Cross-Cultural Approaches to Desirable AI

Die Seminarreihe „Cross-Cultural Approaches to Desirable AI“ will die interkulturelle und interdisziplinäre Diskussion über die Ethik der Künstlichen Intelligenz (KI) fördern. Ihr Hauptaugenmerk liegt auf der Unterstützung und Entwicklung von Forschung zu KI und sozialer Gerechtigkeit, insbesondere aus intersektionalen feministischen und antirassistischen Perspektiven. „Cross-Cultural Approaches to Desirable AI“ ist eine Zusammenarbeit zwischen den Universitäten Cambridge, Bonn, Tokio und der University of Europe for Applied Sciences.

Wir bringen Wissenschaftler*innen aller Karrierestufen aus unterschiedlichen Disziplinen zusammen, die sich mit KI und digitalen Technologien beschäftigen. Die Veranstaltungsreihe soll eine Brücke zwischen den Perspektiven der Geistes- und Naturwissenschaften schlagen und insbesondere Studierende mit technischem und ingenieurwissenschaftlichem Hintergrund dazu ermutigen, sich mit ethischen Fragen rund um KI auseinanderzusetzen.

Das Konzept „Desirable AI“ bezieht sich auf die Entwicklung von Technologien, die soziale Gerechtigkeit und ökologische Nachhaltigkeit in den Mittelpunkt stellen. Ziel ist es nicht, einfach nur die Technologie zu optimieren, sondern eine KI zu entwickeln, die die unterschiedlichen Werte und Bedürfnisse der verschiedenen Kulturen widerspiegelt und respektiert. Dieser Ansatz erfordert die Anerkennung unterschiedlicher Weltanschauungen und vermeidet gleichzeitig Probleme wie kulturelle Aneignung und „Diversity Washing“ in der Technologieentwicklung.

final_klein_Cross-Cultural Approaches.png
© Clarote & AI4Media / Better Images of AI / Labour/Resources / CC-BY 4.0

Anmeldung und Zeitraum

Datum

09.10.2024 - 22.01.2025

Mittwochs, 10-12 Uhr (s.t.)

Die Sitzungen werden über Zoom abgehalten.

                                              

Anmeldung

Bitte melden Sie sich an, um den Zoom-Zugang zu erhalten:

Sitzungsplan

Organizers of the Seminar Series: Kerry McInerney, Eleanor Drage (University of Cambridge), Jiré Emine Gözen (University of Europe for Applied Sciences), Ai Hisano, Sunjin Oh (University of Tokyo), Christiane Schäfer (University of Bonn)

  • Wakanyi Hoffman, Utrecht University: Ubuntu 2.0. A Compatible Hybrid Intelligence for Human and Machine Co-Living.
  • Jahaziel Osei Mensah, University of Ghana/IWE: The Role of ‘Ukama’ Towards Environmental Sustainability in Africa.
  • Pius Mosima, Leiden University: Being Human in the Technological Age.
  • Amara Esther Chimakonam, University of Fort Hare: Towards Indigenous Ethical Principles for Artificial Intelligence.
  • Panel Chair: Christiane Schäfer, CST Bonn
  • Sunjin Oh, UTokyo: Rootless Signifiers: Does Signification Have a Chance in the Digital World?
  • Xinqi He, Rikkyo University: A post-humanist perspective on plagiarism in the era of AI.
  • Priya Mu, UTokyo: Embodied Self and the Creative Process.
  • Aisha Sobey, LCFI: The Hidden Expectations of Self-Tracking Apps: Who Really Benefits?
  • Amy Gaeta, LCFI: Diagnostic Advertisements: Social Media, Surveillance, and Targeted Heath Marketing.
  • Carolin Fleischer-Heininger, LMU: Desirable AI for the real world: shaping futures through a cultural disability studies lens.
  • Panel Chair: Kerry McInerney, University of Cambridge
  • Tobias Matzner, University of Paderborn: The Politics of Data and Algorithms.
    • The social and political implications of so called artificial intelligence are widely discussed in public discourses as well as academic circles. In both, the argument most often points at training data. Data are biased and systems that are trained on them take over these biases. Computer scientists sometimes state this a bit coarsely as "bullshit in, bullshit out". In the presentation, I want to discuss that this emphasis on data is not wrong, but too short. In a sense, data only become data (and thus biased data) when they are used by an algorithm. To show this, I will revisit the history of AI since the 1980s and detail how the concept of data changed from data as description of the world to data as material for algorithms. In consequence, I will point out how algorithms and data need to be considered as mutually related.
  • Mary Shnayien, University of Paderborn: Calculating Difference(s).
    • If in the search for the locus of bias in AI the emphasis should not just lie on training data, but also, as my colleague points out, on the algorithms, it would be interesting to also look at the mathematical principles governing said algorithms. In my presentation, I want to do a deep dive into the history of some mathematical principles and calculation methods at the core of AI, as well as their respective ways of generating difference. The central question of my inquiry is which assumptions about gender, race and/or other categories of difference are incorporated into AI systems via mathematical processes.

