Argument technology

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Argument technology is a sub-field of collective intelligence and artificial intelligence that focuses on applying computational techniques to the creation, identification, analysis, navigation, evaluation and visualisation of arguments and debates.

Contents

In the 1980s and 1990s, philosophical theories of arguments in general, and argumentation theory in particular, were leveraged to handle key computational challenges, such as modeling non-monotonic and defeasible reasoning and designing robust coordination protocols for multi-agent systems. [1] At the same time, mechanisms for computing semantics of Argumentation frameworks were introduced as a way of providing a calculus of opposition for computing what it is reasonable to believe in the context of conflicting arguments. [2]

With these foundations in place, the area was kick-started by a workshop held in the Scottish Highlands in 2000, the result of which was a book coauthored by philosophers of argument, rhetoricians, legal scholars and AI researchers. [3] Since then, the area has been supported by various dedicated events such as the International Workshop on Computational Models of Natural Argument (CMNA) [4] which has run annually since 2001; the International Workshop on Argument in Multi Agent Systems (ArgMAS) annually since 2004; the Workshop on Argument Mining, [5] annually since 2014, and the Conference on Computational Models of Argument (COMMA), [6] biennially since 2006. Since 2010, the field has also had its own journal, Argument & Computation, which was published by Taylor & Francis until 2016 [7] and since then by IOS Press. [8]

One of the challenges that argument technology faced was a lack of standardisation in the representation and underlying conception of argument in machine readable terms. Many different software tools for manual argument analysis, in particular, developed idiosyncratic and ad hoc ways of representing arguments which reflected differing underlying ways of conceiving of argumentative structure. [9] This lack of standardisation also meant that there was no interchange between tools or between research projects, and little re-use of data resources that were often expensive to create. To tackle this problem, the Argument Interchange Format [10] set out to establish a common standard that captured the minimal common features of argumentation which could then be extended in different settings.

Since about 2018, argument technology has been growing rapidly, with, for example, IBM's Grand Challenge, Project Debater, results for which were published in Nature in March 2021; [11] German research funder, DFG's nationwide research programme on Robust Argumentation Machines, RATIO, [12] begun in 2019; and UK nationwide deployment of The Evidence Toolkit by the BBC in 2019. [13] A 2021 video narrated by Stephen Fry provides a summary of the societal motivations for work in argument technology. [14]

Argument technology has applications in a variety of domains, including education, healthcare, policy making, political science, intelligence analysis and risk management and has a variety of sub-fields, methodologies and technologies. [15]

Technologies

Argument assistant

An argument assistant is a software tool which support users when writing arguments. Argument assistants can help users compose content, review content from one other, including in dialogical contexts. In addition to Web services, such functionalities can be provided through the plugin architectures of word processor software or those of Web browsers. Internet forums, for instance, can be greatly enhanced by such software tools and services.

Argument blogging

ArguBlogging is software which allows its users to select portions of hypertext on webpages in their Web browsers and to agree or disagree with the selected content, posting their arguments to their blogs with linked argument data. [16] It is implemented as a bookmarklet, adding functionality to Web browsers and interoperating with blogging platforms such as Blogger and Tumblr. [16]

Argument mapping

Kialo debate tree schema with an example path through it: all Con-argument boxes and some Pros were emptied to illustrate an example path. Structured online debate - Kialo debate tree.png
Kialo debate tree schema with an example path through it: all Con-argument boxes and some Pros were emptied to illustrate an example path.

Argument maps are visual, diagrammatic representations of arguments. Such visual diagrams facilitate diagrammatic reasoning and promote one's ability to grasp and to make sense of information rapidly and readily. Argument maps can provide structured, semi-formal frameworks for representing arguments using interactive visual language. One avenue of research and development is the design of online platforms to leverage collective intelligence to populate such maps and to integrate data, optimize and assess arguments.

Argument mining

Argument mining, or argumentation mining, is a research area within the natural language processing field. The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs.

An argument search engine is a search engine that is given a topic as a user query and returns a list of arguments for and against the topic [18] [19] or about that topic. [20] Such engines could be used to support informed decision-making or to help debaters prepare for debates.

