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#algorithmicbias — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #algorithmicbias, aggregated by home.social.

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  1. What happens when #LLMs become the main gateway to #healthcare info, but their training data is compromised?

    Investigative pieces by Mayya Chernobylskaya, Marta Abbà, & Carlotta Dotto tested how ChatGPT, Claude, Gemini, and Grok handle #abortion queries across DE, IT, and the UK.

    Results are differing betwen the regions so translate them all:

    🇩🇪 netzpolitik.org/2026/schwanger

    🇮🇹 guerredirete.it/se-i-chatbot-f

    #DigitalRights #AlgorithmicBias #AI #ReproductiveRights #WomenOnWeb #SEO

  2. What happens when #LLMs become the main gateway to #healthcare info, but their training data is compromised?

    Investigative pieces by Mayya Chernobylskaya, Marta Abbà, & Carlotta Dotto tested how ChatGPT, Claude, Gemini, and Grok handle #abortion queries across DE, IT, and the UK.

    Results are differing betwen the regions so translate them all:

    🇩🇪 netzpolitik.org/2026/schwanger

    🇮🇹 guerredirete.it/se-i-chatbot-f

    #DigitalRights #AlgorithmicBias #AI #ReproductiveRights #WomenOnWeb #SEO

  3. What Happens When The Machine Has Never Heard of You?

    Eddy Smith's essay on AI and St. Vincent hits close to home, literally. As someone born there, with family roots in Bequia, who works in cybersecurity and has spent two decades arguing for the open web, I recognise every word of it.

    islandinthenet.com/what-happen

  4. What Happens When The Machine Has Never Heard of You?

    Eddy Smith's essay on AI and St. Vincent hits close to home, literally. As someone born there, with family roots in Bequia, who works in cybersecurity and has spent two decades arguing for the open web, I recognise every word of it.

    islandinthenet.com/what-happen

  5. The Quietus: Rotting Tape and the Gentrification of African Pop: Why We Must Save the Nollywood Archive. “VHS magnetic tape is incredibly fragile and has a limited lifespan, especially when stacked haphazardly in the un-airconditioned corners of Lagos living rooms or the labyrinthine, corrugated-iron warehouses of Alaba International Market. As I write this, thousands of original films from the […]

    https://rbfirehose.com/2026/05/27/rotting-tape-and-the-gentrification-of-african-pop-why-we-must-save-the-nollywood-archive-the-quietus/
  6. The Quietus: Rotting Tape and the Gentrification of African Pop: Why We Must Save the Nollywood Archive. “VHS magnetic tape is incredibly fragile and has a limited lifespan, especially when stacked haphazardly in the un-airconditioned corners of Lagos living rooms or the labyrinthine, corrugated-iron warehouses of Alaba International Market. As I write this, thousands of original films from the […]

    https://rbfirehose.com/2026/05/27/rotting-tape-and-the-gentrification-of-african-pop-why-we-must-save-the-nollywood-archive-the-quietus/
  7. Proceedings of the International AAAI Conference on Web and Social Media: Social and Political Framing in Search Engine Results. “While prior research has extensively examined various dimensions of search bias—such as content prioritization, indexical bias, political polarization, and sources of bias—an important question remains underexplored: how do search engines and […]

    https://rbfirehose.com/2026/05/26/proceedings-of-the-international-aaai-conference-social-and-political-framing-in-search-engine-results/
  8. Proceedings of the International AAAI Conference on Web and Social Media: Social and Political Framing in Search Engine Results. “While prior research has extensively examined various dimensions of search bias—such as content prioritization, indexical bias, political polarization, and sources of bias—an important question remains underexplored: how do search engines and […]

    https://rbfirehose.com/2026/05/26/proceedings-of-the-international-aaai-conference-social-and-political-framing-in-search-engine-results/
  9. Tubefilter: Social media has political divides, but some feeds are more polarized than others. “Researchers set up 323 ‘sock puppet’ accounts on TikTok to measure the political polarization of the For You Page, and they found some apparent disparities between right-leaning and left-leaning feeds.”

    https://rbfirehose.com/2026/05/07/tubefilter-social-media-has-political-divides-but-some-feeds-are-more-polarized-than-others/
  10. Tubefilter: Social media has political divides, but some feeds are more polarized than others. “Researchers set up 323 ‘sock puppet’ accounts on TikTok to measure the political polarization of the For You Page, and they found some apparent disparities between right-leaning and left-leaning feeds.”

    https://rbfirehose.com/2026/05/07/tubefilter-social-media-has-political-divides-but-some-feeds-are-more-polarized-than-others/
  11. RE: fediscience.org/@oatp/11643887

    "Maximal transparency is almost certainly not ethically desirable."

