Schelenz, Laura (2024): Rassismus durch Künstliche Intelligenz? Wie Schwarzfeministische Ansätze diskriminierende KI beleuchten und kritisieren. In: Code & Vorurteil: Über Künstliche Intelligenz, Rassismus und Antisemitismus. Verbrecher Verlag Berlin.
Schelenz, Laura; Segal, Avi; Adelio, Oduma; Gal, Kobi (2023): Transparency-Check: An Instrument for the Study and Design of Transparency in AI-based Personalization Systems. In: ACM Journal on Responsible Computing. https://doi.org/10.1145/3636508
Schelenz, Laura (2023): Technology, Power, and Social Inclusion: Afghan Refugee
Women’s Interaction with ICT in Germany. The International Journal of Information, Diversity, & Inclusion 7(3): 2-31. https://doi.org/10.33137/ijidi.v7i3/4.40292
Schelenz, Laura (2023): Diversity and Social Justice in Technology Design: Reflections on Diversity-Aware Technology. International Journal of Critical Diversity Studies 5(2): 33-53, https://doi.org/10.13169/intecritdivestud.5.2.0033
Schelenz, Laura (2023): Digitale Diversitätsnarrative entzaubern. Strukturelle Ungleichheit bekämpfen. Im Rahmen des Projektes Digitales Deutschland. Online verfügbar: https://digid.jff.de/magazin/diversitaet/diversitaetsnarrative/
Helm, Paula; Loizos, Michael, Schelenz, Laura (2022): Diversity by Design? Balancing the Inclusion and Protection of Users in an Online Social Platform. In: AIES '22: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, https://doi.org/10.1145/3514094.3534149
Martinez Demarco, Sol; Schelenz, Laura (2022): Diversity and Technology. A Zine. Internationales Zentrum für Ethik in den Wissenschaften. Materialien zur Ethik in den Wissenschaften; 21. ISBN: 978-3-935933-20-9.
Schelenz, Laura (2022): Diversity Concepts in Computer Science and Technology Development: A Critique. In Science, Technology, & Human Values. DOI: https://doi.org/10.1177/01622439221122549
Schelenz, Laura (2022): Artificial Intelligence Between Oppression and Resistance: Black Feminist Perspectives on Emerging Technologies. In Ariane Hanemaayer (Ed.): Artificial Intelligence and Its Discontents. Cham: Springer International Publishing (Social and Cultural Studies of Robots and AI), pp. 225–249.
Schelenz, Laura; Vondermaßen, Marcel (2022): Diversity, Identity, Oppression: The Construction of “Blackness” in Dear White People. In Open Philosophy 5 (1), pp. 44–56. DOI: 10.1515/opphil-2020-0171.
Schelenz, Laura (2021): Schwarzfeministische Perspektiven auf Künstliche Intelligenz: Erkenntnisse und neue Fragen zu KI-gestützter Gesichtserkennung und Überwachung. In Femina Politica – Zeitschrift für feministische Politikwissenschaft 30 (2-2021), pp. 73–93. DOI: 10.3224/feminapolitica.v30i2.07.
Schelenz, Laura; Bison, Ivano; Busso, Matteo; de Götzen, Amalia; Gatica-Perez, Daniel; Giunchiglia, Fausto; Meegahapola, Lakmal; Ruiz-Correa, Salvador (2021): The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations. In: AIES '21: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3461702.3462595
Schelenz, Laura (2021): Diversity-aware Recommendations for Social Justice? Exploring User Diversity and Fairness in Recommender Systems. In Judith Masthoff, Eelco Herder, Nava Tintarev, Marko Tkalčič (Eds.): Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization. Utrecht Netherlands, 21 06 2021 25 06 2021. New York, NY, USA: ACM, pp. 404–410. https://doi.org/10.1145/3450614.3463293
Schelenz, Laura; Pawelec, Maria (2021): Information and Communication Technologies for Development (ICT4D) critique. In Information Technology for Development, pp. 1–24. https://doi.org/10.1080/02681102.2021.1937473
Heesen, Jessica; Reinhardt, Karoline; Schelenz, Laura (2021): Diskriminierung durch Algorithmen vermeiden: Analysen und Instrumente für eine demokratische digitale Gesellschaft. In Gero Bauer, Maria Kechaja, Sebastian Engelmann, Lean Haug (Eds.): Diskriminierung und Antidiskriminierung. Beiträge aus Wissenschaft und Praxis. 1. Auflage. Bielefeld: transcript; Transcript Verlag (Gesellschaft der Unterschiede, 60).
Schelenz, Laura; Segal, Avi; Gal, Kobi (2020): Best Practices for Transparency in Machine Generated Personalization. In Tsvi Kuflik, Ilaria Torre, Robin Burke, Cristina Gena (Eds.): Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization. Genoa Italy, 14 July 2020 - 17 July 2020. New York, NY, USA: ACM, pp. 23–28. https://doi.org/10.1145/3386392.3397593