  • Panel Chairs: Jiré Emine Gözen & Iris Lorscheid, University of Europe
  • Yulu Pi, LCFI: Human-Centric and Outcome-Focused Approaches to AI Governance and Compliance.
  • James Wright, UNESCO: UNESCO’s approach to AI ethics and governance.
  • Hiroki Habuka, Wadhwani AI Center: Japan's Approach to AI Regulation: Shaping the future of Human-Machine Interaction.
  • Panel Chair: Eleanor Drage, University of Cambridge
  • Audrey Borowski, CST Bonn: Planetary AI.
  • Chelsea Haramia, CST Bonn: Unsustainable AI and Global Ethical Inequality.
  • José Renato Laranjeira de Pereira, IWE Bonn: AI Policies and the Environment.
  • Tom Metcalf, IWE Bonn: Artificial Intelligence, Political Education, and the Sustainability of Democracy.
  • Beate Hertwig & María Nenclares, University of Europe
    • This conversation explores the intersection of AI and design, focusing on how AI influences the creative design process and accessibility. In the first half, Beate Hertwig  discusses AI's impact on creativity, examining in which ways will AI impact the creative process, along with examples from UX processes and group interactions. The second half, led by Maria, dives into the accessibility of AI tools, emphasizing inclusivity, equity, and democratization in technology in the context of desirable futures. A participatory approach will be encouraged throughout, inviting critical questions from the audience and fostering a dialogue between Beate and Maria. The session concludes with thoughts on the future of AI in design professions, addressing ethics and inclusivity.
  • Panel Chair: Jiré Emine Gözen, University of Europe
  • Tomasz Hollanek, LCFI: Designing Deadbots: Human-AI Interaction in the Digital Afterlife Industry.
  • Katarzyna Nowaczyk-Basińska, LCFI: Imaginaries of Immortality in the Age of AI: An Intercultural Perspective.
  • Chihyung Jeon, Graduate School of Science, Technology, and Policy of KAIST: Alive again, digitally: Resurrecting dead persons into virtual humans in South Korea.
  • Akito Orita, Kanto Gakuin University: Digital Data of the Deceased in Japan. Two Paradoxes of Preservation and Rejection.
  • Panel Chair: Stephen Cave, University of Cambridge
  • Galina Shyndriayeva, Musashi University: New Accords: Exploring Creativity and Agency in AI-Assisted Perfume Creation.
  • Alyssa Yap, UTokyo: Desirable Images of the Healthy Human: From Medical Interventions to Generative AI Prompt Generation.
  • Grant Jun Otsuki, UTokyo: ​​Towards a Post-Turing Typology of Machines.
  • Panel Chair: Ai Hisano, UTokyo

Desirable Digitalisation: Rethinking AI for Just and Sustainable Futures

Das Projekt "Desirable Digitalisation: Rethinking AI for Just and Sustainable Futures" ist ein gemeinsames Forschungsprogramm der Universitäten Cambridge und Bonn, das von der Stiftung Mercator gefördert wird. Es erforscht, wie KI (künstliche Intelligenz) und andere digitale Technologien durch Konzepte des Humanen beeinflusst werden und wie sie verantwortungsvoll, sozial gerecht und ökologisch nachhaltig gestaltet werden können.


Kontakt und Organisation

Avatar Schäfer

Christiane Schäfer

Universität Bonn, Center for Science and Thought, Institut für Philosophie, Konrad-Zuse-Platz 1-3

53227 Bonn


Wird geladen