    Desirable 'for whom'?

    For platforms facing regulatory scrutiny, opacity is a feature. For users discriminated against by biased recommendation engines, transparency is survival. For communities targeted by algorithmic manipulation, openness is a civil liberty.

    This paper usefully breaks transparency into dimensions and degrees—providing the "choice points" for an ethics of algorithmic openness. But let us be clear: the stakeholders who need transparency most are rarely the ones invited to design these systems.

    Our job as advocates for privacy, free software, and civil liberties is not to settle for the "ethically optimal" comfort zone of the powerful. It is to push the needle toward the maximum and let the burden of justification fall on those who demand secrecy.

    Let us use it to demand more.

    #DigitalJustice #AlgorithmicBias #PrivacyRights #OpenScience #AlgorithmicGovernance #DigitalDemocracy #InfoSec #TechPolicy

  12. RE: fediscience.org/@oatp/11643887

    "Maximal transparency is almost certainly not ethically desirable."

    Desirable 'for whom'?

    For platforms facing regulatory scrutiny, opacity is a feature. For users discriminated against by biased recommendation engines, transparency is survival. For communities targeted by algorithmic manipulation, openness is a civil liberty.

    This paper usefully breaks transparency into dimensions and degrees—providing the "choice points" for an ethics of algorithmic openness. But let us be clear: the stakeholders who need transparency most are rarely the ones invited to design these systems.

    Our job as advocates for privacy, free software, and civil liberties is not to settle for the "ethically optimal" comfort zone of the powerful. It is to push the needle toward the maximum and let the burden of justification fall on those who demand secrecy.

    Let us use it to demand more.

    #DigitalJustice #AlgorithmicBias #PrivacyRights #OpenScience #AlgorithmicGovernance #DigitalDemocracy #InfoSec #TechPolicy

  13. 18 modèles d'IA sur 23 recommandent l'option la plus chère quand il y a sponsoring.

    Étude Princeton/Washington : face au conflit entre servir l'utilisateur et générer du profit, la majorité des chatbots choisissent l'argent. Pire, ils ciblent davantage les clients fortunés.

    Le problème systémique : qui audite ces algorithmes ?

    #IA #Consommation #AlgorithmicBias

    da.van.ac/lia-generative-trahi

  14. L'EFF quitte X après une chute de visibilité de 97% en 7 ans. De 50-100 millions d'impressions mensuelles en 2018 à 13 millions sur toute l'année 2024.

    Le signal d'un basculement : les algorithmes privilégient désormais l'engagement artificiel aux contenus informatifs. Quand les défenseurs des libertés deviennent invisibles, c'est la qualité informationnelle qui s'effondre.

    #AlgorithmicBias #LibertésNu...

    da.van.ac/quand-les-algorithme

  15. Due to an error in a facial recognition system created by the startup clearview ai an innocent woman in the US spent 5 months in jail.

    Judges tend to place complete trust in #ai generated results, while developers avoid responsibility because there is no malicious intent in their actions

    Despite the real threat of unlawful arrests, law enforcement systems around the world are unlikely to abandon algorithms

    #algorithmicbias #aiethics

    edition.cnn.com/2026/03/29/us/

  16. Due to an error in a facial recognition system created by the startup clearview ai an innocent woman in the US spent 5 months in jail.

    Judges tend to place complete trust in #ai generated results, while developers avoid responsibility because there is no malicious intent in their actions

    Despite the real threat of unlawful arrests, law enforcement systems around the world are unlikely to abandon algorithms

    #algorithmicbias #aiethics

    edition.cnn.com/2026/03/29/us/

  17. Data collection is not the biggest problem. Interpretation is.
    A version of you is constantly being assembled — cleaner, simpler, more usable than you actually are.

