https://www.bibsonomy.org/tag/algorithmsBibSonomy RSS Feed for /tag/algorithmshttps://www.adnews.com.au/opinion/please-explain-instagram-s-algorithms-and-unconscious-biasmartina352026-03-15T11:06:59+01:00algorithms bias instagram unconscious <a itemprop="url" data-versiondate="2026-03-15T11:06:59+01:00" href="https://www.adnews.com.au/opinion/please-explain-instagram-s-algorithms-and-unconscious-bias" rel="nofollow" class="description-link">https://www.adnews.com.au/opinion/please-explain-instagram-s-algorithms-and-unconscious-bias</a>Abstract page for arXiv paper 2508.09053: Behavioural Theory of Reflective Algorithms II: Reflective Parallel Algorithmshttps://arxiv.org/abs/2508.09053scch2026-01-19T08:07:25+01:00Algorithms Behavioural Reflective Theory <span itemprop="description">Abstract page for arXiv paper 2508.09053: Behavioural Theory of Reflective Algorithms II: Reflective Parallel Algorithms</span>This “journal-first” paper presents a summary of the behavioural theory of reflective sequential algorithms (RSAs), i.e. sequential algorithms that can modify their own behaviour. The theory comprises a set of language-independent postulates defining the...https://link.springer.com/chapter/10.1007/978-3-031-33163-3_18scch2025-07-07T08:09:43+02:00Algorithms Behavioural Reflective Theory <span itemprop="description">This “journal-first” paper presents a summary of the behavioural theory of reflective sequential algorithms (RSAs), i.e. sequential algorithms that can modify their own behaviour. The theory comprises a set of language-independent postulates defining the...</span>https://stars.library.ucf.edu/honorstheses/1086/nsurina2024-03-24T21:56:47+01:00algorithms hashtags tiktok <a itemprop="url" data-versiondate="2024-03-24T21:56:47+01:00" href="https://stars.library.ucf.edu/honorstheses/1086/" rel="nofollow" class="description-link">https://stars.library.ucf.edu/honorstheses/1086/</a>https://www.sciencedirect.com/science/article/pii/S0167642322000971scch2023-09-18T15:48:03+02:00algorithms behavioural reflective sequential theory <a itemprop="url" data-versiondate="2023-09-18T15:48:03+02:00" href="https://www.sciencedirect.com/science/article/pii/S0167642322000971" rel="nofollow" class="description-link">https://www.sciencedirect.com/science/article/pii/S0167642322000971</a>SoLAR Spotlight–Conversations on LearningAnalyticshttps://soundcloud.com/user-916492194/episode-18-explainable-models-for-learning-analyticsereidt2022-10-23T11:52:53+02:00SoLAR algorithms deployment education explainableAI learninganalytics modeldevelopment podcast <span itemprop="description">SoLAR Spotlight–Conversations on LearningAnalytics</span>Marius Wehner und Lynn Schmodde von der Wirtschaftswissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf berichten von ihrer Forschung zu Learning Analytics. Im Verbundprojekt LADi haben sie Diskriminierungspotenziale und Bias in den Algorithmen untersucht sowie die Wahrnehmung der Lernenden von Beurteilungen durch Learning Analytics. Interviewer in Folge 11 des DINItus Podcasts ist Erik Reidt vom ZIM/Multimediazentrum der HHU Düsseldorf.https://anchor.fm/dinitus/episodes/Learning-Analytics--Teil-1-e1kptkpereidt2022-07-24T11:19:49+02:00algorithms assessment bias edtech ethics evidence highered learninganalytics podcast research <span itemprop="description">Marius Wehner und Lynn Schmodde von der Wirtschaftswissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf berichten von ihrer Forschung zu Learning Analytics. Im Verbundprojekt LADi haben sie Diskriminierungspotenziale und Bias in den Algorithmen untersucht sowie die Wahrnehmung der Lernenden von Beurteilungen durch Learning Analytics. Interviewer in Folge 11 des DINItus Podcasts ist Erik Reidt vom ZIM/Multimediazentrum der HHU Düsseldorf.</span>This episode is all about bias. Our hosts Maren Scheffel and Nia Dowel talk to Shamya Karumbaiah and Rene Kizilcec about bias in learning analytics and some of the work they are doing in that area.https://soundcloud.com/user-916492194/episode-16-biasereidt2022-06-19T11:16:01+02:00SoLAR algorithms bias edtech education learninganalytics podcast support <span itemprop="description">This episode is all about bias. Our hosts Maren Scheffel and Nia Dowel talk to Shamya Karumbaiah and Rene Kizilcec about bias in learning analytics and some of the work they are doing in that area.</span>Philosophical Disquisitions Friday, April 8, 2022https://philosophicaldisquisitions.blogspot.com/2022/04/how-can-algorithms-be-biased.htmlereidt2022-04-10T12:42:39+02:00algorithms artificialintelligence bias fairness framework learninganalytics output validation <span itemprop="description">Philosophical Disquisitions Friday, April 8, 2022</span>Der Kurs vermittelt ein grundlegendes Verständnis für Machine Learning und den Umgang mit Algorithmen. Nach einem Einführungsteil auf der Basis inhaltlicher Wissensvermittlung, haben Sie intensiv die Möglichkeit, Kompetenzen durch forschendes Lernen und anhand realer Szenarien zu entwickeln.https://ki-campus.org/amaleaereidt2022-01-09T10:07:44+01:00MOOC algorithms artificialintelligence learninganalytics machinelearning practice <span itemprop="description">Der Kurs vermittelt ein grundlegendes Verständnis für Machine Learning und den Umgang mit Algorithmen. Nach einem Einführungsteil auf der Basis inhaltlicher Wissensvermittlung, haben Sie intensiv die Möglichkeit, Kompetenzen durch forschendes Lernen und anhand realer Szenarien zu entwickeln.