Abstract
Trust is a human condition. For a human to trust a machine, the human must understand the capabilities and functions of the machine in a context spanning the domain of trust so that the actions of the machine are predictable for a given set of inputs. In general, we would like to expand the domain of trust so that a human–machine system can be optimized for the widest range of operating scenarios. This reasoning motivates the desire to cast the operations of the machine into a knowledge structure that is tractable to the human. Since the machine is deterministic, for every action, there is a reaction and the dynamics of the machine can be described through a structural causal model to enable the formulation of the counterfactual queries upon which human trust may be anchored.
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References
Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2d ed.). New York: Cambridge University Press
Peters, J., Janzing, D., & Schölkopf, B. (2017). Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA: MIT Press.
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Deignan, P. (2021). Human–Machine Understanding: The Utility of Causal Models and Counterfactuals. In: Lawless, W.F., Mittu, R., Sofge, D.A., Shortell, T., McDermott, T.A. (eds) Systems Engineering and Artificial Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-77283-3_12
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DOI: https://doi.org/10.1007/978-3-030-77283-3_12
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