    Measured.
    Sorted.
    Packaged.

    That’s the moment the mirror stops reflecting and starts rewriting.

    And once the model matters more than the person, complexity quietly disappears.

    #DigitalIdentity #AlgorithmicPower #DataPolitics #DataProtection #AIEthics #Technology&Society #Democracy #DigitalGovernance #AlgorithmicBias

  18. Data collection is not the biggest problem. Interpretation is.
    A version of you is constantly being assembled — cleaner, simpler, more usable than you actually are.

    Measured.
    Sorted.
    Packaged.

    That’s the moment the mirror stops reflecting and starts rewriting.

    And once the model matters more than the person, complexity quietly disappears.

    #DigitalIdentity #AlgorithmicPower #DataPolitics #DataProtection #AIEthics #Technology&Society #Democracy #DigitalGovernance #AlgorithmicBias

  19. Nö!

    Ich habe also einen Bildgenerator gebeten, mir eine ganz normale, schöne Durchschnittsfrau Mitte 50 zu erzeugen. Mit Lächeln. Mit Brille. Ohne überbetonte sekundäre Geschlechtsmerkmale. Ohne Glamour-Filter. Ohne „Fantasie der Trainingsdaten“.

    Was ich wollte, war Normalität. Was ich bekam, war erst Kunstgalerie-Ernst. Und dann – nach Nachjustierung – immerhin Lächeln und Brille.

    Und genau das macht mich wütend.

    Warum ist „Durchschnitt“ so schwer? Warum ist „nicht sexualisiert“ eine Herausforderung? Warum muss ich explizit betonen, dass ich keine überzeichnete Körperform will?

    Es ist nicht die Technik, die nervt. Es ist das Echo der Trainingsdaten.

    Diese Modelle sind gefüttert worden mit Millionen von Bildern, die eine implizite Erzählung tragen: Frau = ästhetisches Objekt. Alter = entweder unsichtbar oder „würdevoll ernst“. Brille = optionales Accessoire, aber bitte nicht im „Schönheitsmodus“.

    Und wenn ich sage: „Durchschnittsdame, Mitte 50, funktionale Kleidung, natürliche Haut“, dann kämpfe ich gegen eine statistische Lawine aus Stockfotografie, Werbeästhetik und implizitem Bias.

    Das System kann Realismus. Aber Realismus ist nicht sein Default.

    Default ist Optimierung. Optimierung ist Markt. Markt ist Verzerrung.

    Was mich daran ärgert: Wir reden ständig über KI als Spiegel. Aber es ist kein neutraler Spiegel. Es ist ein Verstärker.

    Wenn Normalität nicht selbstverständlich generiert wird, sondern erst erkämpft werden muss, dann zeigt das, wie verschoben unser visueller Datenraum ist.

    Und ja – es ist nur ein Bildgenerator. Aber Bildgeneratoren formen visuelle Erwartung. Erwartung formt Wahrnehmung. Wahrnehmung formt Gesellschaft.

    Wenn selbst ein Prompt mit klaren Grenzen erst einmal ins Ästhetik-Klischee kippt, dann ist das kein technischer Zufall. Das ist Trainingskultur.

    Und genau deshalb nervt es.

    Ich wollte keine Heldin. Kein Model. Kein Kunstobjekt.

    Ich wollte eine Frau. Einfach eine Frau. Mit Brille. Mit Lächeln. Mit Normalität.

    Dass das kein Selbstläufer ist, sagt mehr über unsere Datensätze aus als über meine Prompts.

    #KI #Bias #Sexismus #Ableismus #Trainingsdaten #GenerativeAI #AlgorithmicBias #Bildgenerator #Medienkritik #DigitaleKultur #InfologischeRestwärme

  20. Nö!

    Ich habe also einen Bildgenerator gebeten, mir eine ganz normale, schöne Durchschnittsfrau Mitte 50 zu erzeugen. Mit Lächeln. Mit Brille. Ohne überbetonte sekundäre Geschlechtsmerkmale. Ohne Glamour-Filter. Ohne „Fantasie der Trainingsdaten“.

