Biology and Philosophy symposium on Simulation and Similarity: Using Models to Understand the World

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Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2020, background): “ior to real-world deployment [26]. Engineering models are abstracted simplifications of reality, necessarily incomplete, emphasizing some features of reality while deemphasizing or overlooking others [143]; they are plausible aids but “potentially fallible” [82]. ML datasets are in fact prototypical”
The math is not the territory: navigating the free energy principle (2021, background): “According to Weisberg’s (2007; 2013) account of models, a model’s struc-ture—whether mathematical, computational, or physical—does not inherently relate to a target system in an epistemically fruitful way, e.g., by representing features of that target system.”