=Paper=
{{Paper
|id=Vol-2151/Paper_S8
|storemode=property
|title=Towards Argumentative Dialogue as a Humane Interface between People and Intelligent Machines
|pdfUrl=https://ceur-ws.org/Vol-2151/Paper_S8.pdf
|volume=Vol-2151
|authors=Simon Wells
|dblpUrl=https://dblp.org/rec/conf/sicsa/Wells18
}}
==Towards Argumentative Dialogue as a Humane Interface between People and Intelligent Machines==
https://ceur-ws.org/Vol-2151/Paper_S8.pdf
Towards Argumentative Dialogue as a Humane
Interface between People and Intelligent
Machines
Simon Wells1[0000−0003−4512−7868]
Edinburgh Napier University, Edinburgh, United Kingdom. s.wells@napier.ac.uk
http://www.simonwells.org
Abstract. We sketch the framework for a theoretical and applied sys-
tem that we are developing which uses argumentation schemes and dia-
logue games to support dialectical interaction between people and ma-
chine learning systems. The goal is to support the automated justifica-
tion and explanation of decisions made by AI systems, through a natural,
human-oriented interface, in response to contemporary societal concerns
about the impact of AI decisions upon individuals.
Keywords: Artificial Intelligence · Machine Learning · Explainable AI
· Argumentation · Dialogue · Explanation · Justification
1 Introduction
Decisions are increasingly made by intelligent machines. Whilst many of these
decisions are operational, for example trading decisions made by financial tech-
nology (FinTech) software, the A.I. wavefront, constituting the range of problems
that can be decided by machines, is accelerating. It is likely that coming years will
bring new decision making machines acting within at least the Banking, Medical,
Insurance, Legal, and Transport sectors. Sectors that are heavily regulated and
in which there is frequently a legal mandate that the output of predictive models,
and the decisions that follow from them, can be scrutinised and interpreted. Tra-
ditionally the trade-off has been towards simpler linear predictive models, sac-
rificing some accuracy for simplicity and hence understandability of the model.
However this has changed in recent years as findings in Machine Learning (ML)
[1] and Deep Learning (DL) [2] have produced advances in capability but with
the expense of increased complexity and reduced understandability.
When interpretation of results is difficult and understandability of the model
is poor there is a consequent barrier to both responsible and legal use of those
systems as well as wider, more general acceptance of the systems. There is also
the additional risk that a form of “moral outsourcing” occurs in which operators
of intelligent systems derogate from their responsibilities as a consequence of
delegating to the machine, despite people legally remaining the “problem hold-
ers”. Additionally, the vocal positions of a number of public commentators has
fed both a popular and a legislative wariness of A.I. This has lead to, perhaps
2 S. Wells
premature, moves towards legal regulation of intelligent machines, autonomous
robots, and ML algorithms. Many of those calling for legislation, and many of
the drafted regulations, propose explainable A.I.; systems that can generate ex-
planations for their decisions in concert with the search for solutions. However
this is not a capability of the intelligent systems that are at stake.
Conversely, techniques such as argumentative dialogue, are human friendly in
a way that ML and DL based techniques are not. People communicate with each
other to share knowledge using language, they argue in order to persuade oth-
ers, they present arguments to justify their positions, they engage in dialogues
to explore alternative positions, and they do these things frequently throughout
their lives. Argumentative dialogue is thus a primary interface between people,
and possesses attributes that are required for the interface between people and
intelligent machines. Approaches to computational argumentation includes tech-
niques for reasoning defeasibly about knowledge, and for managing various types
of dialogue between arguers.
We propose that the explanatory role within A.I. should be taken up by
the application of computational argument. Specifically that argumentative di-
alogue has the potential not merely to be a human computer interface but to
be a humane interface between people and intelligent machines. Argumentative
dialogue is a unifying technology that can enable intelligent machines not only
to explain but also to justify their decisions and, when necessary, to select those
arguments necessary to persuade others that the decision made was right under
the circumstances. In this paper we demonstrate how the construction of such
a humane interface draws together computational argument research at the log-
ical, dialectical, procedural, and heuristic layers and applies it to the problem
of understanding, explaining, and justifying decision making within intelligent
machines based upon differing formalisms.
In this paper we present an overview of research to bring together increas-
ingly capable ML techniques with increasingly sophisticated human-oriented,
dialogue-based interfaces.
2 A Dual Process Model of Explanation
Dual process models have been used in psychology for many years to account
for the multiple ways in which thinking happens. Whilst various authors have
delineated the boundary between types of reasoning in a variety of ways, the
basic split is between unconscious, automatic reasoning processes as exemplified
by the majority of problems that are solved by the current generation of ML sys-
tems and conscious, controlled reasoning processes, the type of slow, rule-based,
explicit, language linked logical reasoning that is exemplified by traditional sym-
bolic AI, targeting domains such as natural language, or strategic planning. This
approach accounts partly for the problem of explainable AI. Great advances have
been made in solving problems associated with unconscious reasoning processes,
but we conjecture that human acceptance of the results is often dependent upon
conscious reasoning, an explanation of the reasoning in terms that people would
Argumentative Dialogue as a Humane Interface 3
usually use to explain their own behaviour to others. Furthermore, people often
don’t accept the base explanations for another’s behaviour and require a defense
of the behaviour at issue. Such verbal interactions, where the interlocutors may
choose the right thing to say in order to persuade their opponent, are inherently
strategic, and thus explainable AI systems, when acting in human society, will
often need to go beyond mere explanation, to justify their behaviour given the
context in which it occurs. To differentiate this specific subset of dialogical inter-
actions, we refer to such Justificatory and Explanatory Techniques for Dialogues
as “JET Dialogues”.
