Generated by All in One SEO v4.9.10, this is an llms.txt file, used by LLMs to index the site. # Mammoth Multi-Attribute, Multimodal Bias Mitigation in AI Systems ## Sitemaps - [XML Sitemap](https://mammoth-ai.eu/sitemap.xml): Contains all public & indexable URLs for this website. ## Posts - [The AI Creator's AI Fairness Definition Guide](https://mammoth-ai.eu/the-ai-creators-ai-fairness-definition-guide/) - [AI bias: overview, measurement, mitigation and application to computer vision](https://mammoth-ai.eu/ai-bias-overview-measurement-mitigation-and-application-to-computer-vision/) - AI bias: overview, measurement, mitigation and application to computer vision - [Fairness-Aware ML Tutorial Series](https://mammoth-ai.eu/fairness-aware-ml-tutorial-series-2/) - Fairness-Aware ML Tutorial Series - [#6 Behind the Algorithm](https://mammoth-ai.eu/6-behind-the-algorithm/) - [#5 Rethinking AI](https://mammoth-ai.eu/5-rethinking-ai/) - [D5.4 Final DCE and Policy Report](https://mammoth-ai.eu/d5-4-final-dce-and-policy-report/) - Pending approval from the EC. - [D4.2 Stakeholder Evaluation of System, Processes and Results](https://mammoth-ai.eu/d4-2-stakeholder-evaluation-of-system-processes-and-results/) - Pending approval from the EC. - [D4.1 Evaluation and Mitigation of Bias in the Use Cases](https://mammoth-ai.eu/d4-1-evaluation-and-mitigation-of-bias-in-the-use-cases/) - Pending approval from the EC. - [Final Press Release](https://mammoth-ai.eu/final-press-release/) - The Horizon Europe-funded project MAMMOth (Multi-Attribute, Multimodal Bias Mitigation in AI Systems) officially concluded on 31 October 2025, after three years of pioneering work to make artificial intelligence (AI) fairer, more inclusive, and more accountable. Coordinated by the Centre for Research and Technology Hellas (CERTH), MAMMOth brought together a consortium of leading universities, research centres, - [Protected attribute inventory for additional domains](https://mammoth-ai.eu/protected-attribute-inventory-for-additional-domains/) - Excited to announce the release of the training materials from the MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systems" project by the University of Groningen! - [D2.3 Explainable Methods for Discovering Bias in Multimodal Data](https://mammoth-ai.eu/d2-3-explainable-methods-for-discovering-bias-in-multimodal-data/) - Pending approval from the EC. - [D5.3 MAMMOth's Handbook: Best practices](https://mammoth-ai.eu/d5-3-mammoths-handbook-best-practices/) - Pending approval from the EC. - [D3.3 Methods and Algorithms for Multidimensional Fairness in Multi-attribute Networks](https://mammoth-ai.eu/d3-3-methods-and-algorithms-for-multidimensional-fairness-in-multi-attribute-networks/) - Pending approval from the EC. - [D3.2 Methods and Algorithms for Multidimensional Fairness-aware Classification](https://mammoth-ai.eu/d3-2-methods-and-algorithms-for-multidimensional-fairness-aware-classification/) - Pending approval from the EC. - [D1.2 MAMMOth Βias Τoolkit](https://mammoth-ai.eu/d1-2-mammoth-βias-τoolkit/) - Pending approval from the EC. - [MAMMOth Technical Stakeholders Brief](https://mammoth-ai.eu/mammoth-technical-stakeholders-brief/) - [Fairness-Aware ML Tutorial Series](https://mammoth-ai.eu/fairness-aware-ml-tutorial-series/) - The tutorial series offers a hands-on guide to fairness-aware machine learning for beginners in Fair-ML. It emphasises the urgency for equitable, transparent, and accountable AI/ML systems given their influence on important decisions. It is designed to empower data scientists, researchers, and developers to create more equitable and trustworthy AI systems... - [Measuring Algorithmic Fairness](https://mammoth-ai.eu/measuring-algorithmic-fairness/) - In this tutorial, we explore how algorithmic fairness can be quantified and explored, and what one may need to pay attention to. We do this through the FairBench library, which is tailored to practical use in many pipelines (regardless of whether they focus on numpy, pandas, torch,tf, etc.). Fairbench serves as a comprehensive AI fairness exploration framework, offering tools to... - [Visual Bias Mitigator (VB-Mitigator)](https://mammoth-ai.eu/visual-bias-mitigator-vb-mitigator/) - The Visual Bias Mitigator is an open-source framework designed to empower researchers in the field of bias mitigation in computer