Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring (...) processes “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for theNew York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (shrink)

This paper addresses the problem posed by the current split between the two opposed hypotheses in the growing literature on the fallacy of begging the question the epistemic hypothesis, based on knowledge and belief, and the dialectical one, based on formal dialogue systems. In the first section, the nature of split is explained, and it is shown how each hypothesis has developed. To get the beginning reader up to speed in the literature, a number of key problematic examples are (...) analyzed illustrating how both approaches can be applied. Useful tools are brought to bear on them, including the automated argument diagramming system Araucaria, and profiles of dialogue used to represent circular argumentation in a dialogue tableau format. These tools are used to both to model circular reasoning and to provide the contextual evidence needed to properly determine whether the circular reasoning in a given case is better judged fallacious or not. A number of technical problems that have impeded the development of both hypotheses are studied. One central problem is the distinction between argument and explanation. It is concluded that the best way to move forward and solve these problems is to reformulate the two hypotheses in such a way that they might be able to co-exist. On this basis, a unified methodology is proposed that allows each hypothesis to move forward as a legitimate avenue for research using the same tools. (shrink)

With the advent of automated decision-making, governments have increasingly begun to rely on artificially intelligent algorithms to inform policy decisions across a range of domains of government interest and influence. The practice has not gone unnoticed among philosophers, worried about “algocracy”, and its ethical and political impacts. One of the chief issues of ethical and political significance raised by algocratic governance, so the argument goes, is the lack of transparency of algorithms. One of the best-known examples of philosophical analyses (...) of algocracy is John Danaher’s “The threat of algocracy”, arguing that government by algorithm undermines political legitimacy. In this paper, I will treat Danaher’s argument as a springboard for raising additional questions about the connections between algocracy, comprehensibility, and legitimacy, especially in light of empirical results about what we can expect the voters and policymakers to know. The paper has the following structure: in Sect. 2, I introduce the basics of Danaher’s argument regarding algocracy. In Sect. 3 I argue that the algocratic threat to legitimacy has troubling implications for social justice. In Sect. 4, I argue that, nevertheless, there seem to be good reasons for governments to rely on algorithmic decision support systems. Lastly, I try to resolve the apparent tension between the findings of the two preceding Sections. (shrink)

Critical thinking about international politics often involves reasoning about the beliefs, goals, appraisals, actions, and plans of actors such as countries, governments, politicians, etc. We analyzed arguments in interpretive reports about international politics, in order to develop a prototype argument diagramming tool for this domain, AVIZE (Argument Visualization and Evaluation). The purpose of AVIZE is to aid users in the construction and self-evaluation of real-world arguments in the domain of international politics. AVIZE provides a set of argument schemes as (...) cognitive building blocks for constructing argument diagrams. Most of the schemes are related to concepts from the field of automated plan recognition in artificial intelligence. While some currently available argument diagramming tools provide schemes, they are not tailored to the domain of international politics. This paper describes the argument schemes for this domain and the design of the argument diagramming tool. (shrink)

Critical thinking about international politics often involves reasoning about the beliefs, goals, appraisals, actions, and plans of actors such as countries, governments, politicians, etc. We analyzed arguments in interpretive reports about international politics, in order to develop a prototype argument diagramming tool for this domain, AVIZE (Argument Visualization and Evaluation). The purpose of AVIZE is to aid users in the construction and self-evaluation of real-world arguments in the domain of international politics. AVIZE provides a set of argument schemes as (...) cognitive building blocks for constructing argument diagrams. Most of the schemes are related to concepts from the field of automated plan recognition in artificial intelligence. While some currently available argument diagramming tools provide schemes, they are not tailored to the domain of international politics. This paper describes the argument schemes for this domain and the design of the argument diagramming tool. (shrink)

