We also explore the legal implications surrounding employment discrimination, including the importance of bias audits and understanding your rights as an applicant. Whether you're facing issues like hostile work environments, retaliation, or discrimination based on disability or pregnancy, this episode equips you with the knowledge to advocate for yourself. The Employee Survival Guide® is not just about surviving the job market; it's about thriving within it. Ultimately, Mark Carey encourages listeners to embrace their power in a system that often feels rigged against them. By understanding the intricacies of AI in hiring and knowing your employee rights, you can navigate the employment landscape with confidence. Tune in for a compelling discussion that not only highlights the challenges of the job search but also offers actionable insights to empower you on your journey. Don't let algorithms dictate your future—take charge of your career today!  If you enjoyed this episode of the Employee Survival Guide please like us on Facebook, Twitter and LinkedIn. We would really appreciate if you could leave a review of this podcast on your favorite podcast player such as Apple Podcasts and Spotify. Leaving a review will inform other listeners you found the content on this podcast is important in the area of employment law in the United States. For more information, please contact our employment attorneys at Carey & Associates, P.C. at 203-255-4150, www.capclaw.com.Disclaimer: For educational use only, not intended to be legal advice.

Send us a textThis week, we're sharing a fan-favorite replay, an episode that ranks in the top ten of our all-time most listened-to episodes. In this week's replay episode we unlock the secrets of building adaptive, personalized robots with Dr. Randi Williams, a leading figure in AI and robotics, as she shares her journey from a math-obsessed child inspired by Jimmy Neutron to a pioneering expert aiming to make technology fairer and more inclusive. Dr. Williams takes us behind the scenes of her work at the Algorithmic Justice League (AJL), discussing the triumphs and challenges of creating robots that can truly engage with humans. Through the lens of projects like PopBots, you'll discover how even preschoolers can grasp foundational AI concepts and start innovating from an early age. Hear the inspiring story of a young learner who programmed a multilingual robot, and explore the engaging tools and platforms like MIT's Playground that make learning AI fun and accessible. Finally, we tackle the crucial issue of algorithmic bias and the importance of diverse data sets in AI training. This episode underscores how creativity and a passion for learning can drive meaningful advancements in AI and robotics.  Resources for parents and kids:Preschool-Oriented Programming (POP) Platform PopBotsPlayground Raise MITDay of AITuring Test GameUnmasking AICoded BiasPersonal Robots GroupScratchNational Coding Week Support the showHey parents and teachers, if you want to stay on top of the AI news shaping your kids' world, subscribe to our weekly AI for Kids Substack: https://aiforkidsweekly.substack.com/ Help us become the #1 podcast for AI for Kids and best AI podcast for kids, parents, teachers, and families. Buy our debut book “AI… Meets… AI”Social Media & Contact: Website: www.aidigitales.com Email: contact@aidigitales.com Follow Us: Instagram, YouTube Books on Amazon or Free AI Worksheets Listen, rate, and subscribe! Apple Podcasts Amazon Music Spotify YouTube Other Like o...

What if our smartest health tools still miss the people who need them most? We sit down with AI and digital health scientist Mireille Gomes to examine how innovation can serve dignity, not just efficiency—and what it takes to build technology that works from Geneva to rural clinics without electricity.The journey of Mireille Gomes spans continents and roles, from vaccine strategy at Gavi to AI diagnostics at Merck. Together, we unpack the real barriers to deployment—uneven infrastructure, overworked staff, and data voids that erase entire communities from the record. We look at consent‑first design, why open data must be truly anonymous, and how representation in civil registration and vital statistics underpins every “fair” algorithm. You'll hear pragmatic ideas for triage tools that flag urgency in seconds, health education in local languages, and micro‑local models that adapt to context while sharing standards globally.We also push on the hard questions: Who decides which data matters? Can algorithms be biased toward justice if the world is not? Where is the line between breakthrough and overreach when crises demand speed? Mirielle argues for building abuse cases into development, testing for misuse before launch, and preserving community storytelling—especially Indigenous knowledge—alongside dashboards. The goal is health equity by design, so no one's care depends on their birthplace or bandwidth.If you care about AI in healthcare, data justice, and solutions that actually work on the ground, this conversation offers a clear roadmap and candid guardrails. If it resonates, subscribe, leave a review, and share it with someone shaping the future of digital health.Send us a textCheck out "Protection for the Inventive Mind" – available now on Amazon in print and Kindle formats. The views and opinions expressed (by the host and guest(s)) in this podcast are strictly their own and do not necessarily reflect the official policy or position of the entities with which they may be affiliated. This podcast should in no way be construed as promoting or criticizing any particular government policy, institutional position, private interest or commercial entity. Any content provided is for informational and educational purposes only.

Health care executive Ronke Lawal discusses her article, "Digital mental health's $20 billion blind spot." Ronke explains how the booming digital mental health industry is systematically failing 40 percent of the U.S. population (racial and ethnic minorities), ignoring a $20 billion market opportunity. She argues that the "one-size-fits-all" model, based on Western-centric care, is a product failure that leads to algorithmic bias, misdiagnosis in diverse patients, and culturally incompetent artificial intelligence (AI) chatbots. This failure in digital mental health doesn't just alienate users; it creates real financial consequences for health systems, including higher emergency department use and readmission rates. Ronke makes the ironclad business case for embedding "cultural intelligence" into technology, arguing it's the only way to fix systemic bias and build effective digital mental health tools. Our presenting sponsor is Microsoft Dragon Copilot. Want to streamline your clinical documentation and take advantage of customizations that put you in control? What about the ability to surface information right at the point of care or automate tasks with just a click? Now, you can. Microsoft Dragon Copilot, your AI assistant for clinical workflow, is transforming how clinicians work. Offering an extensible AI workspace and a single, integrated platform, Dragon Copilot can help you unlock new levels of efficiency. Plus, it's backed by a proven track record and decades of clinical expertise, and it's part of Microsoft Cloud for Healthcare, built on a foundation of trust. Ease your administrative burdens and stay focused on what matters most with Dragon Copilot, your AI assistant for clinical workflow. VISIT SPONSOR → https://aka.ms/kevinmd SUBSCRIBE TO THE PODCAST → https://www.kevinmd.com/podcast RECOMMENDED BY KEVINMD → https://www.kevinmd.com/recommended

Algorithmic Bias and Discrimination: AI systems, trained on existing datasets that may reflect societal prejudices, can inadvertently perpetuate and even amplify biases based on race, gender, socioeconomic status, or other characteristics.Become a supporter of this podcast: https://www.spreaker.com/podcast/racism-white-privilege-in-america--4473713/support.