vision. This codebase provides a comprehensive environment where users can easily implement, run, and evaluate existing visual bias mitigation methods. - [5th Webinar on “Tackling feature bias in counterfactual explanations for mixed tabular data"](https://mammoth-ai.eu/5th-webinar-on-tackling-feature-bias-in-counterfactual-explanations-for-mixed-tabular-data/) - Tackling feature bias in counterfactual explanations for mixed tabular data - [4th Webinar on “Achieving Socio-Economic Parity through the Lens of EU AI Act”](https://mammoth-ai.eu/4th-webinar-on-achieving-socio-economic-parity-through-the-lens-of-eu-ai-act/) - Achieving Socio-Economic Parity through the Lens of EU AI Act - [3rd Webinar on “Critical AI literacy: Implications for education & public engagement with science”](https://mammoth-ai.eu/3rd-webinar-on-critical-ai-literacy-implications-for-education-public-engagement-with-science/) - Critical AI literacy: Implications for education & public engagement with science - [2nd MAMMOth webinar](https://mammoth-ai.eu/2nd-mammoth-webinar/) - Addressing Visual Bias - [1st MAMMOth webinar](https://mammoth-ai.eu/1st-mammoth-webinar/) - Training in Co-Creation as a Methodological Approach to Improve AI Fairness - [#4 Κnowing & Not Knowing](https://mammoth-ai.eu/podcast-4/) - [#3 Mind over Machine](https://mammoth-ai.eu/podcast-3/) - [#2 Βeyond AI Bias](https://mammoth-ai.eu/podcast-2/) - [#1 Can we trust AI?](https://mammoth-ai.eu/podcast-1/) - [February Newsletter](https://mammoth-ai.eu/newsletterv1/) - With our very first newsletter, we are introducing our new MAMMOth website! We are Live!!! This has been in the works for weeks and the launch date is finally here! We welcome visitors with featured content focused on multi-discrimination mitigation for tabular networks and multi-modal data in AI systems. For more updated content, please visit - [Algorithmen lernen zu diskriminieren](https://mammoth-ai.eu/algorithmen-lernen-zu-diskriminieren-copy/) - Ob bei der Einschätzung von medizinischen Notfällen oder der Reihung von Jobinteressenten – KI-Systeme schreiben den Rassismus und Sexismus unserer Gesellschaft fort. Ein automatischer Seifenspender, der schwarze Menschen diskriminiert, weil seine auf Nahinfrarot basierenden Sensoren dunklere Hautfarben nicht erkennt, ist lästig. Eine kostspielige Pulsuhr, deren optische Herzfrequenzmessung nur bei hellen Hauttönen einwandfrei funktioniert, ein Ärgernis. - [Ethics: What do we want AI (not) to do?](https://mammoth-ai.eu/ethics-what-do-we-want-ai-not-to-do/) - Dagmar Heeg & Dali Fekete - Centre for Learning and Teaching i.s.m. MAMMOth Workshop: AI & Ethics: What do we want AI (not) to do? Don't miss our in-depth workshop on artificial intelligence (AI) and ethics. We kick off with an introduction to AI, bias and ethics, followed by an interactive exploration of AI in - [Open Day](https://mammoth-ai.eu/open-day/) - An overview of the Mammoth project as well as a summary of some WP3 tasks. This poster was presented at an open-day event organised by UniBw. - [May Newsletter](https://mammoth-ai.eu/mammoth-newsletter/) - The official newsletter of MAMMOth Project [MAY2023] Call for Papers is out now! Fairness in Machine Learning continues to be a growing area of research and is perhaps now more relevant than ever, as new AI-powered applications like ChatGPT and MidJourney are being widely used by the public, and legal regulations of AI/ML (e.g., the - [3rd Workshop on Bias and Fairness in AI](https://mammoth-ai.eu/3rd-workshop-on-bias-and-fairness-in-ai/) - 3rd Workshop on Bias and Fairness in AI Workshop at ECML PKDD 2023, 22nd of September,Torino (Italy) Call for Papers is out now! Fairness in Machine Learning continues to be a growing area of research and is perhaps now more relevant than ever, as new AI-powered applications like ChatGPT and MidJourney are being widely used by the - [Ethics guidelines for trustworthy AI](https://mammoth-ai.eu/ethics-guidelines-for-trustworthy-ai/) - On 8 April 2019, the High-Level Expert Group on AI presented Ethics Guidelines for Trustworthy Artificial Intelligence. This followed the publication of the guidelines' first draft in December 2018 on which more than 500 comments were received through an open consultation. For more information, please visit Ethics guidelines for trustworthy AI - [AI Assistants