Recent developments in large language models and image generation software raise the possibility that AI systems might one day replace humans in some of the intrinsically valuable work through which humans find meaning in their lives – work like scientific and philosophical research and the creation of art. If AIs can do this work more efficiently than humans, this might make human performance of these activities pointless. This represents a threat to human wellbeing which is distinct from, and harder (...) to solve, than the automation of merely instrumentally valuable activities. In this paper I outline the problem, assess its seriousness, and investigate possible solutions. I argue that AI could reduce our incentives to perform such work, and this might result in a great deskilling of humanity. Furthermore, even if humans continue to do such work, the mere existence of AI systems would undermine its meaning and value. I critique Danaher’s (2019a) and Suits’ (1978) arguments that we should embrace the total automation of work and retreat to a ‘utopia of games’. Instead, I argue that the threat to meaning and value posed by AI gives us a prima facie reason to slow down its development. (shrink)

How should policymakers respond to the risk of technological unemployment that automation brings? First, I develop a procedure for answering this question that consults, rather than usurps, individuals’ own attitudes and ambitions towards that risk. I call this the insurance argument. A distinctive virtue of this view is that it dispenses with the need to appeal to a class of controversial reasons about the value of employment, and so is consistent with the demands of liberal political morality. Second, I appeal (...) to the insurance argument to show that governments ought not simply to provide those who are displaced by machines with unemployment benefits. Instead, it must offer re-training programmes, as well as enact more general macroeconomic policies that create new opportunities for employment. My contribution is important not only because it helps us to resolve a series of urgent policy disputes—disputes that have been discussed extensively by labour market economists and policymakers, but less so by political philosophers—but also because my analysis sheds light on more general philosophical controversies relating to risk. (shrink)

We argued [Since this argument appeared in other journals, I am reprising it here, almost verbatim.] (Fulda in J Law Info Sci 2:230–232, 1991/AI & Soc 8(4):357–359, 1994) that the paradox of the preface suggests a reason why machines cannot, will not, and should not be allowed to judge criminal cases. The argument merely shows that they cannot now and will not soon or easily be so allowed. The author, in fact, now believes that when—and only when—they are ready they (...) actually should be so allowed, in the interests of justice. Both the original argument applied and this detailed reconsideration applies exclusively to trial courts, and both specifically exclude(d) sentencing. The argument highlights some key relevant differences between minds and machines and attempts, also, to explain why automation is of far greater import for the first-level justice system (trial courts) than for higher courts. A final section discusses why sentencing was, is, and should be excluded. (shrink)

This paper presents an enhanced ontology formalization, combining previous work in Conceptual Structure Theory and Order-Sorted Logic. Most existing ontology formalisms place greater importance on concept types, but in this paper we focus on relation types, which are in essence predicates on concept types. We formalize the notion of ‘predicate of predicates’ as meta-relation type and introduce the new hierarchy of meta-relation types as part of the ontology definition. The new notion of closure of a relation or meta-relation type is (...) presented as a means to complete that relation or meta-relation type by transferring extra arguments and properties from other related types. The end result is an expanded ontology, called the closure of the original ontology, on which automated inference could be more easily performed. Our proposal could be viewed as a novel and improved ontology formalization within Conceptual Structure Theory and a contribution to knowledge representation and formal reasoning (e.g., to build a query-answering system for legal knowledge). (shrink)

Theorems in automated theorem proving are usually proved by formal logical proofs. However, there is a subset of problems which humans can prove by the use of geometric operations on diagrams, so called diagrammatic proofs. Insight is often more clearly perceived in these proofs than in the corresponding algebraic proofs; they capture an intuitive notion of truthfulness that humans find easy to see and understand. We are investigating and automating such diagrammatic reasoning about mathematical theorems. Concrete, rather than (...) general diagrams are used to prove particular concrete instances of the universally quantified theorem. The diagrammatic proof is captured by the use of geometric operations on the diagram. These operations are the inference steps of the proof. An abstracted schematic proof of the universally quantified theorem is induced from these proof instances. The constructive -rule provides the mathematical basis for this step from schematic proofs to theoremhood. In this way we avoid the difficulty of treating a general case in a diagram. One method of confirming that the abstraction of the schematic proof from the proof instances is sound is proving the correctness of schematic proofs in the meta-theory of diagrams. These ideas have been implemented in the system, called Diamond, which is presented here. (shrink)