What if the key to entrepreneurial success isn't following a straight path, but embracing the unexpected turns along the way? In this episode of The Angel Next Door Podcast, host Marcia Dawood is joined by Madeline Reeves, a dynamic entrepreneur whose journey from aspiring pediatric oncologist to fintech leader and founder of Fearless Foundry challenges our assumptions about what it means to build a career—and a business—on your own terms.Madeline draws from her rich background in business development, creative consulting, and tech to share her experiences breaking into male-dominated industries and tackling challenges like workplace bias. Her story serves as a reminder that resilience, curiosity, and a willingness to rewrite the rules can shape not just businesses, but also lives.Throughout the episode, you'll hear practical insights on building authentic brands, leveraging strategic marketing, and thoughtfully integrating AI into creative processes. Madeline also dives into critical conversations around algorithmic bias and the importance of women shaping the future of technology. This is a must-listen for anyone looking for honest, actionable advice on modern entrepreneurship and the courage to do things differently. To get the latest from Madeline Reeves, you can follow her below!https://www.linkedin.com/in/madelinefearless/https://www.fearlessfoundry.com/ Sign up for Marcia's newsletter to receive tips and the latest on Angel Investing!Website: www.marciadawood.comDo Good While Doing WellLearn more about the documentary Show Her the Money: www.showherthemoneymovie.comAnd don't forget to follow us wherever you are!Apple Podcasts: https://pod.link/1586445642.appleSpotify: https://pod.link/1586445642.spotifyLinkedIn: https://www.linkedin.com/company/angel-next-door-podcast/Instagram: https://www.instagram.com/theangelnextdoorpodcast/Pinterest: https://www.pinterest.com/theangelnextdoorpodcast/TikTok: https://www.tiktok.com/@marciadawood

Mastering Life Choices with Dr. Ali Asadi | The PEARL Model for Better Decision-Making | Dr. Foojan Zeine In this transformative conversation, Dr. Foojan Zeine sits down with Dr. Ali Asadi, author of "Mastering Life Choices", to explore his groundbreaking PEARL Model — a powerful framework for evaluating decisions using both emotional and logical reasoning. Together, they uncover how to navigate difficult choices, manage risk, avoid emotional bias, and even how AI can assist (but not replace) your decision-making process. This episode is packed with practical tools, psychological insights, and real-life examples that will help you make more thoughtful, impactful decisions in all areas of life — relationships, career, health, and beyond.

We think of computers as neutral, but what if they're learning our worst habits? From hiring to home loans, AI is making huge decisions about our lives, and it often gets things wrong in ways that are deeply unfair. Our latest feature article dives into the problem of algorithmic bias, using real-world examples to show how flawed data creates a biased machine. But it's not all doom and gloom. We also explore the solutions—from "Explainable AI" to the crucial role of human oversight. If you care about fairness and technology, this is a must-read. #AI #Bias #TechEthics #SocialJustice #FutureTech https://englishpluspodcast.com/algorithmic-bias-unmasking-the-flaws-in-our-digital-mirrors/ To unlock full access to all our episodes, consider becoming a premium subscriber on Apple Podcasts or Patreon. And don't forget to visit englishpluspodcast.com for even more content, including articles, in-depth studies, and our brand-new audio series and courses now available in our Patreon Shop!

At a time when startups are primarily funded by private market investors, who you know has become a critical factor in gaining access to that venture capital. But how does the reliance on alumni and professional networks create barriers for startups from historically disadvantaged groups?Emmanuel Yimfor '20 is a finance professor at Columbia Business School and holds a Ph.D. from Rice University. His research focuses on entrepreneurial finance, diversity and private capital markets, with insights into gender and racial disparities in venture capital funding, board representation and how resources could be more equitably allocated.Emmanuel joins co-host Maya Pomroy '22 to discuss his career journey from working at a Cameroonian telecommunications company to teaching at some of the top U.S. business schools, as well as his research on the influence of alumni networks in venture capital funding, how AI tools can address biases in lending, and finally how he's teaming up with his son to bring AI tools to young innovators and entrepreneurs in Cameroon. Episode Guide:01:00 Exploring Entrepreneurial Finance03:36 The Role of Networks in VC Funding08:10 Emmanuel's Journey From Cameroon to the U.S.12:34 The Rice University Experience15:43 Research on Alumni Networks and Funding21:49 Algorithmic Bias in Lending33:17 Empowering Future Innovators in Cameroon38:42 Final Thoughts and Future OutlookOwl Have You Know is a production of Rice Business and is produced by University FM.Episode Quotes:Rethinking who gets funded in venture capital31:07: What does good networks mean exactly? If you look at venture capital partners, for example, right? They have worked at McKinsey before they became venture capital partners. So they have worked at certain companies, they have done certain jobs that then led them to become VCs. And so to the extent that we have a lack of representation in this pipeline of jobs that is leading to VC, then the founders that do not come from these same backgrounds do not have as equal access to the partners. And so what that suggests is something very basic, which is like, just rethink the set of deals that you are considering. That might expand the pool of deals that you consider, because, you know, there might be a smart person out there that is maybe not the same race as you, but that has an idea that you really, really want to fund. And that is something that I think, like, everybody would agree with. You know, we want to allocate capital to its most productive uses.From hard data to meaningful change29:13: So I have a belief in America, at least based on my life journey, which is: if you work hard for long enough, somebody is going to recognize you and you will be rewarded for it. And so I really believe that America takes in data, thinks about that data for a while to think about whether the research is credible enough, and then, using that data, they are a good Bayesian, so they get a new posterior. They act in a new way that is consistent with what the new before and the new data. And so I think about my role as a researcher as just like, you know, providing that data. Here is the data, and here is what is consistent with what we are doing right now. Now, you know, what you do with that information now is like, you know, update what you are doing in a way that is most consistent with efficient capital allocation—is my hope.Why Emmanuel finds empirical work so exciting 21:34: Empirical work is so exciting to me because then you are like, "I am a little bit of a police detective." So you take a little bit of this thing that feels hard to measure, and then you can create hypotheses to link it to the eventual outcomes, to the extent that that thing that is hard to measure is something that is leading to efficient capital allocation. Then, on average, you know, this feeling that you get about founders that are from the same alma mater should lead to good things as opposed to leading to bad things. And so, you know, that is exactly the right spirit of how to think about the work.Show Links: TranscriptGuest Profiles:Emmanuel Yimfor | Columbia Business SchoolEmmanuel Yimfor | LinkedInEmmanuel's Website 

Explore the hidden flaws in A/B testing on platforms like Google and Meta. In this episode, marketing professor Johannes Boegershausen reveals why ad delivery algorithms often undermine true randomization and what that means for marketers and researchers. Learn why platform-based test results shouldn't be over-extrapolated—and how to run more reliable experiments.