Automated argumentative essay scoring

The goal of automated argumentative essay scoring systems is to assist students in improving their writing skills by measuring the quality of their argumentative content. [21] [22]

Debate technology

Structured debates from platforms like Kialo could be used for "artificial deliberative agents" (ADAs) or computational reasoning. Intelligent assistant for argumentative support and arguments inquiry.png
Structured debates from platforms like Kialo could be used for "artificial deliberative agents" (ADAs) or computational reasoning.
Example of an ADA contributing missing information to a debate via crawled Kialo data and selected based on the prior conversation and crawled argument weight ratings. Basic design of artificial deliberative agents (ADAs) for argumentation.png
Example of an ADA contributing missing information to a debate via crawled Kialo data and selected based on the prior conversation and crawled argument weight ratings.

Debate technology focuses on human-machine interaction and in particular providing systems that support, monitor and engage in debate. One of the most high-profile examples of debating technology is IBM's Project Debater [11] which combines scripted communication with very large-scale processing of news articles to identify and construct arguments on the fly in a competitive debating setting. Debating technology also encompasses tools aimed at providing insight into debates, typically using techniques from data science. These analytics have been developed in both academic [25] and commercial [26] settings.

Decision support system

Argument technology can reduce both individual and group biases and facilitate more accurate decisions. Argument-based decision support systems do so by helping users to distinguish between claims and the evidence supporting them, and express their confidence in and evaluate the strength of evidence of competing claims. [27] They have been used to improve predictions of housing market trends, [27] risk analysis, [28] ethical and legal decision making.

Ethical decision support system

An ethical decision support system is a decision support system which supports users in moral reasoning and decision-making. [29] [30]

A legal decision support system is a decision support system which supports users in legal reasoning and decision-making.

Explainable artificial intelligence

An explainable or transparent artificial intelligence system is an artificial intelligence system whose actions can be easily understood by humans.

Intelligent tutoring system

An intelligent tutoring system is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. The intersection of argument technology and intelligent tutoring systems includes computer systems which aim to provide instruction in: critical thinking, argumentation, [31] ethics, [32] law, [33] mathematics, [34] and philosophy.

A legal expert system is a domain-specific expert system that uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law.

Machine ethics

Machine ethics is a part of the ethics of artificial intelligence concerned with the moral behavior of artificially intelligent beings. As humans argue with respect to morality and moral behavior, argument can be envisioned as a component of machine ethics systems and moral reasoning components.

Proof assistant

In computer science and mathematical logic, a proof assistant or interactive theorem prover is a software tool to assist with the development of formal proofs by human-machine collaboration. This involves some sort of interactive proof editor, or other interface, with which a human can guide the search for proofs, the details of which are stored in, and some steps provided by, a computer.

Ethical considerations

Ethical considerations of argument technology include privacy, transparency, societal concerns, and diversity in representation. These factors cut across different levels such as technology, user interface design, user, service context, and society. [35] There is concern about unethical misuse for "generating arguments on controversial topics with specific stances and deploying them on social platforms". [36] Another issue may concern the design of conclusion-making algorithms, such as e.g. enabling such to conclude that certain key data is needed instead of only making lists of best-fit conclusions or enabling the generation of multiple conclusions from the same data based on different argument-assessments or assessment methods.

Related Research Articles

Persuasive technology is broadly defined as technology that is designed to change attitudes or behaviors of the users through persuasion and social influence, but not necessarily through coercion. Such technologies are regularly used in sales, diplomacy, politics, religion, military training, public health, and management, and may potentially be used in any area of human-human or human-computer interaction. Most self-identified persuasive technology research focuses on interactive, computational technologies, including desktop computers, Internet services, video games, and mobile devices, but this incorporates and builds on the results, theories, and methods of experimental psychology, rhetoric, and human-computer interaction. The design of persuasive technologies can be seen as a particular case of design with intent.

Computational semiotics is an interdisciplinary field that applies, conducts, and draws on research in logic, mathematics, the theory and practice of computation, formal and natural language studies, the cognitive sciences generally, and semiotics proper. The term encompasses both the application of semiotics to computer hardware and software design and, conversely, the use of computation for performing semiotic analysis. The former focuses on what semiotics can bring to computation; the latter on what computation can bring to semiotics.

