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ICAIL Doctoral Consortium, Montreal 2019

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Abstract

This is a report on the Doctoral Consortium co-located with the 17th International Conference on Artificial Intelligence and Law in Montreal.

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Notes

  1. Business Process Model And Notation. https://www.omg.org/spec/BPMN/2.0/ accessed 20.02.2020.

  2. Both models are referred to as automated judicial proceedings.

  3. It is worth mentioning in this context that many empirical studies in various fields proved that the explanation can change the users’ attitudes towards the system: it can increase their confidence and trust in the systems e.g. (Herlocker et al. 2000; Sinha and Swearingen 2002; Bilgic and Mooney 2005 and Symeonidis et al. 2009), and improve their ability to correctly assess whether a prediction is accurate (e.g. Kim et al. 2016; Gkatzia et al. 2016 and Biran and McKeown 2017).

  4. I reject the possibility to use AI in order to make decisions which could not be controlled by humans.

  5. As a result, generic explanations and standard ML explanations [e.g. the list of “most predictive topics” used by Aletras et al. (2016)] are unlikely to be useful and acceptable. Similarly, rewriting the steps of the decision-making algorithm in natural language is not what is required.

  6. Judgment of the European Court of Human Rights of 16 November 2010 (Case of Taxquet v. Belgium).

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Correspondence to Michał Araszkiewicz.

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Araszkiewicz, M., Amantea, I.A., Chakravarty, S. et al. ICAIL Doctoral Consortium, Montreal 2019. Artif Intell Law 28, 267–280 (2020). https://doi.org/10.1007/s10506-020-09267-z

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