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Artificial Intelligence and Pro-Social Behaviour

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Collective Agency and Cooperation in Natural and Artificial Systems

Part of the book series: Philosophical Studies Series ((PSSP,volume 122))

Abstract

If artificial intelligence (AI) were achievable, what would the consequences be for human society? Perhaps surprisingly, the answer to this question is already at hand. We are achieving rapid and accelerating success in our quest to build AI. That very success—and the slowness with which both the academic community and the general public have come to recognise it—has shown how little we understand our own intelligence, and its role in our lives and culture. Here I attempt to address this problem of understanding, exploiting a variety of scientific evidence, including social simulation. I begin by reviewing current progress in AI, which is profound but underestimated. I suggest this lack of recognition is due to the mistaken belief that intelligence implies agency. I next examine the related question of human uniqueness: why do only we have language and extensive built culture? I use models and data to show that the propensities to use culture, share information and behave altruistically are neither unique to humans nor inexplicable to biology, but rather our uniqueness hinges on the extent of our capacities for communication and memory. Finally, I apply the impact of AI on extending our intelligence to these theories, to predict—and observe—consequences of AI on human societies and individual human lives. I make and support policy recommendations based on these predictions.

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Notes

  1. 1.

    Except where plants are seen as either a part of a broader ecosystem, or as a possession of a human.

  2. 2.

    The arguments in this chapter hinge on inclusive fitness (Hamilton 1964; Gardner and West 2014) rather than individual survival, but I postpone that discussion here. It is appears in Sect. 15.3.

  3. 3.

    Search companies record information about searches and the response of users to the web pages served, so those companies are also intelligent and motivated agents that benefit from the act of the search, but they do not originate it.

  4. 4.

    Memes are the hypothesised replicators for horizontal (non-genetic) transmission of behaviour. Like genes, they have yet to be precisely defined or measured (Mesoudi et al. 2004). It is also not yet clear the extent to which they change in frequency in accordance to Darwinian evolution (El Mouden et al. 2014). Nevertheless, memes are widely acknowledged as a useful abstraction for thinking about the transfer of traits expressed as behaviour between individuals by means other than biological reproduction.

  5. 5.

    Adaptive in the biological sense of having been facilitating selection. The AI literature sometimes uses the term adaptive to mean plastic or mutable.

  6. 6.

    Further, humans at least may choose to associate with those with similar gene structure even where they are not family members (Christakis and Fowler 2014).

  7. 7.

    The capacity to recognise novel sounds and to learn novel contexts to express sounds should not be confused with the capacity for vocal imitation (Bryson 2009; Fitch and Zuberbühler 2013).

  8. 8.

    The vast numbers of possible strategies is produced by a process called combinatorial explosion, which I explain in more detail in Sect. 15.4.2. The importance of having a varied set of available possible solutions in order for evolutionary selection to proceed is part of the ‘Fundamental theorem of evolution’ (Fisher 1930; Price 1972), and will be key in the final section of this chapter, Sect. 15.5.

  9. 9.

    There is decent evidence that association with dominance is indeed the ultimate evolutionary explanation for spiteful behaviour, see for a review Sylwester et al. (2013).

  10. 10.

    These were public goods games (PGG). Participants were separated by partitions and were unable to directly interact with or identify other group members. They played games in groups of four, with each participant able to either keep all of the endowment received from the experimenter (20 experimental currency units; ECU) or contribute some portion of the endowment to the public good. At the end of a round, all contributions were combined and the sum multiplied by 1.6. The obtained amount was divided evenly amongst all of the group members, regardless of their contribution. The payoff of each participant was calculated by summing up the amount kept and the amount received from the public good. Ten rounds were played as described above, and also ten rounds with the addition of punishment: participants after seeing the contributions of other players to the pubic good and could decide how much they wished to spend on reducing the payoff to other players. Participants could spend up to 10 ECU punishing the other players. Each ECU spent on sanctioning resulted in 3 ECU being deducted from the payoff to the targeted individual. A participant’s payoff was calculated by subtracting the amount of ECU spent on sanctioning and the deduction points received from other players from the payoff from the PGG. Received deductions were capped so as not to exceed PGG earnings. Participants did not receive information about who deducted points from their payoff, making punishment anonymous. At the end of the experiment, participants received real money in the local currency in exchange for the total ECU accumulated across all rounds. See further Sylwester et al. (2014); Herrmann et al. (2008b).

  11. 11.

    Those who gave the most or the least to the group could assess the nature of the punishment they received, but our results held even when these were excluded (Sylwester et al. 2014).

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Acknowledgments

Thanks to Lydia Harriss, Catrin Misselhorn, Miles Brundage and Evan Selinger for encouragement and discussions; David Gunkel and Will Lowe for debate; Misselhorn, Brundage, Selinger and Robin Dunbar for comments on an earlier draft; and Thomas König and the University of Mannheim SFB 884, The Political Economy of Reforms, for a quiet office to work in.

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  1. Department of Computer Science, University of Bath, Bath, UK

    Joanna J. Bryson

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Correspondence to Joanna J. Bryson .

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  1. Institute of Philosophy, University of Stuttgart, Stuttgart, Germany

    Catrin Misselhorn

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Bryson, J.J. (2015). Artificial Intelligence and Pro-Social Behaviour. In: Misselhorn, C. (eds) Collective Agency and Cooperation in Natural and Artificial Systems. Philosophical Studies Series, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-319-15515-9_15

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