In abstract argumentation, each argument is regarded as atomic. There is no internal structure to an argument. Also, there is no specification of what is an argument or an attack. They are assumed to be given. This abstract perspective provides many advantages for studying the nature of argumentation, but it does not cover all our needs for understanding argumentation or for building tools for supporting or undertaking argumentation. If we want a more detailed formalization of arguments (...) than is available with abstract argumentation, we can turn to structured argumentation, which is the topic of this special issue of Argument and Computation. In structured argumentation, we assume a formal language for representing knowledge and specifying how arguments and counterarguments can be constructed from that knowledge. An argument is then said to be structured in the sense that normally, the premises and claim of the argument are made explicit, and the relationship between the premises and claim is formally defined (for instance, using logical entailment). In this introduction, we provide a brief overview of the approaches covered in this special issue on structured argumentation. (shrink)

Few automated legal reasoning systems have been developed in domains of law in which a judicial decision maker has extensive discretion in the exercise of his or her powers. Discretionary domains challenge existing artificial intelligence paradigms because models of judicial reasoning are difficult, if not impossible to specify. We argue that judicial discretion adds to the characterisation of law as open textured in a way which has not been addressed by artificial intelligence and law researchers in (...) depth. We demonstrate that systems for reasoning with this form of open texture can be built by integrating rule sets with neural networks trained with data collected from standard past cases. The obstacles to this approach include difficulties in generating explanations once conclusions have been inferred, difficulties associated with the collection of sufficient data from past cases and difficulties associated with integrating two vastly different paradigms. A knowledge representation scheme based on the structure of arguments proposed by Toulmin has been used to overcome these obstacles. The system, known as Split Up, predicts judicial decisions in property proceedings within family law in Australia. Predictions from the system have been compared to those from a group of lawyers with favourable results. (shrink)

As automation in artificial intelligence is increasing, we will need to automate a growing amount of ethical decision making. However, ethical decision- making raises novel challenges for engineers, ethicists and policymakers, who will have to explore new ways to realize this task. The presented work focuses on the development and formalization of models that aim at ensuring a correct ethical behaviour of artificial intelligent agents, in a provable way, extending and implementing a logic-based proving calculus that is based on (...) class='Hi'>argumentation reasoning with support and attack arguments. This leads to a formal theoretical framework of ethical competence that could be implemented in artificial intelligent systems in order to best formalize certain parameters of ethical decision-making to ensure safety and justified trust. (shrink)

This paper takes an argumentation approach to find the place of trust in a method for evaluating arguments from expert opinion. The method uses the argumentation scheme for argument from expert opinion along with its matching set of critical questions. It shows how to use this scheme in three formal computational argumentation models that provide tools to analyse and evaluate instances of argument from expert opinion. The paper uses several examples to illustrate the use of these tools. (...) A conclusion of the paper is that from an argumentation point of view, it is better to critically question arguments from expert opinion than to accept or reject them based solely on trust. (shrink)

In this paper, we use a pseudo-algorithmic procedure for assessing an AI-generated text. We apply the Comprehensive Assessment Procedure for Natural Argumentation (CAPNA) in evaluating the arguments produced by an Artificial Intelligence text generator, GPT-3, in an opinion piece written for the Guardian newspaper. The CAPNA examines instances of argumentation in three aspects: their Process, Reasoning and Expression. Initial Analysis is conducted using the Argument Type Identification Procedure (ATIP) to establish, firstly, that an argument is present and, (...) secondly, its specific type in terms of the argument classification framework of the Periodic Table of Arguments (PTA). Procedural Questions are then used to test the acceptability of the argument in each of the three aspects. The analysis shows that while the arguments put forward by the AI text generator are varied in terms of their type and follow familiar patterns of human reasoning, they contain obvious weaknesses. From this we can conclude that the automated generation of persuasive, well-reasoned argumentation is a far more difficult task than the generation of meaningful language, and that if AI systems producing arguments are to be persuasive, they require a method of checking the plausibility of their own output. (shrink)

A simplified variant of Gödel’s ontological argument is presented. The simplified argument is valid already in basic modal logics K or KT, it does not suffer from modal collapse, and it avoids the rather complex predicates of essence (Ess.) and necessary existence (NE) as used by Gödel. The variant presented has been obtained as a side result of a series of theory simplification experiments conducted in interaction with a modern proof assistant system. The starting point for these experiments was the (...) computer encoding of Gödel’s argument, and then automated reasoning techniques were systematically applied to arrive at the simplified variant presented. The presented work thus exemplifies a fruitful human-computer interaction in computational metaphysics. Whether the presented result increases or decreases the attractiveness and persuasiveness of the ontological argument is a question I would like to pass on to philosophy and theology. (shrink)

