research-article

Introduction to the Special Issue on Human Interaction with Artificial Advice Givers

Authors:

John O'donovan,

Alexander FelfernigAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 6, Issue 4

Article No.: 26, Pages 1 - 12

https://doi.org/10.1145/3014432

Published: 26 December 2016 Publication History

Abstract

Many interactive systems in today’s world can be viewed as providing advice to their users. Commercial examples include recommender systems, satellite navigation systems, intelligent personal assistants on smartphones, and automated checkout systems in supermarkets. We will call these systems that support people in making choices and decisions artificial advice givers (AAGs): They propose and evaluate options while involving their human users in the decision-making process. This special issue addresses the challenge of improving the interaction between artificial and human agents. It answers the question of how an agent of each type (human and artificial) can influence and understand the reasoning, working models, and conclusions of the other agent by means of novel forms of interaction. To address this challenge, the articles in the special issue are organized around three themes: (a) human factors to consider when designing interactions with AAGs (e.g., over- and under-reliance, overestimation of the system’s capabilities), (b) methods for supporting interaction with AAGs (e.g., natural language, visualization, and argumentation), and (c) considerations for evaluating AAGs (both criteria and methodology for applying them).

References

[1]

Jae-wook Ahn, Peter Brusilovsky, Jonathan Grady, Daqing He, and Sue Yeon Syn. 2007. Open user profiles for adaptive news systems: Help or harm? In Proceedings of the International Conference on World Wide Web. ACM, 11--20.

[2]

Xavier Amatriain, Alejandro Jaimes, Nuria Oliver, and Josep M. Pujol. 2011. Data mining methods for recommender systems. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B. Kantor (Eds.). Springer, Berlin, 39--71.

[3]

Katie Atkinson and Trevor Bench-Capon. 2007. Practical reasoning as presumptive argumentation using action based alternating transition systems. Artif. Intell. 171, 10 (2007), 855--874.

Digital Library

[4]

Eytan Bakshy, Solomon Messing, and Lada A. Adamic. 2015. Exposure to ideologically diverse news and opinion on Facebook. Science 348, 6239 (2015), 1130--1132.

[5]

Tara S. Behrend and Lori Foster Thompson. 2011. Similarity effects in online training: Effects with computerized trainer agents. Comput. Hum. Behav. 27, 3 (2011), 1201--1206.

Digital Library

[6]

Sharon Wraith Bennett and A. Carlisle Scott. 1985. The Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley Publishing Company, Chapter 19, 363--370.

[7]

Timothy Bickmore and Justine Cassell. 2001. Relational agents: A model and implementation of building user trust. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 396--403.

Digital Library

[8]

Peter Brusilovsky, Elmar Schwarz, and Gerhard Weber. 1996. ELM-ART: An intelligent tutoring system on world wide web. In Intelligent Tutoring Systems. 261--269.

[9]

John M. Carroll and Jean McKendree. 1987. Interface design issues for advice-giving expert systems. Commun. ACM 30, 1 (1987), 14--32.

Digital Library

[10]

Federico Cerutti. 2011. Decision support through argumentation-based practical reasoning. In Proceedings of the International Joint Conference on Artificial Intelligence, Vol. 22. 2786--2787.

[11]

Federico Cerutti, Nava Tintarev, and Nir Oren. 2014. Formal arguments, preferences, and natural language interfaces to humans: An empirical evaluation. In Proceedings of the European Conference on Artificial Intelligence. 207--212.

[12]

Leigh Clark, Abdulmalik Ofemile, Svenja Adolphs, and Tom Rodden. 2016. A multimodal approach to assessing user experiences with agent helpers. ACM Trans. Interact. Intell. Syst. 6, 4 (2016), Article 29.

Digital Library

[13]

Andrew J. Cowell and Kay M. Stanney. 2005. Manipulation of non-verbal interaction style and demographic embodiment to increase anthropomorphic computer character credibility. Int. J. Hum.-Comput. Stud. 62, 2 (2005), 281--306.