Get More Credit Than Humans](https://mammoth-ai.eu/ai-assistants-get-more-credit-than-humans/) - AI Assistants Get More Credit Than Humans AI is all around us, and ethical conversations are inevitable now that systems can make, or at least help us make moral decisions... - [2023 European Researchers’ Night](https://mammoth-ai.eu/2023-european-researchers-night-2/) - The European Researchers’ Night is a Europe-wide public event that displays the diversity of science and its impact on citizens’ daily lives in fun, inspiring ways... - [On the way to an unbiased AI-powered face verification](https://mammoth-ai.eu/on-the-way-to-an-unbiased-ai-powered-face-verification/) - IDnow is a leading identity verification platform provider in Europe with a vision to make the connected world a safer place. At IDnow, innovation means more than just creating something new or novel... - [AI Fairness Cluster Inaugural Conference](https://mammoth-ai.eu/ai-fairness-cluster-inaugural-conference/) - Four Horizon Europe projects researching on methods to prevent, detect, and mitigate discriminatory risks in AI have recently formed the AI Fairness Cluster: AEQUITAS, BIAS, FINDHR, and MAMMOth. Together, they form a network comprising over 50 institutions across Europe including universities, research centers, non-governmental organizations, large industry players, and SMEs... - [January Newsletter](https://mammoth-ai.eu/january-24-newsletter/) - Welcome to the first official newsletter for the year 2024! We’re excited to bring you the latest updates and highlights from the MAMMOth Project, including the remarkable strides we’ve taken in our pursuit of excellence, particularly through the EU-funded project MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systems... - [AI Fairness Definition Guide](https://mammoth-ai.eu/ai-fairness-definition-guide/) - In the context of the Horizon Europe MAMMOth project, we developed the “AI Fairness Definition Guide” to help those creating AI (such as researchers, developers, and product owners) understand how to define fairness in the social context of their created systems by working with stakeholders and experts from other disciplines... - [Project presentation by Akis Papadopoulos at the AI Fairness Cluster Inaugural Conference on 19th March 2024, Amsterdam](https://mammoth-ai.eu/project-presentation-by-akis-papadopoulos-at-the-ai-fairness-cluster-inaugural-conference-on-19th-march-2024-amsterdam/) - Project presentation by Akis Papadopoulos at the AI Fairness Cluster Inaugural Conference on 19th March 2024, Amsterdam - [Training Materials](https://mammoth-ai.eu/training-materials/) - Excited to announce the release of the training materials from the MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systems" project by the University of Groningen! - [Financial Case](https://mammoth-ai.eu/financial-case/) - EXUS specializes and focuses on debt collections and recovery technologies. Through its flagship product, EXUS Financial Suite (EFS), EXUS helps financial institutions and utility companies manage credit risk along the whole lifecycle of accounts... - [European Researchers' Night 2023](https://mammoth-ai.eu/european-researchers-night-2023/) - MAMMOth represents with two workshops on September 29 in the Netherlands. At European Research Night attendants can learn about the critical use of ChatGPT, starting from 21:00 at Forum Groningen. - [July Newsletter '26](https://mammoth-ai.eu/july-newsletter-26/) - Welcome to the first official newsletter for the year 2024! We’re excited to bring you the latest updates and highlights from the MAMMOth Project, including the remarkable strides we’ve taken in our pursuit of excellence, particularly through the EU-funded project MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systems... - [Workshop: Critical use of ChatGPT](https://mammoth-ai.eu/workshop-critical-use-of-chatgpt/) - Dagmar Heeg & Dali Fekete - Centre for Learning and Teaching i.s.m. MAMMOth Workshop: Critical use of ChatGPTProgramma soort&SocietyGenreWorkshop Join our dynamic workshop on ChatGPT! We will start a dialogue about prejudice, from a Western perspective to gender biases in language. We learn how women's voices are less represented in AI and highlight other ethical - [BIAS 2023](https://mammoth-ai.eu/bias-2023/) - ?Get ready for an action-packed week