In this paper we describe AGATHA, a program designed to automate the process of theory construction in case based domains. Given a seed case and a number of precedent cases, the program uses a set of argument moves to generate a search space for a dialogue between the parties to the dispute. Each move is associated with a set of theory constructors, and thus each point in the space can be associated with a theory intended to explain the seed case (...) and the other cases in the domain. The space is large and so an heuristic search method is needed. This paper describes two methods based on A* and alpha/beta pruning and also a series of experiments designed to explore the appropriateness of different evaluation functions, the most useful precedents to use as seed cases and the quality of the resulting theories. (shrink)

We report on the mechanisation of (preference-based) conditional normative reasoning. Our focus is on Åqvist's system E for conditional obligation, and its extensions. Our mechanisation is achieved via a shallow semantical embedding in Isabelle/HOL. We consider two possible uses of the framework. The first one is as a tool for meta-reasoning about the considered logic. We employ it for the automated verification of deontic correspondences (broadly conceived) and related matters, analogous to what has been previously achieved for (...) the modal logic cube. The equivalence is automatically verified in one direction, leading from the property to the axiom. The second use is as a tool for assessing ethical arguments. We provide a computer encoding of a well-known paradox (or impossibility theorem) in population ethics, Parfit's repugnant conclusion. While some have proposed overcoming the impossibility theorem by abandoning the presupposed transitivity of ‘better than’, our formalisation unveils a less extreme approach, suggesting among other things the option of weakening transitivity suitably rather than discarding it entirely. Whether the presented encoding increases or decreases the attractiveness and persuasiveness of the repugnant conclusion is a question we would like to pass on to philosophy and ethics. (shrink)

Persuasive technologies can adopt several strategies to change the attitudes and behaviors of their users. In this work I synthesize the lessons learned from three empirical case studies on automated persuasion that have been carried out in the last decade in the contexts of: persuasive news recommendations, social robotics, and e-commerce, respectively. In particular, such studies have assessed, in the technological domain, the effects of nudging techniques relying on well known persuasive argumentation schemas and on framing strategies. In (...) discussing the main findings, I will argue that the obtained persuasive effects are due to the fact that such techniques leverage on cognitive mechanisms that refer to the “system 1” types of automatic processes hypothesized in the context of the dual process theory of reasoning. As a consequence of this state of affairs, any automated persuasive systems (used for ethical purposes: e.g. suggesting good health habits) should be able to re-use these types of system 1 strategies in order to gradually take the users in an argumentation territory where system 2 processes can take place. -/- . (shrink)

This paper critically assesses the possibility of moral enhancement with ambient intelligence technologies and artificial intelligence presented in Savulescu and Maslen (2015). The main problem with their proposal is that it is not robust enough to play a normative role in users’ behavior. A more promising approach, and the one presented in the paper, relies on an artifi-cial moral reasoning engine, which is designed to present its users with moral arguments grounded in first-order normative theories, such as Kantianism or (...) utilitarianism, that reason-responsive people can be persuaded by. This proposal can play a normative role and it is also a more promising avenue towards moral enhancement. It is more promising because such a system can be designed to take advantage of the sometimes undue trust that people put in automated technologies. We could therefore expect a well-designed moral reasoner system to be able to persuade people that may not be persuaded by similar arguments from other people. So, all things considered, there is hope in artificial intelli-gence for moral enhancement, but not in artificial intelligence that relies solely on ambient intelligence technologies. (shrink)

