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Computer decision-support systems for public argumentation: assessing deliberative legitimacy

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Abstract

Recent proposals for computer-assisted argumentation have drawn on dialectical models of argumentation. When used to assist public policy planning, such systems also raise questions of political legitimacy. Drawing on deliberative democratic theory, we elaborate normative criteria for deliberative legitimacy and illustrate their use for assessing two argumentation systems. Full assessment of such systems requires experiments in which system designers draw on expertise from the social sciences and enter into the policy deliberation itself at the level of participants.

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Notes

  1. For an introduction, see Benfer et al. 1990.

  2. One can discern a natural evolution from knowledge systems to argumentation systems in the increasing concern with providing users with the underlying explanation for the system’s conclusion (or advice). Although developers of knowledge systems sought from the start to have their systems explain or justify their conclusions, early expert systems offered little in the way of explanation beyond high-level traces of the inference rules invoked in the chain of reasoning (Jackson 1986). It should be noted, however, that argumentation systems do more than provide detailed explanations of their reasoning. Indeed, in many ways the hallmark of an argumentation system is that it makes explicit use of the structure of the explanation, the interlocking reasons for and against a proposition, in order to determine its conclusions. Applications using some model of argumentation have included systems for: medical domains (Krause et al. 1995; Fox and Das 2000); legal domains (Gordon 1994; Verheij 1998; Bench-Capon et al. 2003); public policy decision support (Gordon and Karacapilidis 1997; Gordon et al. 1997; Karacapilidis et al. 1997); geopolitical risk prediction (Seffers 1998); scientific discourse (McBurney and Parsons 2000, 2001b); software design (Stathis 2000); and autonomous agent dialogues (Parsons et al. 1998; Sierra et al. 1998; Dignum et al. 2000). Carbogim et al. (2000) present a review of such applications.

  3. For example, Dung (1995) proposed dialectical argumentation within AI as a proof theory for non-monotonic reasoning; for a review of this area, see Prakken and Vreeswijk (2001). For broad overviews of developments in computational dialectics, see Walton (2000), and Reed and Norman (2003).

  4. As Haklay notes in a review of information technology for environmental decision-making, the philosophical and ethical aspects of information systems design are rarely made explicit or even explored (Haklay 2001).

  5. The GMD National Research Center for Information Technology became part of the Fraunhofer Gesellschaft in 2001.

  6. Much of the research in the field of AI and law appears devoted to developing systems which take an orrery role, formalizing the reasoning processes used by decision-makers so as to better understand them (Bench-Capon et al. 2003).

  7. Likewise, systems of autonomous software agents assume decision-making is being undertaken by computational entities, and much of the research effort of second-generation electronic commerce, for instance, is directed at such automated decision-making (Jennings et al. 2001).

  8. The question to what extent humans should delegate decision-making authority to computer systems has been discussed in ethical philosophy, though a well-known early objection was raised by AI researcher Weizenbaum (1976); see also Moor (1979), and Kuflik (1999).

  9. E.g., Buchanan and Shortliffe (1984); in Chap. 30 of this work, the authors consider quality evaluation methods for decision support systems. Greenberg (1987) also considers evaluation issues for such systems, focusing his discussion on the validation of the inference rules used by the system. Taylor (1991) briefly discusses evaluation questions as part of an exploration of the wider organizational issues associated with deployment of these systems. Parker (2000) proposes a design methodology based on the types of questions likely to be asked by a user of the system. While promising, this approach seems more appropriate for what we have termed knowledge systems (those encoding expert knowledge) than for argumentation systems.

  10. For example, the SimCoast expert system of the UK’s Centre for Coastal and Marine Sciences was developed to provide assistance to marine coastal environment decision-makers in developing nations (McGlade 1999). However, the expertise encoded in the system embodies a specific Weltanschauung, that of a standardized western scientific ontology, which may not accord with the world views or ontologies of non-scientist users in the developing world. Moreover, Western expertise may actually distort the perception of the local reality, as shown by the example of deforestation in West Africa (Fairhead and Leach 1998). Using developing-country residents as the “experts” for the design phase may well have led to a different system.

  11. There has been considerable research on this variance (see, e.g., Nisbett and Ross 1980; Kahneman et al. 1982). In a recent review of the limited research into how people make important medical decisions, Schneider concluded that patients make decisions quite differently from the ways proposed by normative decision theory or used by experts; however, we have few means to measure the quality of medical decisions. Selecting one procedure or course of treatment usually precludes the selection of alternatives, and so strict comparison of results of alternative decision options at an individual level is impossible. The diversity and complexity of individual circumstances and medical aetiologies make comparisons at an aggregated level also problematic (Schneider 1998).