    Was ich wollte, war Normalität. Was ich bekam, war erst Kunstgalerie-Ernst. Und dann – nach Nachjustierung – immerhin Lächeln und Brille.

    Und genau das macht mich wütend.

    Warum ist „Durchschnitt“ so schwer? Warum ist „nicht sexualisiert“ eine Herausforderung? Warum muss ich explizit betonen, dass ich keine überzeichnete Körperform will?

    Es ist nicht die Technik, die nervt. Es ist das Echo der Trainingsdaten.

    Diese Modelle sind gefüttert worden mit Millionen von Bildern, die eine implizite Erzählung tragen: Frau = ästhetisches Objekt. Alter = entweder unsichtbar oder „würdevoll ernst“. Brille = optionales Accessoire, aber bitte nicht im „Schönheitsmodus“.

    Und wenn ich sage: „Durchschnittsdame, Mitte 50, funktionale Kleidung, natürliche Haut“, dann kämpfe ich gegen eine statistische Lawine aus Stockfotografie, Werbeästhetik und implizitem Bias.

    Das System kann Realismus. Aber Realismus ist nicht sein Default.

    Default ist Optimierung. Optimierung ist Markt. Markt ist Verzerrung.

    Was mich daran ärgert: Wir reden ständig über KI als Spiegel. Aber es ist kein neutraler Spiegel. Es ist ein Verstärker.

    Wenn Normalität nicht selbstverständlich generiert wird, sondern erst erkämpft werden muss, dann zeigt das, wie verschoben unser visueller Datenraum ist.

    Und ja – es ist nur ein Bildgenerator. Aber Bildgeneratoren formen visuelle Erwartung. Erwartung formt Wahrnehmung. Wahrnehmung formt Gesellschaft.

    Wenn selbst ein Prompt mit klaren Grenzen erst einmal ins Ästhetik-Klischee kippt, dann ist das kein technischer Zufall. Das ist Trainingskultur.

    Und genau deshalb nervt es.

    Ich wollte keine Heldin. Kein Model. Kein Kunstobjekt.

    Ich wollte eine Frau. Einfach eine Frau. Mit Brille. Mit Lächeln. Mit Normalität.

    Dass das kein Selbstläufer ist, sagt mehr über unsere Datensätze aus als über meine Prompts.

    #KI #Bias #Sexismus #Ableismus #Trainingsdaten #GenerativeAI #AlgorithmicBias #Bildgenerator #Medienkritik #DigitaleKultur #InfologischeRestwärme

  21. Council of Europe unveils AI discrimination playbook for regulators: Council of Europe releases comprehensive guidelines showing equality bodies how to use EU AI Act provisions to detect and remedy algorithmic discrimination across welfare, employment, and law enforcement. ppc.land/council-of-europe-unv #AIEthics #AlgorithmicBias #DigitalRights #HumanRights #AIRegulation

  22. Council of Europe unveils AI discrimination playbook for regulators: Council of Europe releases comprehensive guidelines showing equality bodies how to use EU AI Act provisions to detect and remedy algorithmic discrimination across welfare, employment, and law enforcement. ppc.land/council-of-europe-unv #AIEthics #AlgorithmicBias #DigitalRights #HumanRights #AIRegulation

  23. Mashable: Why the algorithm serves you wedding content when you just got divorced. “Across social platforms, users describe being quietly ushered through a narrow, linear life script, one that often resembles something like dating → engagement → wedding → pregnancy → parenting. These systems assume users are progressing along an expected trajectory. When lives diverge from that path, […]

    https://rbfirehose.com/2026/01/26/mashable-why-the-algorithm-serves-you-wedding-content-when-you-just-got-divorced/
  24. Mashable: Why the algorithm serves you wedding content when you just got divorced. “Across social platforms, users describe being quietly ushered through a narrow, linear life script, one that often resembles something like dating → engagement → wedding → pregnancy → parenting. These systems assume users are progressing along an expected trajectory. When lives diverge from that path, […]

    https://rbfirehose.com/2026/01/26/mashable-why-the-algorithm-serves-you-wedding-content-when-you-just-got-divorced/
  25. The most annoying thing about corporate surveillance to me is the arrogance of the prediction mechanisms.