because we're about to witness some truly MAMMOth contributions in the world of AI and fairness. https://shorturl.at/uHPX9 - [Infographics by RUG](https://mammoth-ai.eu/infographics-by-rug/) - AI Biases in Face Verification AI Biases in Finance AI Biases in Academic Citations AI Ethics & Biases - [AI Biases Can You Find Them](https://mammoth-ai.eu/ai-biases-can-you-find-them/) - Training Material For MAMMOth created by the RUG AI Biases: Can you find them? Purpose: supporting AI literacy developmentAudience: general public (14 years old and up)Medium: exhibit with printed postersDuration: ~10 minutesSupport level: individually explorableMade by: Dali Fekete (d.fekete@rug.nl) Short Description: This material was made for the Dutch AI Coalition’s (nlaic.com) nationwide AI event series, - [Infographics](https://mammoth-ai.eu/infographics-2/) - These infographics explore the three use cases of MAMMOth: finance, face verification, and academic citations. They require no supervision, making it possible to be displayed at a variety of locations. The face verification infographic is the most approachable (possibly even to schoolchildren), while the one exploring the academic use case is more challenging, making it also suitable for dissemination across research groups. - [Intro to AI Biases](https://mammoth-ai.eu/intro-to-ai-biases-2/) - This brief presentation can be used as an introductory lecture to AI ethics classes, science cafes, workshops, or masterclasses. It explores AI biases from a naive, layperson perspective. It tackles misconceptions about AI, and provides explanations on how algorithms work. - [Traveling exhibit](https://mammoth-ai.eu/traveling-exhibit/) - Digital Dilemmas: AI and the Invisible Biases’ is a traveling exhibit, designed to raise awareness, inform individuals about the pervasive nature of AI (bias) and invite people to share their opinion on the matter. By showcasing real-world examples of AI applications and related errors, it provides an introduction to AI functions, including its inherent biases. The exhibition features engaging elements designed to encourage active participation and provoke thoughtful discussions: a series of posters and a mini-podcast educate the public, while a physical neural net and a bulletin board give space for interactivity, reflection and discussion. - [What Do We Want AI Not To Do](https://mammoth-ai.eu/what-do-we-want-ai-not-to-do/) - Training Material For MAMMOth created by the RUG What Do We Want AI (Not) To Do? Purpose: supporting AI literacy developmentAudience: general public (14 years old and up)Medium: workshopDuration: 60-90 minutesSupport level: according to group sizeMade by: Dali Fekete (d.fekete@rug.nl),Dagmar Heeg (d.m.heeg@rug.nl) Short Description: The workshop focuses on AI ethics across 3 different use cases: - [AI Biases with Ari](https://mammoth-ai.eu/ai-biases-with-ari/) - Targeting a population that is often neglected in AI ethics dissemination, the RUG has created an exhibition aimed at children. A combination of interactive posters, comics, and decoder glasses provide an interactive, easy-to-understand, and most importantly, age-appropriate introduction to AI and AI biases. - [Ethical Dilemmas Around AI Use](https://mammoth-ai.eu/ethical-dilemmas-around-ai-use/) - [AI and Ethics](https://mammoth-ai.eu/ai-and-ethics/) - [Adapting Course Design and Assessment](https://mammoth-ai.eu/adapting-course-design-and-assessment/) - [Academic integrity & Critical use of AI](https://mammoth-ai.eu/academic-integrity-critical-use-of-ai/) - [MAMMOth Policy Brief](https://mammoth-ai.eu/mammoth-policy-brief/) - [MAMMOth Civil Society Brief](https://mammoth-ai.eu/mammoth-civil-society-brief/) - [Periodic Technical and ScientificReport](https://mammoth-ai.eu/periodic-technical-and-scientificreport/) - [D6.1 Data Management Plan](https://mammoth-ai.eu/d6-1-data-management-plan/) - [D3.1 Multi-dimensional Discrimination Definitions and Operational Measures](https://mammoth-ai.eu/d3-1-multi-dimensionaldiscrimination-definitions-andoperational-measures/) - Pending approval from the EC. - [D2.2 Methods and Algorithms for Bias Analysis in Multi-attribute Networks](https://mammoth-ai.eu/d2-2-methods-and-algorithms-for-bias-analysis-in-multi-attribute-networks/) - Pending approval from the EC. - [D2.1 Methods and Algorithms for Bias-aware