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Auflistung nach Autor:in "Braun, Tanya"

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  • Zeitschriftenartikel
    AI in Healthcare and the Public Sector
    (KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Braun, Tanya; Möller, Ralf
  • Zeitschriftenartikel
    AI in Healthcare and the Public Sector: How to Face the Challenges of High-Risk Applications and What AI Research Can Get Out of It
    (KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Braun, Tanya; Möller, Ralf
    Application projects, may it be in healthcare and the public sector or elsewhere, have the potential to advance foundational (“genuine”) artificial intelligence (AI) research. Unfortunately, insights from specific application projects are rarely propagated back to AI research. This article argues for ways to facilitate such backpropagation and how the contributions in this special issue enable exactly this backpropagation. It also addresses the challenges that come along with high-risk application project, which frequently occur in the area of healthcare and the public sector due to the sensitivity of the subjects.
  • Zeitschriftenartikel
    Lessons from Resource-Aware Machine Learning for Healthcare: An Interview with Katharina Morik
    (KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Braun, Tanya; Möller, Ralf
  • Zeitschriftenartikel
    Lifting in Support of Privacy-Preserving Probabilistic Inference
    (KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Gehrke, Marcel; Liebenow, Johannes; Mohammadi, Esfandiar; Braun, Tanya
    Privacy-preserving inference aims to avoid revealing identifying information about individuals during inference. Lifted probabilistic inference works with groups of indistinguishable individuals, which has the potential to prevent tracing back a query result to a particular individual in a group. Therefore, we investigate how lifting, by providing anonymity, can help preserve privacy in probabilistic inference. Specifically, we show correspondences between k -anonymity and lifting and present s-symmetry as an analogue as well as PAULI, a privacy-preserving inference algorithm that ensures s-symmetry during query answering.
  • Textdokument
    StaRAI or StaRDB?
    (BTW 2019 – Workshopband, 2019) Braun, Tanya
    This tutorial aims at connecting databases and statical relational AI (StaRAI), demonstrating how database systems can benefit from methods developed within StaRAI, e.g., for implementing efficient systems combining databases and StaRAI. Thus, the goal of this tutorial is two-fold: (i) Present an overview of methods within StaRAI. (ii) Provide a forum to members of both communities for exchanging ideas.