John Pasmore, thinks the answer is yes — but not if we keep doing things the old way. In this episode, the CEO and founder of Latimer AI lays out the company's strategy for inclusive AI: replace scraped social content with vetted academic material, digitize underrepresented history, and build guardrails with purpose.Charna and John also explore the implications for enterprise, healthcare, and education — sectors where small biases can cause serious harm.  TIMESTAMPS[00:00:00] — Intro  [00:02:00] — John's Journey into AI[00:04:00] — Data Sources & Historical Archives[00:06:00] — Underrepresented Digital Histories[00:08:00] — Flawed Training Sets in LLMs[00:10:00] — Measuring & Detecting Bias[00:12:00] — Algorithmic Bias in Hiring[00:14:00] — Copyright & Ethical Data Use[00:16:00] — Multimodal Platform Rollout[00:18:00] — Enterprise Privacy & LLM Hosting[00:20:00] — Optimism & Intergenerational Impact[00:22:00] — Founding in a Crowded Market[00:26:00] — Charna's Takeaways on Systemic Bias[00:28:00] — Guardrails vs Structural Solutions[00:30:00] — Training Data vs Output Behavior[00:32:00] — Algorithmic vs Contextual Bias[00:34:00] — Providing Cultural Context to LLMs[00:36:00] — Community-Based Data Labeling[00:38:00] — The Yard Tour & HBCU Partnerships[00:40:00] — Wrapping up the Season & What's Next QUOTESJohn Pasmore “If a company is using AI to look at resumes, what is it? How is it classifying people's names or, we're surprised that sometimes it's using the name and coming to some conclusion about the desirability of a candidate just based on their name, where maybe that wasn't the intent."Charna Parkey “Instead of modifying the model itself, we can say, okay, here's a historical context, here's a new cultural insight, and here's the situation. Now tell me about the outcome, right?" 

This podcast episode is AI generated, using the tool NotebookLM and summarizes Joy Buloamwini's book "Unmasking AI". Listen in to learn about the groundbreaking insights from Joy Buolamwini's powerful book, Unmasking AI, exploring her personal journey and pivotal research.Key Takeaways from this episode:• AI systems inherently carry algorithmic bias from biased creators and unrepresentative "pale male datasets," leading to disproportionate failures for marginalized groups, especially darker-skinned women.• AI harms extend beyond technical errors, causing profound societal consequences like wrongful arrests and denial of essential services, highlighting the need for a "sociotechnical" understanding of AI.• Algorithmic justice requires shifting power, mandating accountability, ensuring explicit consent for data, and recognizing that not building or deploying certain AI systems is a vital choice.• Driving change in AI requires interdisciplinary collaboration and courageous advocacy, humanizing harms through diverse communication like technical research, art, and personal storytelling, and empowering marginalized voices to influence policy.You can find Joy's book here******Audit Analytics & AI ConsultingBook a call to learn moreMERCH!If you're a fan of nerdy swag, check out our merch store! SupportIf you like what you see, consider buying me a broccoli (it fuels my creativity)

with Audrey Watters | Episode 903 | Tech Tool Tuesday Are we racing toward an AI future without asking the right questions? Author and ed-tech critic Audrey Watters joins me to show teachers how to hit pause, get thoughtful, and keep classroom relationships at the center. Sponsored by Rise Vision Did you know the same solution that powers my AI classroom also drives campus-wide emergency alerts and digital signage? See how Rise Vision can save your school thousands: RiseVision.com/10MinuteTeacher Highlights Include Why “human first” still beats the newest AI tool: Audrey explains how relationships drive real learning. Personalized learning myths busted: How algorithmic “solutions” can isolate students. Practical guardrails for AI: Three reflection questions every teacher should ask before hitting “assign.”

In this powerful episode of For the Love of History, host TC is joined by scholar and author Dr. Anita Say Chan to explore the unsettling historical roots of modern data science and artificial intelligence. Drawing from her groundbreaking book Predatory Data: Eugenics in Big Tech and Our Fight for an Independent Future, Anita uncovers how today's predictive algorithms trace back to 19th-century eugenics. Yes, really. Statistical regression—the backbone of online recommendation engines—was developed by a eugenicist. And that's just the beginning. We unpack how algorithmic bias, data colonialism, and techno-eugenics operate in today's platforms—from Facebook's role in global violence to the AI industry's resistance to regulation. If you're curious about the intersections of technology, race, gender, and power, this is the episode you've been waiting for.

Today, we're stepping into one of the most urgent conversations in tech right now: bias in artificial intelligence.Tracey Spicer AM is a Walkley Award-winning journalist, author, and longtime activist for gender equity. In this episode, she unpacks the unseen biases coded into the technologies we use every day—and what happens when we leave them unchecked. Drawing on years of research for her latest book Man-Made, Tracey explores the ethical challenges and opportunities in AI development, and why we all have a role to play in shaping more equitable outcomes.In this episode, Tracey shares:How gender, race, age, and ability bias are embedded into AI systemsThe real-world impacts of biased tech—from hiring software to image generatorsWhy ‘human in the loop' systems are critical for ethical AIHow organisations can audit their data, clean up algorithms, and lead responsiblyHost: Jenelle McMaster, Deputy CEO and People & Culture Leader at EYGuest: Tracey Spicer AM, journalist, author, and AI ethics advocate