Digital Library

[14]

Vania Dimitrova. 2003. STyLE-OLM: Interactive open learner modelling. Int. J. Artif. Intell. Educ. 17, 2 (2003), 35--78.

[15]

Alan Dix. 2016. Human-like computing and human-computer interaction. In Human Centred Design for Intelligent Environments (HCD4IE) Workshop at the British Human Computer Interaction Conference.

Digital Library

[16]

EPSRC. 2016. Report of human-like computing workshop. Engineering and Physical Sciences Research Council.

[17]

David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya A. Kalyanpur, Adam Lally, J. William Murdock, Eric Nyberg, John Prager, and others. 2010. Building Watson: An overview of the DeepQA project. AI Mag. 31, 3 (2010), 59--79.

Digital Library

[18]

David A. Ferrucci. 2012. Introduction to “this is Watson.” IBM J. Res. Dev. 56, 3.4 (2012), 1--1.

Digital Library

[19]

Adam D. Galinsky and Thomas Mussweiler. 2001. First offers as anchors: The role of perspective-taking and negotiator focus. J. Person. Soc. Psychol. 81, 4 (2001), 657.

[20]

Bryce Goodman and Seth Flaxman. 2016. European union regulations on algorithmic decision-making and a “right to explanation.” In Workshop on Human Interpretability in Machine Learning at the International Conference on Machine Learning.

[21]

Stephen Hawking, Stuart Russell, Max Tegmark, and Frank Wilczek. 2014. Stephen hawking: “Transcendence looks at the implications of artificial intelligence—but are we taking AI seriously enough?” The Independent 2014, 05-01 (2014).

[22]

Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl. 2000. Explaining collaborative filtering recommendations. In Proceedings of the ACM Conference on Computer Supported Cooperative Work. 241--250.

Digital Library

[23]

Traci J. Hess, Mark A. Fuller, and John Mathew. 2005. Involvement and decision-making performance with a decision aid: The influence of social multimedia, gender, and playfulness. J. Manag. Inform. Syst. 22, 3 (2005), 15--54.

Digital Library

[24]

Daniel Kahneman. 2011. Thinking, Fast and Slow. Allen Lane, New York.

[25]

Byungkyu Kang, Nava Tintarev, Tobias Höllorer, and John O’Donovan. 2016. What am I not seeing? An interactive approach to social content discovery in microblogs. In Proceedings of the International Conference on Social Informatics (SocInfo). 279--294.

Digital Library

[26]

Bart Knijnenburg and Martijn Willemsen. 2016. Inferring capabilities of intelligent agents from their external traits. ACM Trans. Interact. Intell. Syst. 6, 4 (2016), Article 28.

Digital Library

[27]

Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015. Principles of explanatory debugging to personalize interactive machine learning. In Proceedings of the International Conference on Intelligent User Interfaces.

Digital Library

[28]

Belgin Mutlu, Eduardo Veas, and Christoph Trattner. 2016. VizRec: Recommending personalized visualizations. ACM Trans. Interact. Intell. Syst. 6, 4 (2016), Article 31.

Digital Library

[29]

Clifford Nass, Jonathan Steuer, and Ellen R. Tauber. 1994. Computers are social actors. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 72--78.

[30]

Raymond S. Nickerson. 1976. On conversational interaction with computers. In Proceedings of the Workshop on User-Oriented Design of Interactive Graphics Systems at ACM SIGGRAPH. ACM, 101--113.

[31]

Donald A. Norman. 1986. Cognitive engineering. In User Centered System Design: New Perspectives on Human-Computer Interaction, Donald A. Norman and Stephen W. Draper (Eds.). Erlbaum, Hillsdale, NJ, 31--61.

[32]

Eli Pariser. 2011. The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. Penguin, New York.

[33]

Henry Prakken and Giovanni Sartor. 1997. Argument-based extended logic programming with defeasible priorities. J. Appl. Non-Classical Log. 7, 1--2 (1997), 25--75.