Clearly a link between unconscious, ML-based, AI systems, and conscious rea-
soning processes to support JET dialogues is required. We propose that ar-
gumentation schemes [4], a mechanism for cataloging, relating, and criticising
instances of reasoning, provide this link. Argumentation Schemes are formali-
sations of stereotypical patterns of reasoning, primarily as expressed in natural
argumentative language. For example, the Argumentation Scheme for the Argu-
ment from Sign is schematized as follows:
Specific Premise: A (a finding) is true in this situation.
General Premise: B is generally indicated as true when its sign, A, is true.
Conclusion: B is true in this situation.
Schemes capture the idea that whilst the expression of a given instance of
reasoning is often unique to the situation in which it occurs, the framework
of expression, how the data (premises) are marshalled and linked to the result
(conclusion), frequently fits into a finite range of possibilities. Furthermore, some
instances of fallacious reasoning can often be equated with poor application
of a scheme. Schemes have been catalogued yielding collections of hundreds of
individual schemes[5] that refer to patterns of real-world reasoning.
One interpretation of Argumentation Schemes is that they can be used to form
a functional link between the automatic and unconscious reasoning of modern
data-driven ML systems and dialogue systems that can provide a human-oriented
interface to the, otherwise opaque, reasoning processes that are happening. The
argument for the link between schemes and ML-reasoning is straightforward,
if modern data-driven AI systems make decisions based upon input data, then
they can be described as reasoning systems. It has not been suggested that the
current generation of AI can perform reasoning tasks that go beyond the kinds of
reasoning that people do, but that they perhaps operate on different scales, for
example, making decisions faster, more rapidly, more reliably, at greater scale,
than a person could. However the actual reasoning processes are not supra-
human. Any decisions that a machine could make using such a system, and
hence any conclusions that could be drawn, are not beyond the wit of humanity
and will conform to existing patterns of reasoning. Patterns that have been
recognised and formalised as Argumentation Schemes. It follows therefore that
for each instance of ML reasoning, a corresponding argumentation scheme can
4 S. Wells
be identified and instantiated for subsequent utilisation within an interactive
dialogue system. In this approach, the inputs to the ML system correspond to
premises within an argument, the outputs to conclusions drawn, and the ML
process corresponds to a known instance of a reasoning process, identified by a
named Argumentation Scheme, as illustrated in Fig. 1.
ML System
Key
Input ML Process Output Data Flow
Argumentative Relationship
Correspondence
Boundary
Premises Reasoning Pattern Conclusion
Argumentation Scheme
Fig. 1. Correspondences & mappings between reasoning in ML systems and reasoning
archetypes described in Argumentation Schemes.
To this end we are aligning existing, identified argumentation schemes with
published ML research, using the OpenML repository [3], in order to understand
the kinds of reasoning that are achieved by current, state of the art processes.
Argumentation Schemes that represent the reasoning process that occurs within
a given ML system, together with the inputs and outputs to that system, are then
used to generate utterances for use within an argumentative dialogue system as
illustrated in Fig. 2. The system use the Dialogue Game Description Language
(DGDL) [7] to represent various kinds of explanation and justification dialogue
and dialogues are managed using A Dialogue MANagement Tool (ADAMANT),
a dialogue runtime that manages dialogical interactions according to the rules
expressed in DGDL descriptions and using the Argumentation scheme extensions
to DGDL from [6]. The figure illustrates two agents, a ML agent and one or more
other dialogue participants who interact with each other, in dialogues mediated
by ADAMANT, yielding a dialogue transcript, a record of the explanation and
justifications that have been produced.
3 Conclusions & Further Research
There are a number of threads of further work which roughly correspond to var-
ious sub-systems illustrated in Fig. 2. Firstly, mapping from ML representations
to knowledge structures. Secondly, natural language generation. Thirdly, strate-
gic and contextual personalisation of dialogue game interactions. This captures
the notion that whilst an explanation will be fairly static, a factual statement
of the reasoning process, a justification, which ideally persuades the other party,
but at least is acceptable to them, may differ depending upon the circumstances
Argumentative Dialogue as a Humane Interface 5
ML System
Input ML Process Output
Premises Reasoning Pattern Conclusion
Argumentation Scheme
updates
Agent
Knowledge ADaManT Dialogue Transcript
Database
ML Agent
Dialogue
Participant(s)
Fig. 2. Overview of data flow from ML systems into Dialogue Systems via Argumen-
tation Schemes.
in which the dialogue takes place, and the nature of the other party. Persuasive
arguments are very susceptible to the disposition, knowledge, and circumstances
of the person to whom they are directed, choosing the right argument for the
right person, strategy, is, as a result, extremely important in successful dialogical
interaction. As these research threads are drawn together, so a humane inter-
face to the underlying ML system is built enabling more rich, human-oriented
interactions to occur.
References
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http://www.deeplearningbook.org
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http://doi.acm.org/10.1145/2641190.2641198
4. Walton, D.N.: The New Dialectic. University of Toronto Press (1998)
5. Walton, D.N., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge Univer-
sity Press (2008)
6. Wells, S.: Supporting argumentation schemes in argumentative dialogue games.
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dialogue. Journal of Applied Logic 10(4), 309–329 (2012)