On this episode of the Crazy Wisdom Podcast, host Stewart Alsop welcomes Jessica Talisman, a senior information architect deeply immersed in the worlds of taxonomy, ontology, and knowledge management. The conversation spans the evolution of libraries, the shifting nature of public and private access to knowledge, and the role of institutions like the Internet Archive in preserving digital history. They also explore the fragility of information in the digital age, the ongoing battle over access to knowledge, and how AI is shaping—and being shaped by—structured data and knowledge graphs. To connect with Jessica Talisman, you can reach her via LinkedIn.  Check out this GPT we trained on the conversation!Timestamps00:05 – Libraries, Democracy, Public vs. Private Knowledge Jessica explains how libraries have historically shifted between public and private control, shaping access to knowledge and democracy.00:10 – Internet Archive, Cyberattacks, Digital Preservation Stewart describes visiting the Internet Archive post-cyberattack, sparking a discussion on threats to digital preservation and free information.00:15 – AI, Structured Data, Ontologies, NIH, PubMed Jessica breaks down how AI trains on structured data from sources like NIH and PubMed but often lacks alignment with authoritative knowledge.00:20 – Linked Data, Knowledge Graphs, Semantic Web, Tim Berners-Lee They explore how linked data enables machines to understand connections between knowledge, referencing the vision behind the semantic web.00:25 – Entity Management, Cataloging, Provenance, Authority Jessica explains how libraries are transitioning from cataloging books to managing entities, ensuring provenance and verifiable knowledge.00:30 – Digital Dark Ages, Knowledge Loss, Corporate Control Stewart compares today's deletion of digital content to historical knowledge loss, warning about the fragility of digital memory.00:35 – War on Truth, Book Bans, Algorithmic Bias, Censorship They discuss how knowledge suppression—from book bans to algorithmic censorship—threatens free access to information.00:40 – AI, Search Engines, Metadata, Schema.org, RDF Jessica highlights how AI and search engines depend on structured metadata but often fail to prioritize authoritative sources.00:45 – Power Over Knowledge, Open vs. Closed Systems, AI Ethics They debate the battle between corporations, governments, and open-source efforts to control how knowledge is structured and accessed.00:50 – Librarians, AI Misinformation, Knowledge Organization Jessica emphasizes that librarians and structured knowledge systems are essential in combating misinformation in AI.00:55 – Future of Digital Memory, AI, Ethics, Information Access They reflect on whether AI and linked data will expand knowledge access or accelerate digital decay and misinformation.Key InsightsThe Evolution of Libraries Reflects Power Struggles Over Knowledge: Libraries have historically oscillated between being public and private institutions, reflecting broader societal shifts in who controls access to knowledge. Jessica Talisman highlights how figures like Andrew Carnegie helped establish the modern public library system, reinforcing libraries as democratic spaces where information is accessible to all. However, she also notes that as knowledge becomes digitized, new battles emerge over who owns and controls digital information​​.The Internet Archive Faces Systematic Attacks on Knowledge: Stewart Alsop shares his firsthand experience visiting the Internet Archive just after it had suffered a major cyberattack. This incident is part of a larger trend in which libraries and knowledge repositories worldwide, including those in Canada, have been targeted. The conversation raises concerns that these attacks are not random but part of a broader, well-funded effort to undermine access to information​​.AI and Knowledge Graphs Are Deeply Intertwined: AI systems, particularly large language models (LLMs), rely on structured data sources such as knowledge graphs, ontologies, and linked data. Talisman explains how institutions like the NIH and PubMed provide openly available, structured knowledge that AI systems train on. Yet, she points out a critical gap—AI often lacks alignment with real-world, authoritative sources, which leads to inaccuracies in machine-generated knowledge​​.Libraries Are Moving From Cataloging to Entity Management: Traditional library systems were built around cataloging books and documents, but modern libraries are transitioning toward entity management, which organizes knowledge in a way that allows for more dynamic connections. Linked data and knowledge graphs enable this shift, making it easier to navigate vast repositories of information while maintaining provenance and authority​​.The War on Truth and Information Is Accelerating: The episode touches on the increasing threats to truth and reliable information, from book bans to algorithmic suppression of knowledge. Talisman underscores the crucial role librarians play in preserving access to primary sources and maintaining records of historical truth. As AI becomes more prominent in knowledge dissemination, the need for robust, verifiable sources becomes even more urgent​​.Linked Data is the Foundation of Digital Knowledge: The conversation explores how linked data protocols, such as those championed by Tim Berners-Lee, allow machines and AI to interpret and connect information across the web. Talisman explains that institutions like NIH publish their taxonomies in RDF format, making them accessible as structured, authoritative sources. However, many organizations fail to leverage this interconnected data, leading to inefficiencies in knowledge management​​.Preserving Digital Memory is a Civilization-Defining Challenge: In the digital age, the loss of information is more severe than ever. Alsop compares the current state of digital impermanence to the Dark Ages, where crucial knowledge risks disappearing due to corporate decisions, cyberattacks, and lack of preservation infrastructure. Talisman agrees, emphasizing that digital archives like the Internet Archive, WorldCat, and Wikimedia are foundational to maintaining a collective human memory​​.

Jameela Jamil joins us for a conversation about her rise to fame, the dangers of divisive echo chambers, and her decision to reject harmful beauty narratives.With her signature wit and vulnerability, Jameela's dives into toxic beauty standards, the destructive anti-aging narrative that has plagued women for generations, and the deep-rooted values that inspire Jameela to use her platform as a force for positive change.

We hope you're enjoying our past season rebroadcasts so far. It's been fun to take this trip down memory lane to revisit some of the episodes that you all enjoyed the most. We're on to season 3, and Qudsiya's conversation with Lydia X. Z. Brown, a disability justice advocate and activist, who has dedicated their life and resources to combating injustice, oppression, and violence in all its forms. This is another foundational episode Qudsiya often recommends to listeners who want to understand the concept of intersectionality. Lydia breaks it all down for us in this episode, and applies the concept of intersectionality to the context of algorithmic bias, which is a hot topic these days with the rise of artificial intelligence.⁠⁠⁠⁠⁠⁠⁠⁠⁠Visit our website⁠ ⁠for⁠⁠ transcripts⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-- Subscribe to Qudsiya's Substack, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Getting Down To It⁠⁠⁠ Support the team behind the podcast ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠with a donation⁠⁠⁠Let us know what you think with a comment or review on⁠⁠⁠Apple podcasts.⁠⁠⁠⁠⁠⁠⁠⁠⁠

Comment on the Show by Sending Mark a Text Message.This episode is part of my initiative to provide access to important court decisions  impacting employees in an easy to understand conversational format using AI.  The speakers in the episode are AI generated and frankly sound great to listen to.  Enjoy!Can technology uphold fairness, or is it silently perpetuating bias? Discover the complex world of AI in the hiring process as we unravel the case of Derek Mobley versus Workday Inc. Mobley, a black man over 40 with mental health conditions, challenges the algorithms that he claims have unjustly barred him from over 100 job opportunities. Despite the court's decision not to categorize Workday as an employment agency, the episode prompts a pivotal discussion about the responsibilities HR tech companies might bear when their software influences employment outcomes. We grapple with the concept of disparate impact discrimination and what it means when unintentional practices result in a skewed playing field for protected groups.From the courtrooms to the broader tech landscape, the implications of this case ripple across the HR industry and beyond. We weigh the necessity for transparency, accountability, and fairness in algorithmic decision-making while acknowledging the delicate balance with innovation. Listen as we delve into the potential for increased scrutiny and regulation of HR tech companies, and encourage job seekers to critically engage with the data that drives these systems. Join us in exploring how technology shapes our employment landscape and what needs to change to ensure it does so equitably. If you enjoyed this episode of the Employee Survival Guide please like us on Facebook, Twitter and LinkedIn. We would really appreciate if you could leave a review of this podcast on your favorite podcast player such as Apple Podcasts. Leaving a review will inform other listeners you found the content on this podcast is important in the area of employment law in the United States. For more information, please contact our employment attorneys at Carey & Associates, P.C. at 203-255-4150, www.capclaw.com.Disclaimer: For educational use only, not intended to be legal advice.

Brad M. Thompson, Partner, Epstein Becker & Green PC, Chris Provan, Managing Director & Senior Principal Data Scientist, Mosaic Data Science, and Sam Tyner-Monroe, Ph.D., Managing Director of Responsible AI, DLA Piper LLP (US), discuss how to analyze and mitigate the risk of bias in artificial intelligence through the lens of data science. They cover HHS' Section 1557 Final Rule as it pertains to algorithmic bias, examples of biased algorithms, the role of proxies, stratification of algorithms by risk, how to test for biased algorithms, how compliance programs can be adapted to meet the unique needs of algorithmic bias, the NIST Risk Management Framework, whether it's possible to ever get rid of bias, and how explainability and transparency can mitigate bias. Brad, Chris, and Sam spoke about this topic at AHLA's 2024 Complexities of AI in Health Care in Chicago, IL.To learn more about AHLA and the educational resources available to the health law community, visit americanhealthlaw.org.