<span class="mw-page-title-main">WIMP (computing)</span> Style of human-computer interaction

In human–computer interaction, WIMP stands for "windows, icons, menus, pointer", denoting a style of interaction using these elements of the user interface. Other expansions are sometimes used, such as substituting "mouse" and "mice" for menus, or "pull-down menu" and "pointing" for pointer.

<span class="mw-page-title-main">Argumentation theory</span> Study of how conclusions are reached through logical reasoning; one of four rhetorical modes

Argumentation theory, or argumentation, is the interdisciplinary study of how conclusions can be supported or undermined by premises through logical reasoning. With historical origins in logic, dialectic, and rhetoric, argumentation theory includes the arts and sciences of civil debate, dialogue, conversation, and persuasion. It studies rules of inference, logic, and procedural rules in both artificial and real-world settings.

<span class="mw-page-title-main">Argument map</span> Visual representation of the structure of an argument

An argument map or argument diagram is a visual representation of the structure of an argument. An argument map typically includes the key components of the argument, traditionally called the conclusion and the premises, also called contention and reasons. Argument maps can also show co-premises, objections, counterarguments, rebuttals, and lemmas. There are different styles of argument map but they are often functionally equivalent and represent an argument's individual claims and the relationships between them.

In computer science, an anytime algorithm is an algorithm that can return a valid solution to a problem even if it is interrupted before it ends. The algorithm is expected to find better and better solutions the longer it keeps running.

<span class="mw-page-title-main">Issue-based information system</span> Argumentation scheme

The issue-based information system (IBIS) is an argumentation-based approach to clarifying wicked problems—complex, ill-defined problems that involve multiple stakeholders. Diagrammatic visualization using IBIS notation is often called issue mapping.

<span class="mw-page-title-main">Eric Horvitz</span> American computer scientist, and Technical Fellow at Microsoft

Eric Joel Horvitz is an American computer scientist, and Technical Fellow at Microsoft, where he serves as the company's first Chief Scientific Officer. He was previously the director of Microsoft Research Labs, including research centers in Redmond, WA, Cambridge, MA, New York, NY, Montreal, Canada, Cambridge, UK, and Bangalore, India.

Machine ethics is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents. Machine ethics differs from other ethical fields related to engineering and technology. Machine ethics should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with the grander social effects of technology.

A legal expert system is a domain-specific expert system that uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law. Legal expert systems employ a rule base or knowledge base and an inference engine to accumulate, reference and produce expert knowledge on specific subjects within the legal domain.

<span class="mw-page-title-main">Glossary of artificial intelligence</span> List of definitions of terms and concepts commonly used in the study of artificial intelligence

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<span class="mw-page-title-main">Explainable artificial intelligence</span> AI in which the results of the solution can be understood by humans

Explainable AI (XAI), also known as Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the reasoning behind decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.

Animal–Computer Interaction (ACI) is a field of research for the design and use of technology with, for and by animals covering different kinds of animals from wildlife, zoo and domesticated animals in different roles. It emerged from, and was heavily influenced by, the discipline of Human–computer interaction (HCI). As the field expanded, it has become increasingly multi-disciplinary, incorporating techniques and research from disciplines such as artificial intelligence (AI), requirements engineering (RE), and veterinary science.

Argument mining, or argumentation mining, is a research area within the natural-language processing field. The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs. Such argumentative structures include the premise, conclusions, the argument scheme and the relationship between the main and subsidiary argument, or the main and counter-argument within discourse. The Argument Mining workshop series is the main research forum for argument mining related research.

<span class="mw-page-title-main">Artificial intelligence art</span> Machine application of knowledge of human aesthetic expressions

Artificial intelligence art is any artwork, particularly images and musical compositions, created through the use of artificial intelligence (AI) programs, such as text-to-image models and musical generators. It is sometimes confused with digital art. While both AI art and digital art involve the use of technology, AI art is characterized by its use of generative algorithms and deep learning techniques that can autonomously produce art without direct input from human artists.