Multimodal attribute Extraction](https://mammoth-ai.eu/d2-1-methods-and-algorithms-for-bias-aware-multimodal-attribute-extraction/) - Pending approval from the EC. - [D1.1 User Requirements and Architecture](https://mammoth-ai.eu/d1-1-user-requirements-andarchitecture/) - [Intro to AI Biases](https://mammoth-ai.eu/intro-to-ai-biases/) - Training Material For MAMMOth created by the RUG Intro to AI and AI Biases Purpose: educating academics on AI and its limitations, supporting AIliteracy developmentAudience: research communities, general publicMedium: workshop with slidesDuration: ~20 minutesSupport level: according to group sizeMade by: Dali Fekete (d.fekete@rug.nl), Dagmar Heeg (d.m.heeg@rug.nl) Short Description: This brief presentation can be used as - [Intro to UCs](https://mammoth-ai.eu/intro-to-ucs/) - Training Material For MAMMOth created by the RUG AI Biases in MAMMOth UCs Purpose: educating academics on AI and its limitationsAudience: research communitiesMedium: workshop with slidesDuration: ~40 minutesSupport level: according to group sizeMade by: Dali Fekete (d.fekete@rug.nl), Dagmar Heeg (d.m.heeg@rug.nl), Marta Gibin (University of Bologna) Short Description: This co-created presentation can be used to educate - [MOOC](https://mammoth-ai.eu/mooc/) - Training Material For MAMMOth created by the RUG MOOCs: MAMMOth UCs Purpose: educating academics on AI and its limitationsAudience: research communities, working professionalsMedium: massive open online courseDuration: variableSupport level: individually explorableMade by: Dali Fekete (d.fekete@rug.nl)Short Description: These materials explore the three use cases of MAMMOth: finance, face verification, and academic citations. The lessons can either - [Infographics](https://mammoth-ai.eu/infographics/) - These infographics explore the three use cases of MAMMOth: finance, face verification, and academic citations. They require no supervision, making it possible to be displayed at a variety of locations. The face verification infographic is the most approachable (possibly even to schoolchildren), while the one exploring the academic use case is more challenging, making it also suitable for dissemination across research groups. ## Pages - [Home](https://mammoth-ai.eu/) - Welcome to the official website of the EU-funded project MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systems! This Horizon Europe Research and Innovation Action project aims to promote diversity and inclusion in the design, development and deployment of Artificial Intelligence systems, by both building the capacity of relevant stakeholders and by providing bias-preventing AI solutions into - [Publications](https://mammoth-ai.eu/results/publications/) - Results Publications Publications - [Deliverables](https://mammoth-ai.eu/results/deliverables/) - Results Deliverables D1.1 User Requirements and Architecture View File D1.2 MAMMOth Βias Τoolkit View File D2.1 Methods and Algorithms for Bias-aware Multimodal attribute Extraction View File D2.2 Methods and Algorithms for Bias Analysis in Multi-attribute Networks View File D2.3 Explainable Methods for Discovering Bias in Multimodal Data View File D3.1 Multi-dimensional Discrimination Definitions and Operational - [Project recommendations](https://mammoth-ai.eu/results/project-recommendations/) - Results Project recommendations MAMMOth Civil Society Brief View File MAMMOth Policy Brief View File MAMMOth Technical Stakeholders Brief View File - [Partners](https://mammoth-ai.eu/partners/) - Partners CERTH Coordinator – Greece The Centre for Research and Technology-Hellas (CERTH) was established in 2000 as a non-profit research centre. It is headquartered in Thessaloniki and is one of the largest Greek research centres and among the Top-10 EU’s Research Centres in attracting research grants. MAMMOth is coordinated by CERTH’s Information Technologies Institute and - [Training](https://mammoth-ai.eu/training/) - Training Material The training material, displayed in this website, is made freely available to users under the terms of the Creative Commons Attribution 4.0. Academics AI Scientists Children (5-12) Deliverables General Public News Podcasts Policy recommendations Tutorials Webinars AI bias: overview, measurement, mitigation and application to computer vision READ MORE Fairness-Aware ML Tutorial Series READ - [Tutorials](https://mammoth-ai.eu/training/tutorials/) - Tutorials Visual Bias Mitigator (VB-Mitigator) The Visual Bias Mitigator is an open-source framework designed to empower researchers in the field of bias mitigation in computer vision. This codebase provides a comprehensive environment where users can easily implement, run, and evaluate existing visual bias mitigation methods. Read More August 4, 2025 Measuring Algorithmic Fairness In this - [Podcasts](https://mammoth-ai.eu/podcasts/) - Podcasts Follow our conversations on MAMMOth #6 Behind the Algorithm Listen on Spotify January 13, 2026 #5 Rethinking AI Listen on Spotify January 12, 2026 #4 Κnowing & Not Knowing Listen on Spotify June 24, 2025 #3 Mind over Machine Listen on Spotify May 2, 2025 #2 Βeyond AI Bias Listen on Spotify April 8, - [Presentations](https://mammoth-ai.eu/results/presentations/) - Results Presentations Demo Day: "Ethical and Responsible AI Platforms", Copenhagen Fintech Lab and online View File Presentation on BIAS workshop View File Publications - [Webinars](https://mammoth-ai.eu/webinars/) - Webinars 5th Webinar on “Tackling feature bias in counterfactual explanations for mixed tabular data” Tackling feature bias in counterfactual explanations for mixed tabular data Read More July 15, 2025 4th Webinar on “Achieving Socio-Economic Parity through the Lens of EU AI Act” Achieving Socio-Economic Parity through the Lens of EU AI Act Read More July - [Bias mitigation software](https://mammoth-ai.eu/software/bias-mitigation-software/) - Bias mitigation software Uncover and mitigate bias in AI and data-driven systems with our curated selection of research tools and libraries. This page provides direct access to cutting-edge software designed to identify, analyze, and reduce bias in machine learning models, datasets, and decision-making processes. Fairness-aware ML Tutorials Jupyter Notebook This tutorial series offers a hands-on - [Blog](https://mammoth-ai.eu/blog/) - Blog Our blog News Final Press Release The Horizon Europe-funded project MAMMOth (Multi-Attribute, Multimodal Bias Mitigation in AI Systems) officially concluded on 31 October 2025, after three years of pioneering work to make artificial intelligence (AI) fairer, more inclusive, and more accountable. Coordinated by the Centre for Research and Technology Hellas (CERTH), MAMMOth brought together - [Training material Old](https://mammoth-ai.eu/training-material/) - Training material Old Training materialWhat are these training materials?These training materials have been created within the framework of the project “MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systems” (MAMMOth) project by the University of Groningen with the goal of raising awareness about AI bias and supporting related knowledge construction. However, the training activities and resources - [Privacy Policy](https://mammoth-ai.eu/privacy-policy/) - Privacy Policy Data Privacy Policy for the MAMMOth website IntroductionThank you for visiting the MAMMOth website.This privacy policy concerns processing of personal data with the MAMMOth project due to the operation of the website. This covers personal data that you provide us with through the website, and the personal data that you see on our - [M3Fair](https://mammoth-ai.eu/software/m3fair/) - M3Fair UI for fairness investigation.Investigate the fairness of AI models and search for prospective fixes through a UI that integrates many project and third-party results. Software Website - [FairBench](https://mammoth-ai.eu/software/fairbench/) - FairBench Library for thorough fairness assessment.Construct many fairness evaluation measures from a wide range of building blocks to gain a broad perspective in multi-attribute settings. Software Website - [Software](https://mammoth-ai.eu/software/) - [Expert Advisory Board](https://mammoth-ai.eu/partners-copy/) - Expert Advisory Board The activities of the project will be followed and evaluated by an Expert Advisory Board with experts in a variety of areas that are of importance for the project. This page provides an overview of MAMMOth Expert Advisory Board members in alphabetic order. Samuel C. Hoffman Mr. Hoffman received a B.S. degree - [MAMMOth](https://mammoth-ai.eu/project/) - Project MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systemsis a 36 – month (November 2022 – October 