What do you need to know about data privacy and generative AI? In this informative episode we explore the dynamic world of data privacy and generative AI with Amruta Moktali, Chief Product Officer at Skyflow - the world's first and only data privacy vault delivered as an API. Amruta's impressive career trajectory spans leading roles at industry giants like Microsoft, Salesforce, and Cleo, culminating in her current position where she champions data privacy solutions. Listen as she shares invaluable insights on the importance of safeguarding original data, the impact of generative AI, and the growing presence of women in technology. Join us as Amruta unpacks the intricacies of data privacy vaults, offers strategies to manage data responsibly, and discusses the challenges unique to remote work environments. Whether you're a business leader, tech enthusiast, or advocate for diversity in the workplace, this episode offers a wealth of knowledge and actionable advice. What We Discuss in This Episode Amruta's journey from product design to tech leadership. The importance of data privacy in the era of generative AI. Major challenges organizations face in maintaining control over shared data. The balance between using original data and ensuring consent and compliance with privacy regulations. Overview of how Skyflow's data privacy vault secures sensitive information. The impact of diversity and inclusion on tech innovation and decision-making. Strategies for companies to ensure AI models are trained with unbiased data. Risks associated with data breaches and proactive prevention measures. The representation of women in tech and encouraging more diverse talent in STEM fields. Podcast Highlights 0:00 – How Amruta found her passion in data privacy. 5:32 - Protecting your data in the context of generative AI. 8:14 - Privacy policies, consent, and the originality of data. 11:29 - Challenges of maintaining control over shared data. 14:55 - Skyflow's solution for securing sensitive data. 19:22 - Diversity and inclusion's impact on tech innovation. 23:17 - Ensuring equitable training data for AI models. 27:06 - Proactive measures to prevent data breaches. 30:45 - Encouraging diverse talent in tech. Favorite Quotes On Data Security: “Privacy policies and regulations are not just boxes to tick—they're about safeguarding the originality and integrity of data.” On Diversity in Tech: “It's not enough to have a seat at the table; genuine inclusion means actively listening and valuing diverse perspectives.” On Proactive Measures: “The repercussions of a data breach are far-reaching; investing in proactive security measures is crucial for protecting both reputation and trust.”   Episode Show Notes and Resources: https://leadersoftransformation.com/podcast/business/509-data-privacy-ai-and-women-in-tech-with-amruta-moktali/ Check out our complete library of episodes and other leadership resources here: https://leadersoftransformation.com ________

Algorithmic bias has long been recognized as a key problem affecting decision-making processes that integrate artificial intelligence (AI) technologies. The increased use of AI in making military decisions relevant to the use of force has sustained such questions about biases in these technologies and in how human users programme with and rely on data based on hierarchized socio-cultural norms, knowledges, and modes of attention. In this post, Dr Ingvild Bode, Professor at the Center for War Studies, University of Southern Denmark, and Ishmael Bhila, PhD researcher at the “Meaningful Human Control: Between Regulation and Reflexion” project, Paderborn University, unpack the problem of algorithmic bias with reference to AI-based decision support systems (AI DSS). They examine three categories of algorithmic bias – preexisting bias, technical bias, and emergent bias – across four lifecycle stages of an AI DSS, concluding that stakeholders in the ongoing discussion about AI in the military domain should consider the impact of algorithmic bias on AI DSS more seriously.

As AI continues to permeate various aspects of society, its impact on decision-making, bias, and future technological developments is complex. How can we navigate the challenges posed by AI, particularly when it comes to fairness and bias in algorithms? What insights can be drawn from the intersection of economics, computer science, and behavioral studies to guide the responsible development and use of AI?In this episode, Sendhil Mullainathan, a prominent economist and professor, delves into these pressing issues. He shares his journey from computer science to behavioral economics and discusses the role of AI in shaping the future of decision-making and societal structures. Sendhil provides a nuanced view of algorithmic bias, its origins, and the challenges in mitigating it. He also explores the potential and pitfalls of AI in healthcare and policymaking, offering insights into how we can harness AI for the greater good while being mindful of its limitations.0:00 - Start1:51 - Introducing Sendhil14:20 - Algorithmic bias29:20 - Handling Bias41:57 - AI and Decision Making57:01 - AI in our Future1:02:29 - Conclusion and the last question

Data Bytes listeners get an exclusive discount to join Women in Data. ⁠⁠⁠⁠View discount here. (00:00:00) Intro(00:00:20) AI in Hiring(00:00:37) Bias in Automation(00:01:04) Welcome to Podcast(00:01:09) Guest Introduction(00:01:16) Journalism to AI(00:01:26) First Encounter with AI(00:02:10) Job Interview with Robot(00:02:48) Research and Rabbit Hole(00:05:24) Hiring Tools Bias(00:05:51) Systemic Hiring Issues(00:07:04) Human Bias in Hiring(00:08:09) Bias in AI Tools(00:13:26) Echo Chamber Effect(00:13:58) Workplace Surveillance(00:14:12) Amazon Hiring Example(00:22:04) AI and Employee Surveillance(00:24:01) Stress from Surveillance(00:24:38) No Privacy on Work Computer(00:25:07) Tools to Track Activity(00:27:45) Productivity Theater(00:28:19) Meaningful Productivity(00:31:24) Tools for Flight Risk(00:35:45) Need for Transparency(00:40:11) Suggestions for Job Seekers(00:41:07) Forced Consumerism(00:43:02) Journalistic Role(00:43:15) Outro --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

Dive into the intricate world of trustworthy AI in this enlightening episode. Discover the multifaceted nature of trustworthiness, from accuracy and reliability to fairness and transparency. Explore the methodologies, technologies, and industry practices shaping trustworthy AI systems. Learn from real-world case studies and envision the promising future of AI that's not just intelligent but also trustworthy. Join us as we unravel the importance of trust in AI for its broader acceptance and effectiveness.----------Resources used in this episode:In AI We Trust: Ethics, Artificial Intelligence, and Reliability [Link].The relationship between trust in AI and trustworthy machine learning technologies [Link].Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [Link].Trustworthy Artificial Intelligence: A Review [Link].Blockchain for explainable and trustworthy artificial intelligence [Link].Trustworthy AI in the Age of Pervasive Computing and Big Data [Link].From Trustworthy Principles to a Trustworthy Development Process: The Need and Elements of Trusted Development of AI Systems [Link].Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [Link].Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-centered AI Systems [Link].Trustworthy AI: From Principles to Practices [Link].Support the Show.Keep AI insights flowing – become a supporter of the show!Click the link for details

Dr. Rob Eschmann, Associate Professor of Social Work at Columbia University, discusses his latest book When the Hood Comes Off: Racism and Resistance in the Digital Age (University of California Press). We cover how social media works to unmask everyday experiences of racism, and how this affects student life at American universities. Dr. Eschmann also shares his research on social media, racial microaggressions, and Black Twitter; thoughts on TikTok and algorithmic bias; and how resisting racism requires engaging in conversation.  

As the pandemic made a doctor visit as easy as a Zoom call and computer vision proved able to distinguish a benign blemish from something more worrisome, guest Eleni Linos, MD, DrPH, grew fascinated with the many ways digital technologies will impact all of medicine, not just her specialty, dermatology. She now believes the future of digital health is the future of health, period. But much work remains to ensure those benefits extend to every sector of society. Linos previews the future of digital health for host and fellow physician Russ Altman on this episode of Stanford Engineering's The Future of Everything podcast.Contact Links:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads or Twitter/XConnect with School of Engineering >>> Twitter/XChapters:(00:00) IntroductionRuss Altman introduces Professor Eleni Linos and they discuss a future where digital health encompasses all aspects of healthcare and how we have moved towards that. (02:10) Defining Digital HealthThe challenge of defining digital health and envisioning a future where it integrates seamlessly into healthcare without differentiation.(03:33) Dermatology and Digital HealthEleni explains her interest in digital tools for dermatology, how they have been applied in dermatology and why they are useful.(06:41) Challenges in Examining Diverse Skin TypesAddressing challenges in dermatological exams for patients with diverse skin tones and backgrounds.(09:05) Impact on Patients and Health DisparitiesAssessing patient reactions & benefits to remote interactions and studying health disparities concerning age, ethnicity, and technology literacy.(10:56) LLMs, Digital Health, & BiasHow large language models (LLMs) like ChatGPT are used in digital health, and their biases, and the need for and how Dr. Linos is working to reduce these.(15:24) Empathy and AI Dr. Linos tells a personal story about empathy demonstrated by Chat GPT, and reflects on the potential of AI to enhance patient interactions and care.(18:47) Social Media in Public HealthInsights into leveraging social media for public health campaigns, the strategies used to influence behavioral changes in specific demographics, and how it was employed during COVID(24:33) Challenges in Digital Medicine EducationExploring the challenges & opportunities in preparing future clinicians for a digital medicine-infused future. (28:20) Stanford Center for Digital HealthThe vision and purpose of the Center for Digital Health at Stanford, emphasizing the collaboration between academia, tech companies, and a global perspective to tackle future health challenges. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads or Twitter/XConnect with School of Engineering >>> Twitter/X

EPISODE 1913: In this KEEN ON show, Andrew talks to new media artist Eryk Salvaggio who sifts through the debris of an AI age in which we can no longer trust anything we seeEryk Salvaggio is an interdisciplinary design researcher and new media artist. His work explores emerging technologies through a critically engaged lens, testing their mythologies and narratives against their impacts on social and cultural ecosystems. His work, which focuses on generativity and artificial intelligence, often exposes the ideologies embedded into technologies. His work has been curated into film and music festivals, gallery installations, and conferences (such as DEFCON 31 and SXSW). The work interrogates generative AI through a blend of cybernetics, visual culture & media theory, with a critique grounded in resistance and creative misuse, highlighting the gaps that emerge between the analog and digital, such as datasets and the world they claim to represent. Eryk has since worked with partners including AIxDesign's Story & Code program, the AI Village at DEFCON 31, Space10, the Australian National University, the Swiss National Science Foundation, the Wikimedia Foundation, the Internet Archive, and the National Gallery of Australia. His work has been published in academic journals such as Leonardo, Communications of the ACM, IMAGE, Patterns, and by art publishers including DAHJ Gallery, the Furtherfield gallery (London), Turbulence (Boston), Rhizome (New York) and 10th Floor Design Studios (San Francisco). His artwork has been included in pieces with the BBC4, The New York Times, ArtForum, NBC News, Neural, Dirty, and Mute Magazine. His work has been exhibited at Michigan State University Science Museum, the UN Internet Governance Forum, Eyebeam, CalArts, Brown University, Turbulence, The Internet Archive, and in books including Jon Ippolito & Joline Blais' At the Edge of Art, Alex Galloway's Protocol: How Control Exists After Decentralization, and Peter Langford's Image & Imagination. He has presented talks, keynotes and works at SXSW, DEFCON 31, the Systems Research & Design Conference (RSD10&11), the Advances in Systems Sciences and Systems Practice Conference (2022), Melbourne Design Week (2021), MIT Press (2021), the University of St. Gallen (2018), California College of the Arts (2018, 2019, 2020), the University of Maine, RightsCon (2020), and Gensler San Francisco (2017). As a Wikipedia Visiting Scholar at Brown University, he created the article on Algorithmic Bias in 2016. Eryk has taught at the Elisava Barcelona School of Design and Engineering, RIT, and Bradley Universities, and has given talks or lectures at NYU, the University of Cambridge, Aarhus, the University of Copenhagen, and Northeastern. He holds a Masters in Media and Communication from the London School of Economics and a Masters in Applied Cybernetics from the Australian National University. He earned two concurrent undergraduate degrees, in New Media and Journalism, from the University of Maine, where he was listed as visiting faculty as an undergraduate based on his early interactive, online net.art work.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.

Guest: Dr. Bruce Y Lee, Executive Director of PHICOR (Public Health Informatics, Computational, and Operations Research) [@PHICORteam]On LinkedIn | https://www.linkedin.com/in/bruce-y-lee-68a6834/On Twitter | https://twitter.com/bruce_y_leeWebsite | https://www.bruceylee.com/On Forbes | https://www.forbes.com/sites/brucelee/On Psychology Today | https://www.psychologytoday.com/us/contributors/bruce-y-lee-md-mba_____________________________Host: Marco Ciappelli, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining Society PodcastOn ITSPmagazine | https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/marco-ciappelli_____________________________This Episode's SponsorsBlackCloak

In this interview, Dr. Joy Buolamwini argues that algorithmic bias in AI systems poses an existential risk to marginalized people. She challenges the assumptions of tech leaders who advocate for AI “alignment” and explains why tech companies are hypocritical when it comes to addressing bias. Dr. Joy Buolamwini is the founder of the Algorithmic Justice League and the author of “Unmasking AI: My Mission to Protect What Is Human in a World of Machines.”Correction: Aza says that Sam Altman, the CEO of OpenAI, predicts superintelligence in four years. Altman predicts superintelligence in ten years. RECOMMENDED MEDIAUnmasking AI by Joy Buolamwini“The conscience of the AI revolution” explains how we've arrived at an era of AI harms and oppression, and what we can do to avoid its pitfallsCoded BiasShalini Kantayya's film explores the fallout of Dr. Joy's discovery that facial recognition does not see dark-skinned faces accurately, and her journey to push for the first-ever legislation in the U.S. to govern against bias in the algorithms that impact us allHow I'm fighting bias in algorithmsDr. Joy's 2016 TED Talk about her mission to fight bias in machine learning, a phenomenon she calls the "coded gaze." RECOMMENDED YUA EPISODESMustafa Suleyman Says We Need to Contain AI. How Do We Do It?Protecting Our Freedom of Thought with Nita FarahanyThe AI Dilemma Your Undivided Attention is produced by the Center for Humane Technology. Follow us on Twitter: @HumaneTech_ 

This season, IRL host Bridget Todd meets people who are balancing the upsides of artificial intelligence with the downsides that are coming into view worldwide. Stay tuned for the first of five biweekly episodes on October 10! IRL is an original podcast from the non-profit Mozilla.

