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@INPROCEEDINGS{Rathkopf:1049019,
      author       = {Rathkopf, Charles},
      title        = {{A}rtificial {C}ompetence},
      school       = {Tübingen},
      reportid     = {FZJ-2025-05114},
      year         = {2025},
      abstract     = {AI systems increasingly match or surpass humans on complex
                      tasks, yet they often exhibit surprising failure modes or
                      inconsistent behavior across evaluation contexts. While
                      cognitive science relies on the distinction between
                      competence and performance to explain similar discrepancies
                      in humans, this distinction is often framed in terms that
                      preclude its straightforward application to artificial
                      neural networks. This paper develops a unified account of
                      competence applicable to both biological and artificial
                      systems, locating competence at the algorithmic level of
                      analysis. On this view, a system is competent in a domain
                      when it implements an algorithm that reliably generalizes
                      across that domain. Importantly, the relevant notion of
                      implementation applies to neural networks when formalized
                      under causal abstraction: a neural network implements an
                      algorithm if there exists a mapping between the network's
                      components and the algorithm's variables such that both
                      respond identically to causal interventions. This framework
                      provides a principled way to distinguish competence from
                      auxiliary factors that affect performance across systems
                      with very different constraints and architectures. It
                      thereby accounts for double dissociations between
                      performance and competence in both humans and AI systems,
                      and offers a template for designing competence-sensitive
                      evaluation in cognitive science and AI.},
      month         = {Dec},
      date          = {2025-12-08},
      organization  = {Colloquium of the Department of
                       Linguistics at the University of
                       Tübingen, (Germany), 8 Dec 2025 - 8
                       Dec 2025},
      subtyp        = {Invited},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5255 - Neuroethics and Ethics of Information (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5255},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/1049019},
}