2025) project co-funded by the Horizon Europe Programme of European Union under the call Tackling gender, race and other biases in AI – HORIZON-CL4-2021-HUMAN-01-24 (RIA). Objectives Redefine bias based on multiple (protected) characteristics instead of a single attribute. - [For developers](https://mammoth-ai.eu/training-material/for-developers/) - For developers Training materialWhat are these training materials?These training materials have been created within the framework of the project “MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systems” (MAMMOth) project by the University of Groningen with the goal of raising awareness about AI bias and supporting related knowledge construction. However, the training activities and resources can - [For the general public](https://mammoth-ai.eu/training-material/for-the-general-public/) - For the general public Training materialWhat are these training materials?These training materials have been created within the framework of the project “MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systems” (MAMMOth) project by the University of Groningen with the goal of raising awareness about AI bias and supporting related knowledge construction. However, the training activities and - [For children](https://mammoth-ai.eu/training-material/for-children/) - For children Training materialWhat are these training materials?These training materials have been created within the framework of the project “MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systems” (MAMMOth) project by the University of Groningen with the goal of raising awareness about AI bias and supporting related knowledge construction. However, the training activities and resources can - [For academics](https://mammoth-ai.eu/training-material/for-academics/) - For academics Training materialWhat are these training materials?These training materials have been created within the framework of the project “MAMMOth Multi-Attribute, Multimodal Bias Mitigation in AI Systems” (MAMMOth) project by the University of Groningen with the goal of raising awareness about AI bias and supporting related knowledge construction. However, the training activities and resources can - [Results](https://mammoth-ai.eu/results/) - Results Presentations Demo Day: "Ethical and Responsible AI Platforms", Copenhagen Fintech Lab and online View File Presentation on BIAS workshop View File Publications TitleAuthorsDate of publicationPublisherLink to publicationMulti-dimensional discrimination in Law and Machine Learning - A comparative overviewArjun Roy, Jan Horstmann, Eirini Ntoutsi12.06.2023ACM FAccThttps://doi.org/10.1145/3593013.3593979The Use of AI in school science: a Systematic Literature ReviewDagmar Mercedes - [Multi-dimensional Discrimination Definitions and Operational Measures](https://mammoth-ai.eu/results/deliverables/multi-dimensional-discrimination-definitions-and-operational-measures/) - Results Multi-dimensional Discrimination Definitions and Operational Measures Multi-dimensional Discrimination Definitions and Operational Measures Pending approval from the EC. Publications - [Periodic Technical and Scientific Report](https://mammoth-ai.eu/results/deliverables/periodic-technical-and-scientific-report/) - Results Periodic Technical and Scientific Report Periodic Technical and Scientific Report Publications - [User Requirements and Architecture](https://mammoth-ai.eu/results/deliverables/user-requirements-and-architecture/) - Results Data Management Plan D1.1 User Requirements and Architecture Pending approval from the EC Publications - [Data Management Plan](https://mammoth-ai.eu/results/deliverables/data-management-plan/) - Results Data Management Plan D6.1 Data Management Plan Pending approval from the EC Publications - [Disclaimer](https://mammoth-ai.eu/disclaimer/) - Disclaimer Disclaimer statementAccording to Article 17 of the Grant Agreement: Any communication or dissemination activity related to the action must indicate the following disclaimer (translated into local languages where appropriate):“Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union - [News](https://mammoth-ai.eu/news/) - News Take the time to read the latest news about our project! - [Contact us](https://mammoth-ai.eu/contact-us/) - Contact us Let's connect! 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