[34]

Lingyun Qiu and Izak Benbasat. 2009. Evaluating anthropomorphic product recommendation agents: A social relationship perspective to designing information systems. J. Manag. Inform. Syst. 25, 4 (2009), 145--182.

Digital Library

[35]

Leo R. Quintanar, Charles R. Crowell, and Patrik M. Moskal. 1987. Social Ergonomic and Stress Aspects of Work With Computers. Amsterdam: Elsevier Publishers, Chapter The Interactive Computer as a Social Stimulus in Human-Computer Interactions.

[36]

Iyad Rahwan, Mohammed I. Madakkatel, Jean-François Bonnefon, Ruqiyabi N. Awan, and Sherief Abdallah. 2010. Behavioral experiments for assessing the abstract argumentation semantics of reinstatement. Cogn. Sci. 34, 8 (2010), 1483--1502.

[37]

B. Reeves and C. Nass. 1996. The Media Equation: How People Treat Computers, Televisions, and New Media Like Real People and Places. Cambridge University Press, New York, NY.

[38]

Ariel Rosenfeld and Sarit Kraus. 2016. Providing arguments in discussions on the basis of the prediction of human argumentative behavior. ACM Trans. Interact. Intell. Syst. 6 (2016), Article 30. Issue 4.

[39]

J. Ben Schafer, Joseph Konstan, and John Riedl. 1999. Recommender systems in e-commerce. In Proceedings of the ACM Conference on Electronic Commerce. ACM, New York, NY, 158--166.

Digital Library

[40]

James Schaffer, Prasanna Giridhar, Debra Jones, Tobias Höllerer, Tarek Abdelzaher, and John O’Donovan. 2015. Getting the message? A study of explanation interfaces for microblog data analysis. In Proceedings of the International Conference on Intelligent User Interfaces. New York, 345--356.

[41]

Ben Shneiderman. 1997. Direct manipulation versus agents: Paths to predictable, controllable and comprehensible interfaces. In Software Agents, Jeffrey M. Bradshaw (Ed.). AAAI Press, Menlo Park, CA, 97--106.

[42]

David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. 2016. Mastering the game of go with deep neural networks and tree search. Nature 529, 7587 (2016), 484--489.

[43]

Herbert A. Simon. 1955. A behavioural model of choice. Quart. J. Econ. 69, 1 (1955), 99--118.

[44]

Derek Sleeman and John Seely Brown. 1985. Intelligent tutoring systems. Artif. Intell. 26, 2 (1985), 233--238.

[45]

Martin Stettinger, Alexander Felfernig, Gerhard Leitner, and Stefan Reiterer. 2015. Counteracting anchoring effects in group decision making. In Proceedings of the International Conference on User Modeling, Adaptation, and Personalization. 118--130.

[46]

Steve Sutherland, Casper Harteveld, and Michael Young. 2016. Effects of the advisor and environment on requesting and complying with automated advice. ACM Trans. Interact. Intell. Syst. 6, 4 (2016), Article 27.

Digital Library

[47]

Nava Tintarev, Byunkyu Kang, and John O’Donovan. 2015. Inspection mechanisms for community-based content discovery in microblogs. In Proceeding of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at the ACM Conference on Recommender Systems. 21--28.

[48]

Nava Tintarev and Roman Kutlak. 2014. Explanations—making plans scrutable with argumentation and natural language generation. In Proceedings of the International Conference on Intelligent User Interfaces (Demo Track). 29--32.

[49]

Nava Tintarev and Judith Masthoff. 2012. Evaluating the effectiveness of explanations for recommender systems: Methodological issues and empirical studies on the impact of personalization. User Model. User-Adapt. Interact. 22 (2012), 399--439.

Digital Library

[50]

Nava Tintarev and Judith Masthoff. 2015. Explaining recommendations: Design and evaluation. In Recommender Systems Handbook (2nd ed.). Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer, Berlin.