</span>IWM-Lecture series - 7 Dec 2021https://www.slideshare.net/kverbert/towards-the-next-generation-of-interactive-and-adaptive-explanation-methods-250798434ereidt2021-12-12T11:43:29+01:00adaptiveinstruction algorithms evaluation explainableAI interactivity learninganalytics recommender slideshare <span itemprop="description">IWM-Lecture series - 7 Dec 2021</span>Jeroen Ooge, Gregor Stiglic, Katrien Verbert First published: 28 November 2021 https://doi.org/10.1002/widm.1427https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1427ereidt2021-12-05T12:50:51+01:00algorithms artificialintelligence decisionmaking healthcare learninganalytics paper research visualanalytics visualization <span itemprop="description">Jeroen Ooge, Gregor Stiglic, Katrien Verbert First published: 28 November 2021 https://doi.org/10.1002/widm.1427</span>Trade Unions are confronted with the need to go digital. We offer a tool and skills to responsibly collect data and build a data-powered campaign.https://www.fes.de/en/themenportal-gewerkschaften-und-gute-arbeit/international-trade-union-policy/articles-in-international-trade-union-policy/application-open-for-online-course-on-union-techmeneteqel2021-11-17T09:13:40+01:00UnionTexh algorithms union_organizing <span itemprop="description">Trade Unions are confronted with the need to go digital. We offer a tool and skills to responsibly collect data and build a data-powered campaign.</span>EdSurge | Jose Marichal on Nov 1, 2021https://www.edsurge.com/news/2021-11-01-teaching-students-to-make-good-choices-in-an-algorithm-driven-worldereidt2021-11-07T11:08:03+01:00algorithms artificialintelligence decisionmaking ethics highered learninganalytics machinelearning students <span itemprop="description">EdSurge | Jose Marichal on Nov 1, 2021</span>What if algorithms were used to tailor a more personalised course for each student? Alison Watson proposes a Netflix of Learning | 19/10/21https://wonkhe.com/blogs/how-netflix-and-skill-can-transform-higher-education/ereidt2021-10-24T12:41:24+02:00algorithms highered learninganalytics personalizedlearning skillsdevelopment transformation <span itemprop="description">What if algorithms were used to tailor a more personalised course for each student? Alison Watson proposes a Netflix of Learning | 19/10/21</span>Certain words are like sparks in a puddle of gasoline. “Bias” is definitely one of those words—and for good reason. If there is something that we are doing, that we are unaware of, that is causing harm to others, then we definitely should be taking it seriously.https://learningsolutionsmag.com/articles/find-and-mitigate-the-bias-lurking-in-your-learning-dataereidt2021-10-17T13:35:44+02:00algorithms bias data e-learning learninganalytics <span itemprop="description">Certain words are like sparks in a puddle of gasoline. “Bias” is definitely one of those words—and for good reason. If there is something that we are doing, that we are unaware of, that is causing harm to others, then we definitely should be taking it seriously.</span>Bias and variance are inversely connected and It is nearly impossible practically to have an ML model with a low bias and a low variance.https://analyticsindiamag.com/how-to-address-bias-variance-tradeoff-in-machine-learning/ereidt2021-09-26T21:54:44+02:00algorithms bias bias-variancetradeoff learninganalytics machinelearning model variance <span itemprop="description">Bias and variance are inversely connected and It is nearly impossible practically to have an ML model with a low bias and a low variance.</span>By Tom Fleischman | June 17, 2021 Cornell Chroniclehttps://news.cornell.edu/stories/2021/06/testing-ai-fairness-predicting-college-dropout-rateereidt2021-07-18T22:43:29+02:00algorithms artificialintelligence bias drop-out fairness highered learninganalytics predictivemodeling <span itemprop="description">By Tom Fleischman | June 17, 2021 Cornell Chronicle</span>Wie stark lassen sich Lehrende durch Learning Analytics in ihrer Bewertung von Studierenden beeinflussen? Welche diskriminierenden aber auch ungleichheits-reduzierenden Effekte gehen von Algorithmen aus? In diesem Beitrag stellen die Autor*innen das Potential und die Gefahren von Learning Analytics vor und werten die Forschungsergebnisse eines Conjoint-Experiments aus.https://hochschulforumdigitalisierung.de/de/blog/learning-analytics-diskriminierungereidt2021-05-23T10:57:11+02:00LMS algorithms attributes discrimination ethics grading highered inequality learninganalytics <span itemprop="description">Wie stark lassen sich Lehrende durch Learning Analytics in ihrer Bewertung von Studierenden beeinflussen? Welche diskriminierenden aber auch ungleichheits-reduzierenden Effekte gehen von Algorithmen aus? In diesem Beitrag stellen die Autor*innen das Potential und die Gefahren von Learning Analytics vor und werten die Forschungsergebnisse eines Conjoint-Experiments aus.</span>Los 'riders' dejan de ser falsos autónomos: las claves de la ley pactada por Trabajo, patronal y sindicatoshttps://www.msn.com/es-es/dinero/formacion-empleo/los-riders-dejan-de-ser-falsos-aut-c3-b3nomos-las-claves-de-la-ley-pactada-por-trabajo-patronal-y-sindicatos/ar-BB1etEAumeneteqel2021-03-11T20:33:46+01:00España algorithmic_management algorithms algoritmos derechos_laborales labour_rights plataformas_digitales platform_work <span itemprop="description">Los &#039;riders&#039; dejan de ser falsos autónomos: las claves de la ley pactada por Trabajo, patronal y sindicatos</span>