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Behavioural artificial intelligence: an agenda for systematic empirical studies of artificial inference

Abstract

Artificial intelligence (AI) receives attention in media as well as in academe and business. In media coverage and reporting, AI is predominantly described in contrasted terms, either as the ultimate solution to all human problems or the ultimate threat to all human existence. In academe, the focus of computer scientists is on developing systems that function, whereas philosophy scholars theorize about the implications of this functionality for human life. In the interface between technology and philosophy there is, however, one imperative aspect of AI yet to be articulated: how do intelligent systems make inferences? We use the overarching concept “Artificial Intelligent Behaviour” which would include both cognition/processing and judgment/behaviour. We argue that due to the complexity and opacity of artificial inference, one needs to initiate systematic empirical studies of artificial intelligent behavior similar to what has previously been done to study human cognition, judgment and decision making. This will provide valid knowledge, outside of what current computer science methods can offer, about the judgments and decisions made by intelligent systems. Moreover, outside academe—in the public as well as the private sector—expertise in epistemology, critical thinking and reasoning are crucial to ensure human oversight of the artificial intelligent judgments and decisions that are made, because only competent human insight into AI-inference processes will ensure accountability. Such insights require systematic studies of AI-behaviour founded on the natural sciences and philosophy, as well as the employment of methodologies from the cognitive and behavioral sciences.

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Notes

  1. Knight, Will (14 March 2017). “DARPA is funding projects that will try to open up AI's black boxes”. MIT Technology Review. https://www.technologyreview.com/s/603795/the-us-military-wants-its-autonomous-machines-to-explain-themselves/.

  2. Sample, Ian (5 November 2017). “Computer says no: why making AIs fair, accountable and transparent is crucial”. the Guardian. https://www.theguardian.com/science/2017/nov/05/computer-says-no-why-making-ais-fair-accountable-and-transparent-is-crucial.

  3. The EPSRC ‘Human-Like Computing’ initiative aims to bridge this ‘gap’ between ‘symbolic’/’rational’ and ‘neural’/’empirical’ AI. See: http://hlc.doc.ic.ac.uk/.

  4. Cfr stimulus—response and classical conditioning.

  5. Even ‘simple’ codes can sometimes be difficult to fully comprehend, e.g., probabilistic programs (Gordon et al. 2014; Katoen et al. 2015) or concurrent programs (Andrews and Schneider 1983; Ben-Ari 2006; Dijkstra 1965).

  6. New York City Council (2018). A Local Law in relation to automated decision systems used by agencies. http://legistar.council.nyc.gov/LegislationDetail.aspx?ID=3137815&GUID=437A6A6D-62E1-47E2-9C42-461253F9C6D0.

  7. EU Parliament (2016). EU Framework on algorithmic accountability and transparency. http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//TEXT+WQ+E-2016-007674+0+DOC+XML+V0//EN.

  8. https://www.kaggle.com/competitions.

  9. http://hlc.doc.ic.ac.uk/.

  10. https://www.britannica.com/science/hypothetico-deductive-method.

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Acknowledgements

We are very grateful to the anonymous reviewers for their valuable comments that have improved this paper.

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Correspondence to Tore Pedersen.

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Tore Pedersen was partially supported by the project Oslo Analytics funded by the IKTPLUSS program of the Norwegian Research Council. Christian Johansen was partially supported by the project IoTSec—Security in IoT for Smart Grids, with Number 248113/O70 part of the IKTPLUSS program funded by the Norwegian Research Council.

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Pedersen, T., Johansen, C. Behavioural artificial intelligence: an agenda for systematic empirical studies of artificial inference. AI & Soc 35, 519–532 (2020). https://doi.org/10.1007/s00146-019-00928-5

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Keywords

  • Artificial intelligence
  • Artificial inference
  • Behavioral artificial intelligence
  • Artificial intelligent behaviour
  • Bias
  • Transparency
  • Accountability
  • Ethics