  12. Developers of public mobile satellite communications networks such as Iridium and ICO, for example, had lead times of a decade for designing, manufacturing and deploying the innovative satellite technology they required (McBurney and Parsons 2002a). To guide this work, the intending investors sought the advice of market researchers on the size of the potential market for mobile satellite services. In the particular decade concerned (1989–1999) demand for terrestrial cellular services grew much faster than anyone had forecast at the outset of the period, to the detriment of demand for mobile satellite services at the end of the decade. In major part this growth in terrestrial cellular demand was spurred by technological changes and the spread of cellular network coverage unanticipated in 1989. Was the advice given by the market researchers wrong because it did not predict unanticipated events, or, if anticipated, did not sufficiently emphasize unlikely events? The advice may not have been wrong if the world had been otherwise in the subsequent decade. Most management consultancy is not assessable or assessed for these two reasons (an observation based on the second author’s decade of experience as a management consultant).

  13. In the one quality assessment of decision support systems known to us, Groothuis and Svensson (2000) assessed the quality of decisions made with the help of computer systems in the provision of welfare assistance in The Netherlands, comparing the actual determination reached in a sample of welfare assistance cases with the decision which should have been made under the relevant laws. The results of this assessment showed that decision quality varied according to the extent to which the system provided support for the complex administrative tasks involved. For those cases where the system provided full support, few human errors were made; in other cases, where only limited support was provided, decision errors were more frequent. The authors concluded that this was due to the human decision-makers trusting the advice of the system even when such trust was not warranted, thus revealing weaknesses in the human decision-makers rather than in the decision support systems.

  14. For example, a system designed to support water-flow management through a dam will only be required to recommend responsive actions for 200-year floods on average once every 200 years. There may be insufficient data to design the system or to predict its performance in these circumstances, and possibly only one case every 200 years on which to base a live assessment of that performance. Moreover, if a system is designed for an entirely new activity how does one assess the adequacy of its advice? The various intelligent agent systems currently being deployed by NASA for control of autonomous spacecraft are examples of systems which undertake completely new activities. How is it possible to rate their performance in any other but crude terms, such as overall mission success versus non-success? (NASA 1999).

  15. For a well-known statement of the pluralist model, which draws on James Madison’s Federalist Paper no. 10, see Dahl (1956).

  16. In Hitchcock et al. (2001), the characteristic of sharing information is taken to be one that distinguishes deliberation from negotiation dialogues (in contrast to the definition in Walton and Krabbe 1995). In contrast, because much of the research focus in the area of intelligent multi-agent software systems has been on automated negotiation (Jennings et al. 2001), this self-transformative condition is not satisfied by all agent systems.

  17. See Bohman (1996), esp. Chap. 3. Even a technology as apparently benign as literacy can be used by those possessing it to establish and maintain political power, as Bledsoe and Robey (1986) show in their study of Arabic literacy among the Mende of Sierra Leone.

  18. For a review of automated negotiation systems, see Jennings et al. (2001).

  19. By contrast, in systems of autonomous software agents using argumentation, such as the team-formation systems of Dignum et al. (2000), the entirety of the dialogue occurs inside the system.

  20. Such questions arise, for example, in the use of intelligent systems in medical domains. See, for example, Emery et al. (1999).

  21. We focus on the Risk Agora because the dialogue rules are explicit in the system formalism. This is not to deny that certain features of the Zeno argumentation framework could be translated into such rules.

  22. Although similar procedural criteria have been proposed for assessment of automatic electronic auction systems (Sandholm 1999), to our knowledge this is the first time such proposals have been made for argumentation-based computer systems.

  23. Cf. Hitchcock’s “externalization” property, which requires that rules in the system “be formulated in terms of verifiable linguistic behavior” (Hitchcock 1991).

  24. For example, Gardenfors (1994) discusses the reasoning processes used by illiterate Uzbeki peasants, and argues that apparent violations of deductive inference rules are, in fact, differential assessments of argument premises according to the perceived experience of the proponent of the premise. A formalism which did not represent this aspect of argument premises would not be able to represent the styles of arguments used by the Uzbekis.

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Acknowledgements

This work was partly funded by the British Engineering and Physical Sciences Research Council (EPSRC) through a PhD studentship. We gratefully acknowledge this support. We are also grateful for discussions on these topics with Trevor Bench-Capon, Rod Girle, Muki Haklay, David Hitchcock and Bart Verheij. We also thank Thomas Gordon and R. Prescott Loui for their feedback on an earlier draft of this paper.

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Rehg, W., McBurney, P. & Parsons, S. Computer decision-support systems for public argumentation: assessing deliberative legitimacy. AI & Soc 19, 203–228 (2005). https://doi.org/10.1007/s00146-004-0313-2

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