<span class="mw-page-title-main">Bruce M. McLaren</span> American researcher, academic and author (born 1959)

Bruce Martin McLaren is an American researcher, scientist and author. He is an Associate Research Professor at Carnegie Mellon University and a former President of the International Artificial Intelligence in Education Society (2017-2019).

<span class="mw-page-title-main">Hanna Wallach</span> Computational social scientist

Hanna Wallach is a computational social scientist and partner research manager at Microsoft Research. Her work makes use of machine learning models to study the dynamics of social processes. Her current research focuses on issues of fairness, accountability, transparency, and ethics as they relate to AI and machine learning.

Human-AI collaboration is the study of how humans and artificial intelligence (AI) agents work together to accomplish a shared goal. AI systems can aid humans in everything from decision making tasks to art creation. Examples of collaboration include medical decision making aids., hate speech detection, and music generation. As AI systems are able to tackle more complex tasks, studies are exploring how different models and explanation techniques can improve human-AI collaboration.

Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.

<span class="mw-page-title-main">Kialo</span>

Kialo is an online structured debate platform with argument maps in the form of debate trees. It is a collaborative reasoning tool for thoughtful discussion, understanding different points of view, and collaborative decision-making, showing arguments for and against claims underneath user-submitted theses or questions.

References

  1. ↑ van Eemeren, Frans H.; Garssen, Bart; Krabbe, Erik C. W.; Snoeck Henkemans, A. Francisca; Verheij, Bart; Wagemans, Jean H. M. (2014), van Eemeren, Frans H.; Garssen, Bart; Krabbe, Erik C. W.; Snoeck Henkemans, A. Francisca (eds.), "Argumentation and Artificial Intelligence", Handbook of Argumentation Theory, Dordrecht: Springer Netherlands, pp. 615–675, doi:10.1007/978-90-481-9473-5_11, ISBN   978-90-481-9473-5 , retrieved 2021-12-08
  2. ↑ Prakken, Henry (2014). "The ASPIC+ framework for structured argumentation: a tutorial". Argument & Computation. 5 (1): 31–62. doi: 10.1080/19462166.2013.869766 .
  3. ↑ Reed, C. & Norman, T.J. (eds) Argumentation Machines. Kluwer, 2003.
  4. ↑ "Computational Models of Natural Argument". www.cmna.info.
  5. ↑ For example: "Proceedings of the 6th Workshop on Argument Mining – ACL Anthology". www.aclweb.org. Association for Computational Linguistics. August 2019. Retrieved 7 December 2020.
  6. ↑ "Computational Models of Argument conference series". www.comma-conf.org.
  7. ↑ "Journal of Argument & Computation". www.tandf.co.uk. Archived from the original on 2012-02-21.
  8. ↑ "Journal of Argument & Computation". www.iospress.nl.
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  10. ↑ Rahwan, Iyad; Reed, Chris (2009), Simari, Guillermo; Rahwan, Iyad (eds.), "The Argument Interchange Format", Argumentation in Artificial Intelligence, Boston, MA: Springer US, pp. 383–402, Bibcode:2009aai..book..383R, doi:10.1007/978-0-387-98197-0_19, hdl: 21.11116/0000-0002-CA71-B , ISBN   978-0-387-98197-0 , retrieved 2021-12-08
  11. 1 2 Slonim, Noam; Bilu, Yonatan; Alzate, Carlos; Bar-Haim, Roy; Bogin, Ben; Bonin, Francesca; Choshen, Leshem; Cohen-Karlik, Edo; Dankin, Lena; Edelstein, Lilach; Ein-Dor, Liat (March 2021). "An autonomous debating system". Nature. 591 (7850): 379–384. Bibcode:2021Natur.591..379S. doi:10.1038/s41586-021-03215-w. ISSN   1476-4687. PMID   33731946. S2CID   232305184.
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  26. ↑ For example: "Kialo". www.kialo.com. Retrieved 11 August 2020.
  27. 1 2 Introne, Joshua; Iandoli, Luca (2014-08-01). "Improving decision-making performance through argumentation: An argument-based decision support system to compute with evidence". Decision Support Systems. 64: 79–89. doi:10.1016/j.dss.2014.04.005. ISSN   0167-9236.
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