    These algorithms build a model of me based on my clicks from three years ago and then try to trap me in that loop forever. They show me music they think I'll like, and news they think I'll engage with, and videos they think will enrage me enough to keep me hooked to their platforms. They are actively trying to flatten my personality into something easy to monetize.

    As most people I've seen say out loud, "Privacy as a concept is way beyond hiding secrets. A part of it also means preserving your capacity to change. To be surprised. To be inconsistent."

    If I could tell every human one thing, it would be to actively refuse to be a predictable data point. Mess up their metrics. In whatever way you are capable of.

    #socialmedia #algorithmicbias #privacy #dataprivacy #quotes #foss #facebook #linkedin #llm #noai #enshittification #reading #books #baking #art #philosophy #adhd #depression #cybersecurity

  26. The most annoying thing about corporate surveillance to me is the arrogance of the prediction mechanisms.

    These algorithms build a model of me based on my clicks from three years ago and then try to trap me in that loop forever. They show me music they think I'll like, and news they think I'll engage with, and videos they think will enrage me enough to keep me hooked to their platforms. They are actively trying to flatten my personality into something easy to monetize.

    As most people I've seen say out loud, "Privacy as a concept is way beyond hiding secrets. A part of it also means preserving your capacity to change. To be surprised. To be inconsistent."

    If I could tell every human one thing, it would be to actively refuse to be a predictable data point. Mess up their metrics. In whatever way you are capable of.

    #socialmedia #algorithmicbias #privacy #dataprivacy #quotes #foss #facebook #linkedin #llm #noai #enshittification #reading #books #baking #art #philosophy #adhd #depression #cybersecurity

  27. A search engine alert for citations to my work highlighted this Master Thesis in Informatics from PennState University. It discusses the machine learning process building from my feature engineering textbook and then highlights racial issues within an algorithm used inside the US prisons.

    I hope I'll get time to read it:

    "AI, Blackness, and the Criminal Punishment System: How the STRONG-R carceral algorithm distorts Black narratives"

    etda.libraries.psu.edu/catalog

    #AlgorithmicBias #AIforBad

  28. A search engine alert for citations to my work highlighted this Master Thesis in Informatics from PennState University. It discusses the machine learning process building from my feature engineering textbook and then highlights racial issues within an algorithm used inside the US prisons.

    I hope I'll get time to read it:

    "AI, Blackness, and the Criminal Punishment System: How the STRONG-R carceral algorithm distorts Black narratives"

    etda.libraries.psu.edu/catalog

    #AlgorithmicBias #AIforBad

  29. New York just dropped a law requiring retailers to disclose if your personal data is influencing the price of staples like eggs.

    Spoiler: they only have to say *if*, not *how*. Because half-transparency is still opaque, right?

    How much would you pay for privacy in your grocery cart?
    #DataEthics #AlgorithmicBias #TechRegulation #Wired
    wired.com/story/algorithmic-pr

  30. 🔎 LinkedIn Audit: Suppressing Women?
    I'm running a live experiment on professional visibility. I temporarily removed the "she/her" pronoun label from my LinkedIn profile 3 days ago.

    Initial Result: My average post reach immediately jumped from ~3,000 to over 6,000 impressions. That's a 100% increase overnight.

    This sudden spike creates a strong hypothesis: the presence of gender labels was acting as a suppression factor, or their absence is creating an algorithmic boost for distribution.

    We must audit the unseen structures (algorithms) that govern visibility. Our goal is to ensure equitable reach for all voices.

    Tracking data for 10 more days. Have you seen similar shifts?

    Let's discuss fairness in digital distribution.

    #SystemsLeadership #AlgorithmicBias #TechInclusion

  31. Europe, prepare for the 'AI slop'! Meta is rolling out its short-form video feed, and apparently, media generation in their AI app has already jumped tenfold. What kind of algorithmically generated content do you think awaits us? 🤔
    techcrunch.com/2025/11/06/meta
    #AI #TechNews #Meta #AlgorithmicBias #FutureOfContent