The popular social media app Instagram and its parent company, Meta, use artificial intelligence to moderate content. But there are growing concerns that the “training data” for AI is biased against women and people of color. A Los Angeles photographer thinks this “algorithmic bias” is part of the reason Instagram disabled his account. Reporter: Beth Tribolet, KQED The California legislature has passed a bill that would ban the hand-counting of ballots in most elections. The legislation was targeted specifically at Northern California's Shasta County, where supervisors did away with Dominion voting machines earlier this year.  Reporter: Roman Battaglia, Jefferson Public Radio  Much of the world's highest quality cotton is grown in the San Joaquin Valley. But the return of Tulare Lake could have a devastating impact on the Central Valley's cotton industry. Reporter: Kerry Klein, KVPR

Guest: Eric Olson, Co-Founder & CEO at Consensus.app [@ConsensusNLP]On LinkedIn | https://www.linkedin.com/in/eric-olson-1822a7a6/On Twitter | https://twitter.com/IPlayedD1_____________________________Host: Marco Ciappelli, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining Society PodcastOn ITSPmagazine | https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/marco-ciappelli_____________________________This Episode's SponsorsBlackCloak

Guillermo Corea is the Managing Director of the SHRM Workplace Innovation Lab and Venture Capital initiatives. He joined SHRM in 2015. He and his team are focused on finding and cultivating technologies that will impact the future of work. Guillermo's team organizes the SHRM Better Workplaces Challenge Cup and Workplace Tech Accelerator plus they lead the organization's impact investing program. Guillermo is a vocal leader in the HRTech community.  This was a fun one because we got to record in person at SHRMTech 2023 in San Francisco. Only our fifth live recording in more than 190 episodes!Listen and learn...How HR teams should drive workplace innovation Which Shark Tank shark is judging the Better Workplaces Challenge CupHow SHRM Labs connects tech entrepreneurs with HR leaders Why the CHRO is the most strategic exec in the C-suite How the pandemic and an aging employee population are creating opportunities for HRTech The technology Guillermo says will change work most in the next decade How to confront the problem of biased algorithms making HR decisions Why the HR blockchain will replace background check vendors The HRTech company Guillermo is ready to fund! References in this episode...Reza Nazeman, former CIO of SAP Concur, on AI and the Future of WorkKamal Ahluwalia, Eightfold President, on AI and the Future of WorkJason Corsello, VA at Acadia Ventures, on AI and the Future of WorkSHRM Labs

There's an extra dose of dysfunction in this episode but we think it fits since we're talking about how the computers are all trying to take our jobs and the best way to fight back! Okay not really - but we are talking about Artificial Intelligence (AI) and Algorithmic Bias in Healthcare today. Piggybacking on our episodes about Medical Bias (#29 and #30, in case you want to re-listen real quick), this episode was recommended to us by a listener and we take a look at what AI in Healthcare looks like, what it is used for, and why it can be a problem. Hospitals and doctors use AI every day to help diagnose and treat patients - but what happens when the mathematic equation (a/k/a ALGORITHM) used to get those diagnoses was written by someone who is biased? Or what happens when the data we used to create that algorithm is all skewed in the first place? We also talk about ways to combat those problems, so don't worry - it's not the worst thing to ever happen, unless you ask Garet, in which case, the world is ending.    Follow us on Instagram at www.instagram.com/thebarisanklehigh or look us up @thebarisanklehigh and consider joining our Patreon at www.patreon.com/thebarisanklehigh. You can also check out our merch at bit.ly/anklehighmerch if you want to put us on your body!   Sources:  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6875681/ https://nihcm.org/publications/artificial-intelligences-racial-bias-in-health-care https://www.science.org/doi/10.1126/science.aax2342 https://en.wikipedia.org/wiki/Artificial_intelligence https://www.researchgate.net/profile/Huiwen-Loh/publication/360001378_Automated_detection_of_ADHD_Current_trends_and_future_perspective/links/625b57eaa279ec5dd7fd6e8c/Automated-detection-of-ADHD-Current-trends-and-future-perspective.pdf https://www.nature.com/articles/s41746-020-0288-5 https://www.healthnavigator.org.nz/health-a-z/h/heart-attack-symptoms-womens/  

Algorithms and artificial intelligence (AI) are increasingly being deployed in the financial services industry, with massive potential to automate and enhance processes, increase efficiency, improve customer service, and augment investment and lending analyses. However, with those potential benefits come challenges, such as the risk that AI applications may result in unintended bias or “unfair” discrimination against certain sub-groups. Please join Mayer Brown partners Niketa Patel, Tori Shinohara, and Jenn Rosa as they discuss potential risks and the current federal regulation landscape with respect to AI.

Camille Castelyn interviews Marisa Dallas about her diverse career interests including sex-based bias in healthcare artificial intelligence algorithms, patient non-compliance, and the intersection of the pharmaceutical industry and bioethics. Dallas is currently a Doctor of Pharmacy Candidate at the University of Michigan. She completed a Bachelor of Science in Human Biology at Michigan State University and a Master of Science in Bioethics at Columbia University.

EXPERTENGESPRÄCH | Eine künstliche Intelligenz kann nur so sensibel sein, wie der Mensch, der sie programmiert hat. Dadurch dass es auch beim Menschen immer noch viele Blindspots beim Thema Diversity gibt, spiegeln sich diese natürlich auch in der Maschine wieder. Im Gespräch mit Autorin Kenza Ait Si Abbou, erkunden Joel und Lunia diesmal, wie man diese Algorithmic Biases erkennt und korrigieren kann und wie man sowohl Mensch, als letztlich auch künstliche Intelligenz für Diversity besser sensibilisieren kann. Du erfährst... …warum Algorithmic Biases entstehen …welche Algorithmic Biases in KI derzeit ein Problem sind …ob man diese Biases einfach „reparieren“ kann …wie die Methode des Reinforced Learnings mit dem Thema Diskriminierung umgeht …ob zuviel Nachbearbeitung von KI-Ergebnissen auch eine Gefahr darstellen können …mit welchen Methoden man Diskriminierung entlernen kann …welche Empathie in die Führung spielt …wie man Hierarchie-Barrieren innerhalb einer Firma abbauen kann Diese Episode dreht sich schwerpunktmäßig um Diversity: Lasst uns Organisationen neu, offen und tolerant denken! Nachdem wir anfangs die Organisationsentwicklerin Marina Löwe und Ratepay-Gründerin Miriam Wohlfarth wiederholt vor dem Mirko hatten, um dich für Diversity zu sensibilisieren, diskutiert Joel mittlerweile regelmäßig mit Lunia Hara (Diconium) zu Themen rund um Leadership und Diversity. Dabei geht es den beiden explizit nicht um Mann oder Frau, sondern um die Schaffung von Empathie füreinander sowie ein ganzheitliches Bild und Verständnis für verschiedene Rollen und Perspektiven. __________________________ ||||| PERSONEN |||||

