Algorithms for the Greater Good! On Mental Modeling and Acceptable Symbiosis in Human-AI Collaboration

By Tathagata Chakraborti, Subbarao Kambhampati. 2018

28 citations recorded by the source corpus.

Effective collaboration between humans and AI-based systems requires effective modeling of the human in the loop, both in terms of the mental state as well as the physical capabilities of the latter. However, these models can also open up pathways for manipulating and exploiting the human in the hopes of achieving some greater good, especially when the intent or values of the AI and the human are not aligned or when they have an asymmetrical relationship with respect to knowledge or computation power. In fact, such behavior does not necessarily require any malicious intent but can rather be borne out of cooperative scenarios. It is also beyond simple misinterpretation of intents, as in the case of value alignment problems, and thus can be effectively engineered if desired. Such techniques already exist and pose several unresolved ethical and moral questions with regards to the design of autonomy. In this paper, we illustrate some of these issues in a teaming scenario and investigate how they are perceived by participants in a thought experiment.

Subfields: Safety Research, Sociology and Political Science

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Citations in the corpus

Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation (2018, background): “…form have been inspired by works like (Lombrozo 2006; 2012; Miller 2017) which identify properties of explanations in terms of selectivity , con-trastiveness and mental modeling of the explainee, and our recent work (Chakraborti et al. 2018) also demonstrated the usefulness of such explanations.”
Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance (2019, background): “Ongoing and prior research has contributed to improving the interpretability of such predictions either by building more interpretable models (Caruana et al. 2015; Rudin 2018; Lage et al. 2018) or by imitating complex models via simpler but more explainable ones (Lakkaraju, Bach, and Leskovec 2016;…”
User Perceptions and Trust of Explainable Machine Learning Fake News Detectors (2022, background): “One of the most challenging things in adopting AI technology is to situate AI in humans’ beliefs and intentions (Chakraborti & Kambhampati, 2018).”