Regimes of Representability
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Autor:in (Körperschaft)
Publikationsdatum
16.09.2025
Typ der Arbeit
Studiengang
Typ
06 - Präsentation
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Themenheft
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Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
We are asking these questions at a time when AI is increasingly used to generate personalized knowledge e.g., when replacing the lists of results of classic search engines with short summaries or extractive snapshots of the knowledge that is available (for you) on the internet (sic!). Scholars have discussed ad nauseam how both the data sets used for training machine learning algorithms and the processes through which these models “learn” result in skewed representations of reality — because they reproduce biases in data and iterate ways of knowing that render many aspects of reality invisible or unrepresentable.
Yet, none of these discussions and critiques have prevented those technologies framed as AI that have come to govern what we see, remember, and believe to know. In this CML in-conversation event, we will therefore discuss how AI has, despite all its known shortcomings, established credibility, reliability, and relatability — as e.g., a more ethical tool of representing violence, a more personal and intimate respondent, or a more balanced consensus-builder.
Schlagwörter
Artificial intelligence, Machine learning, Race, Epistemology
Fachgebiet (DDC)
Veranstaltung
Critical Media Lab Colloquium
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
16.09.2025
Enddatum der Konferenz
16.09.2025
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Begutachtung
Keine Begutachtung
Open Access-Status
Lizenz
Zitation
Bruder, J., Phan, T., Dhaliwal, R. S., & Ren, Q. (2025, September 16). Regimes of Representability. Critical Media Lab Colloquium. https://irf.fhnw.ch/handle/11654/54407