Welcome back to the Tech Policy Grind! Today, Reema chats with Caitlin Chin, a Class 4 Fellow at the Foundry and a a fellow at the Center for Strategic and International Studies (CSIS), on her work at the intersection of privacy, antitrust, and algorithmic bias. At CSIS, Caitlin researches technology regulation in the United States and abroad. She previously worked as a research analyst at the Brookings Institution, where her projects centered around U.S. federal and state legislation related to information privacy, antitrust, and algorithmic bias. At Brookings, Chin coauthored "Bridging the gaps: A path forward to federal privacy legislation" (with Cameron Kerry, John Morris Jr., and Nicol Turner Lee), which put forward a comprehensive framework for national commercial privacy standards in the United States. In addition, she has published over two dozen other reports or commentaries on public policy issues including "Addressing Big Tech's power over speech" (with Bill Baer) and "Why Democrats and Republicans would benefit from hate crime protections for Asian Americans." She's also spoken on C-SPAN, WOSU/NPR, and France 24, and her work has been cited by the Washington Post, the Wall Street Journal, and the Future of Privacy Forum, among other organizations. She has a BA in government and Spanish from the University of Maryland and an MPP from Georgetown University's McCourt School of Public Policy. Her master's thesis, "Examining national privacy laws in the context of international trade," won a student paper award at the 48th Research Conference on Communications, Information, and Internet Policy (TPRC48) in 2020. She was also a recipient of Public Knowledge's 20/20 Visionaries award in 2021. Coming soon from the Foundry: keep an eye out for the next round of applications to become a Foundry Fellow! If you'd like to sponsor an episode or propose a guest for the show, get in touch with us: foundrypodcasts@ilpfoundry.us If you'd like to support the show, consider donating to the Foundry; you can do so here.

Maia Hightower, M.D., M.B.A., MPH Chief Digital Technology officer of the University of Chicago MedicineIvor Horn, M.D. MPH Director, Health Equity & Social Determinants of Health, Google Artificial Intelligence is full of technological and economic promise, but just like its creators, AI isn't free from subconscious discrimination. As AI becomes more commonplace in the medical field, questions of whether racial bias will be mitigated or expanded in the future are omnipresent. The solution will depend on how much effort is put into making AI more equitable. Join Lee Hawkins, Drs. Maia Hightower and Ivor Horne as they delve into this new frontier.“Algorithmic bias is part of our history. It is part of the history of medicine, part of the history of the United States, and part of the history of our world, for many reasons.”--Dr. Maia Hightower“The real-world bias is in the real-world data.”--Dr. Maia Hightower“I literally went into medicine to transform the way people behave in, the way physicians behave in health care.”— Ivor Horn, M.D. MPH“And when I think about technology, it's all about, ‘how am I giving people more information, more access,' so that when they walk through the doors of a health care system, like, they have the tools to say, ‘I know this, I understand this, this is my question for you, and this is what I expect of this health care system for me and for my family.'”— Ivor Horn, M.D. MPH“We know that data shows that more diverse teams have better outcomes. They're more, businesses are more profitable when they have more diverse teams sitting around the table. And it's really important not just to have the team but also give the team space to speak and confidence to have their voice and bring and be their full selves when they come to the room.”— Ivor Horn, M.D. MPH

Welcome back to the Tech Policy Grind Podcast by the Internet Law and Policy Foundry!  In this episode, Class 4 Fellow Lama Mohammed interviews Sophia Baik, a Postdoctoral Researcher at the Center for Long-Term Cybersecurity and incoming Assistant Professor at the Department of Communication Studies at the University of San Diego. Episode 15 follows Sophia and Lama in a detailed discussion on the future of the American Data Protection and Privacy Act (ADPPA) — the United States' most comprehensive federal privacy legislation to date — by breaking the bill down, highlighting its significance and the future of the bill when Congress comes back from the August recess.   While the rest of the world responds to the growing ubiquitous nature of technology through its enactment of comprehensive privacy bills, the United States continues to fall behind by failing to pass federal privacy legislation. Our experts use this opportunity to dive into why the ADPPA is such a monumental bill, especially as it relates to protecting civil rights and liberties in the digital era. Although the bill is at risk of failing to pass to the House, Sophia provides listeners with recommendations on how to get involved with the privacy movement and how to protect our online data and digital identities.  You can connect with Sophia on Twitter (@jeeyunbaik) and read all her amazing published research on her Google Scholar profile. Thanks for listening, and stay tuned for our next episode!

Artificial intelligence has a variety of useful applications in healthcare. But algorithmic bias, along with data privacy and security concerns, have prompted significant ethical and legal concerns. Linda Malek, partner at Moses & Singer and chair of the firm's healthcare, privacy, and cybersecurity practice group, discusses the risks associated with AI in healthcare.

Too many young people – particularly young people of color – lack enough familiarity or experience with emerging technologies to recognize how artificial intelligence can impact their lives, in either a harmful or an empowering way. Educator Ora Tanner saw this and rededicated her career toward promoting tech literacy and changing how we understand data sharing and surveillance, as well as teaching how AI can be both a dangerous tool and a powerful one for innovation and activism.By now her curricula have touched more than 30,000 students, many of them in her home state of Florida. Tanner also went to bat against the Florida Schools Safety Portal, a project to amass enormous amounts of data about students in an effort to predict and avert school shootings – and a proposal rife with potential biases and abuses.Tanner speaks with EFF's Cindy Cohn and Jason Kelley on teaching young people about the algorithms that surround them, and how they can make themselves heard to build a fairer, brighter tech future.In this episode you'll learn about:Convincing policymakers that AI and other potentially invasive tech isn't always the answer to solving public safety problems.Bringing diverse new voices into the dialogue about how AI is designed and used.Creating a culture of searching for truth rather than just accepting whatever information is put on your plate.Empowering disadvantaged communities not only through tech literacy but by teaching informed activism as well.This podcast is supported by the Alfred P. Sloan Foundation's Program in Public Understanding of Science and Technology.Music for How to Fix the Internet was created for us by Reed Mathis and Nat Keefe of BeatMower. This podcast is licensed Creative Commons Attribution 4.0 International, and includes the following music licensed Creative Commons Attribution 3.0 Unported by their creators: Meet Me at Phountain by gaetanh (c) copyright 2022 http://ccmixter.org/files/gaetanh/64711Hoedown at the Roundabout by gaetanh (c) copyright 2022  http://ccmixter.org/files/gaetanh/64711JPEG of a Hotdog by gaetanh (c) copyright 2022 http://ccmixter.org/files/gaetanh/64711reCreation by airtone (c) copyright 2019 http://dig.ccmixter.org/files/airtone/59721    

For today's episode, Tyrell and Daphne explore how artificial intelligence contributes to racial inequity by discussing the Netflix documentary, Coded Bias. The hosts begin the episode by catching up (00:30) and discussing “Oh Lawd” news (6:40). They then turn their attention to the topic of algorithmic bias and the future of technology (43:38). They start the conversation by discussing the history of artificial intelligence (44:40), how algorithmic determinism shapes decision making across various industries (47:25), and how algorithms are used in the United States and abroad (54:00). They close the episode by discussing the social consequences of algorithmic bias as well as key lessons and takeaways from the documentary (1:06:55).  Resources:  BhD Patreon - https://www.patreon.com/bhdpodcast  The Dark Side of Reform- https://rowman.com/ISBN/9781793643759/The-Dark-Side-of-Reform-Exploring-the-Impact-of-Public-Policy-on-Racial-EquityDiscount Code (30% Off): LXFANDF30