[51]

Nava Tintarev, Nir Oren, Roman Kutlak, Matt Green, Judith Masthoff, K. van Deemter, and W. Vasconcelos. 2013. SAsSy—scrutable autonomous systems. In Do-Form: Enabling Domain Experts to use Formalised Reasoning, a Symposium at the (British) Society for the Study of Artificial Intelligence and the Simulation of Behaviour.

[52]

Janet H. Walker, Lee Sproull, and R. Subramani. 1994. Using a human face in an interface. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 85--91.

[53]

Ian H. Witten and Eibe Frank. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco, CA.

Cited By

Felfernig ATintarev NTrang Tran TStettinger M(2023)Explanations for GroupsGroup Recommender Systems10.1007/978-3-031-44943-7_6(109-131)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-3-031-44943-7_6
Felfernig AAtas MStettinger M(2023)Decision Tasks and Basic AlgorithmsGroup Recommender Systems10.1007/978-3-031-44943-7_1(3-27)Online publication date: 23-Sep-2023
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Pizzi GVannucci VMazzoli VDonvito R(2023)I, chatbot! the impact of anthropomorphism and gaze direction on willingness to disclose personal information and behavioral intentionsPsychology & Marketing10.1002/mar.2181340:7(1372-1387)Online publication date: 24-Mar-2023
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Index Terms

Introduction to the Special Issue on Human Interaction with Artificial Advice Givers
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information systems applications
    1. Decision support systems

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Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 6, Issue 4

Special Issue on Human Interaction with Artificial Advice Givers

December 2016

176 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3015563

Editor:
Michelle Zhou
Juji, Inc., USA

Issue’s Table of Contents

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 December 2016

Accepted: 01 November 2016

Received: 01 October 2016

Published in TIIS Volume 6, Issue 4

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Cited By

Felfernig ATintarev NTrang Tran TStettinger M(2023)Explanations for GroupsGroup Recommender Systems10.1007/978-3-031-44943-7_6(109-131)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-3-031-44943-7_6
Felfernig AAtas MStettinger M(2023)Decision Tasks and Basic AlgorithmsGroup Recommender Systems10.1007/978-3-031-44943-7_1(3-27)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-3-031-44943-7_1
Pizzi GVannucci VMazzoli VDonvito R(2023)I, chatbot! the impact of anthropomorphism and gaze direction on willingness to disclose personal information and behavioral intentionsPsychology & Marketing10.1002/mar.2181340:7(1372-1387)Online publication date: 24-Mar-2023
https://doi.org/10.1002/mar.21813
Katti CBarbosa B(2022)AI vs. HumanImplementing Automation Initiatives in Companies to Create Better-Connected Experiences10.4018/978-1-6684-5538-8.ch008(160-178)Online publication date: 30-Jun-2022
https://doi.org/10.4018/978-1-6684-5538-8.ch008
Baabdullah AAlalwan AAlgharabat RMetri BRana N(2022)Virtual agents and flow experience: An empirical examination of AI-powered chatbotsTechnological Forecasting and Social Change10.1016/j.techfore.2022.121772181(121772)Online publication date: Aug-2022
https://doi.org/10.1016/j.techfore.2022.121772
Felfernig ABoratto LStettinger MTkalčič MFelfernig ABoratto LStettinger MTkalčič M(2018)Explanations for GroupsGroup Recommender Systems10.1007/978-3-319-75067-5_6(105-126)Online publication date: 8-Mar-2018
https://doi.org/10.1007/978-3-319-75067-5_6
Felfernig ABoratto LStettinger MTkalčič MFelfernig ABoratto LStettinger MTkalčič M(2018)Decision Tasks and Basic AlgorithmsGroup Recommender Systems10.1007/978-3-319-75067-5_1(3-26)Online publication date: 8-Mar-2018
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Rosenfeld AAgmon NMaksimov OKraus S(2017)Intelligent agent supporting human–multi-robot team collaborationArtificial Intelligence10.1016/j.artint.2017.08.005252(211-231)Online publication date: Nov-2017
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