This study explores the integration of advanced language models (LLMs) with legal argumentation processes, aiming to address a question posed by Walton in 2004: the potential of argumentation methods to identify the best arguments for supporting or refuting a claim. By leveraging the capabilities of LLMs, we demonstrate the practical application of argumentation generation methods in the legal domain, marking a shift from traditional retrieval-focused tasks to generation-focused endeavors. Our methodology diverges from existing literature by emphasizing the (...) development of defeasible legal arguments, suitable for a hypothesis reduction process, rather than merely crafting effective rebuttals. Utilizing a Retrieval-Augmented Generation-based system, our approach simulates reliable legal argument generation by drawing from similar case data, thereby creating a network of interconnected arguments. These networks are formalized into argumentation schemes to compute argument acceptance and facilitate decision-making. To validate ALEX (Argumentation system for Legal Explanation), we conducted an extensive comparative study with state-of-the-art LLMs, including a fine-tuned model, evaluating the quality of generated arguments against original judicial decisions. Additionally, an expert survey evaluated the accuracy of case analysis and the practical alignment of generated arguments with real-world requirements, providing valuable insights into the system’s performance. Our research also includes a detailed case analysis, tracing the argumentation generation process step by step, and providing recommendations for future improvements. This study showcases ALEX’s capacity to improve legal analysis through automated argumentation, enhanced argument quality, and the structuring of arguments into formal frameworks, with demonstrated benefits in real-world applications. By integrating traditional legal practices with advanced LLM capabilities, ALEX refines legal reasoning and establishes a solid foundation for the evolution of automated legal argumentation systems. (shrink)

There is a driving need computationally to interrogate large bodies of text for a range of non-denotative meaning (e.g., to plot chains of reasoning, detect sentiment, diagnose genre, and so forth). But such meaning has always proven computationally allusive. It is often implicit, ‘hidden’ meaning, evoked by linguistic cues, stylistic arrangement, or conceptual structure – features that have hitherto been difficult for Natural Language Processing systems to recognize and use. Non-denotative textual effects are the historical concern of rhetorical (...) studies, and we have turned to rhetoric in order to find new ways to advance NLP, especially for sophisticated tasks like Argument Mining. This paper highlights certain rhetorical devices that encode levels of meaning that have been overlooked in Computational Linguistics generally and Argument Mining particularly, and yet lend themselves to automated detection. These devices are the linguistic configurations known as Rhetorical Figures. We argue for the importance of these devices for Argument Mining, especially in collocations, and we present an XML annotation scheme for Rhetorical Figures to make figuration more tractable for computational approaches, particularly with an eye on the improvements they offer Argument Mining. We also discuss the intellectual and technical challenges involved in figure annotation and the implications for Machine Learning. (shrink)

There is a driving need computationally to interrogate large bodies of text for a range of non-denotative meaning (e.g., to plot chains of reasoning, detect sentiment, diagnose genre, and so forth). But such meaning has always proven computationally allusive. It is often implicit, ‘hidden’ meaning, evoked by linguistic cues, stylistic arrangement, or conceptual structure – features that have hitherto been difficult for Natural Language Processing systems to recognize and use. Non-denotative textual effects are the historical concern of rhetorical (...) studies, and we have turned to rhetoric in order to find new ways to advance NLP, especially for sophisticated tasks like Argument Mining. This paper highlights certain rhetorical devices that encode levels of meaning that have been overlooked in Computational Linguistics generally and Argument Mining particularly, and yet lend themselves to automated detection. These devices are the linguistic configurations known as Rhetorical Figures. We argue for the importance of these devices for Argument Mining, especially in collocations, and we present an XML annotation scheme for Rhetorical Figures to make figuration more tractable for computational approaches, particularly with an eye on the improvements they offer Argument Mining. We also discuss the intellectual and technical challenges involved in figure annotation and the implications for Machine Learning. (shrink)

Many seem to think that AI-induced responsibility gaps are morally bad and therefore ought to be avoided. We argue, by contrast, that there is at least a pro tanto reason to welcome responsibility gaps. The central reason is that it can be bad for people to be responsible for wrongdoing. This, we argue, gives us one reason to prefer automated decision-making over human decision-making, especially in contexts where the risks of wrongdoing are high. While we are not the first (...) to suggest that responsibility gaps should sometimes be welcomed, our argument is novel. Others have argued that responsibility gaps should sometimes be welcomed because they can reduce or eliminate the psychological burdens caused by tragic moral choice-situations. By contrast, our argument explains why responsibility gaps should sometimes be welcomed even in the absence of tragic moral choice-situations, and even in the absence of psychological burdens. (shrink)