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  M. Fuckner (Marcio)

Docent Onderzoeker
Telefoon:
0621155201
Bij geen gehoor:
0621155201
Email:
m.fuckner@hva.nl
Werkadres:
Theo Thijssenhuis, Wibautstraat 2-4 1091 GM Amsterdam
, Wibauthuis, Wibautstraat 3b, 1091 GH Amsterdam
Postadres:
Postbus 2728,1000 CS Amsterdam
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Marcio Fuckner is senior onderzoeker bij het lectoraat Responsible IT van de Hogeschool van Amsterdam. Hij doet onderzoek naar AI met verschillende onderzoeks- en onderwijspartners, overheidsdiensten en mediaorganisaties. Marcio is ook docent aan de Master of Applied AI.

Opleiding

Marcio behaalde zijn bachelor in Software Engineering aan de Pontifical Catholic University van Parana en zijn master in Computerwetenschappen aan dezelfde Braziliaanse instelling. Hij behaalde een PhD in Computerwetenschappen aan de Sorbonne University Alliance UTC in Frankrijk.

Onderzoek

Zijn onderzoek richt zich op ontwikkelen van methodologieën om AI-systemen verantwoord te bouwen. Enkele voorbeelden: in spraakherkenningssystemen deed hij onderzoek naar het kwantificeren van bias voor verschillende groepen. Hij heeft een simulatie gemaakt voor aanbevelingssystemen om de effecten van echokamers en filterbubbels te analyseren.

  • Workshop on Responsible Applied Artificial InTelligence
    van Dierendonck, R., Fuckner, M., Harbers, M., Horsman, S., van der Horst, T., Kok, F., Leijnen, S., Peters, M., Robben, S. M. B., & Wiggers, P. (2024). Workshop on Responsible Applied Artificial InTelligence. In Proceedings of the Workshops at the Third International Conference on Hybrid Human-Artificial Intelligence co-located with (HHAI 2024) (Vol. 3825, pp. 180). CEUR-WS.
  • Improving Book Search with AI
    Bašić, S., Fuckner, M., & Wiggers, P. (2024). Improving Book Search with AI. Poster session presented at ICT Open 2024, Utrecht, Netherlands.
  • Uncovering bias in ASR systems
    Fuckner, M., Horsman, S., Janssen, I., & Wiggers, P. (2024). Uncovering bias in ASR systems: Evaluating the performance of Wav2vec2 and Whisper for Dutch speakers. Poster session presented at 2nd Dutch Speech Tech Day , Hilversum, Netherlands.
  • Integration of Large Language Models in the Public Sector
    de Bruin, M., Fuckner, M., & Nait Aicha, A. (2024). Integration of Large Language Models in the Public Sector. Poster session presented at BNAIC/BeNeLearn 2024, Utrecht, Netherlands.
  • DRAMA – Op weg naar inclusieve spraakherkenning
    Horsman, S., Fuckner, M., & Wiggers, P. (2023). DRAMA – Op weg naar inclusieve spraakherkenning. Web publication or website, RAAIT. https://raait.nl/kennisbank/inclusieve-spraakherkenning/
  • Explainable Misinformation Detection from Text: A Critical Look
    Bašić, S., Fuckner, M., & Wiggers, P. (2023). Explainable Misinformation Detection from Text: A Critical Look. In T. Calders, C. Vens, J. Lijffijt, & B. Goethals (Eds.), Artificial Intelligence and Machine Learning: 34th Joint Benelux Conference, BNAIC/Benelearn 2022, Mechelen, Belgium, November 7–9, 2022, Revised Selected Papers (pp. 1-15). (Communications in Computer and Information Science; Vol. 1805). Springer. https://doi.org/10.1007/978-3-031-39144-6_1
  • From Novice to Composer
    Slingerland, E., & Fuckner, M. (2023). From Novice to Composer: Using AI to Facilitate Music Creation with MIDI Generation and Sample Extraction. Paper presented at BNAIC/BeNeLearn 2023, Delft, Netherlands.
  • Uncovering Bias in ASR Systems
    Fuckner, M., Horsman, S., Wiggers, P., & Janssen, I. (2023). Uncovering Bias in ASR Systems: Evaluating Wav2vec2 and Whisper for Dutch speakers. Paper presented at 2023 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), Bucharest, Romania. https://ieeexplore.ieee.org/document/10314895
  • Explainable misinformation detection from text
    Bašić, S., Fuckner, M., & Wiggers, P. (2022). Explainable misinformation detection from text: a critical look. Poster session presented at BNAIC/BeNeLearn 2022, Mechelen, Belgium.
  • The Bubble Machine: An agent-based modelling tool to analyse the interplay between cognitive preferences, social network interactions and algorithmic curation
    Fuckner, M., Bašić, S., Wiggers, P., Westplat, Y., van Kersbergen, R., & Firouzifard, I. (2022). The Bubble Machine: An agent-based modelling tool to analyse the interplay between cognitive preferences, social network interactions and algorithmic curation. 1-3. Abstract from BNAIC/BeNeLearn 2022, Mechelen, Belgium. https://bnaic2022.uantwerpen.be/wp-content/uploads/BNAICBeNeLearn_2022_submission_1650.pdf
  • Algorithm curation and the emergence of filter bubbles
    Fuckner, M., Bašić, S., & Wiggers, P. (2022). Algorithm curation and the emergence of filter bubbles: an agent-based modelling approach. Poster session presented at AI, media & democracy lab, opening event, Amsterdam.
  • Algorithm curation and the emergence of filter bubbles
    Fuckner, M., Bašić, S., & Wiggers, P. (2022). Algorithm curation and the emergence of filter bubbles: an ABM approach. Abstract from ICT.OPEN 2022, Amsterdam, Netherlands.
  • Exploring Bias in Data and Models for Misinformation Detection from Text
    Bašić, S., Fuckner, M., & Wiggers, P. (2022). Exploring Bias in Data and Models for Misinformation Detection from Text. Abstract from ICT.OPEN 2022, Amsterdam, Netherlands.
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