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Adopting robot lawyer? The extending artificial intelligence robot lawyer technology acceptance model for legal industry by an exploratory study

Published online by Cambridge University Press:  13 February 2019

Ni Xu  and
Kung-Jeng Wang Open the ORCID record for Kung-Jeng Wang [Opens in a new window]
Ni Xu
Affiliation:
School of Management, National Taiwan University of Science and Technology, Taipei, Taiwan (R.O.C.)
Kung-Jeng Wang*
Affiliation:
School of Management, National Taiwan University of Science and Technology, Taipei, Taiwan (R.O.C.)
*
*Corresponding author. Email: kjwang@mail.ntust.edu.tw
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Abstract

The development of artificial intelligence has created new opportunities and challenges in industries. The competition between robots and humans has elicited extensive attention among legal researchers. In this exploratory study, we addressed issues regarding the introduction of robots to the practice of legal service through a semistructured interviews with lawyers, judges, artificial intelligence experts, and potential clients. An extended robot lawyer technology acceptance model with five facets and 11 elements is proposed in this study. This model highlights two dimensions: ‘legal use’ and ‘perception of trust.’ In summary, this study provides new specific implications and exhibits three characteristics, namely, derivative, macroscopic, and instructive, in the legal services with artificial intelligence. In addition, artificial intelligence robot lawyers are being developed with some of the abilities necessary to substitute for human beings. Nevertheless, working with human lawyers is imperative to produce benefits from this type of reciprocity.

Keywords

artificial intelligencelegal servicesqualitative researchrobottechnology acceptance model

Information

Type
Research Article
Information
Journal of Management & Organization , Volume 27 , Issue 5 , September 2021 , pp. 867 - 885
Copyright
Copyright © Cambridge University Press and Australian and New Zealand Academy of Management 2019

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References

Abroud, A., Choong, Y. V., Muthaiyah, S., & Fie, D. Y. G. (2015). Adopting e-finance: decomposing the technology acceptance model for investors. Service Business, 9(1), 161–182.CrossRefGoogle Scholar
Adamski, D. (2018). Lost on the digital platform: Europe’s legal travails with the digital single market. Common Market Law Review, 55(3), 719–751.Google Scholar
Aguilo-Regla, J. (2005). Introduction: Legal informatics and the conceptions of the law. In V. R. Benjamins, P. Casanovas, J. Breuker, & A. Gangemi (Eds.), Law and the semantic web: Legal ontologies, methodologies, legal information retrieval, and applications (Vol. 3369, pp. 18–24). Berlin: Springer-Verlag Berlin.CrossRefGoogle Scholar
Alam, F., Danieli, M., & Riccardi, G. (2017). Annotating and modeling empathy in spoken conversations. Computer Speech & Language, 50, 40–61.CrossRefGoogle Scholar
Alarie, B., Niblett, A., & Yoon, A. H. (2016). Focus feature: Artificial intelligence, big data, and the future of law. University of Toronto Law Journal, 66(4), 423–428. https://doi.org/10.3138/utlj.4005.CrossRefGoogle Scholar
Alarie, B., Niblett, A., & Yoon, A. H. (2018). How artificial intelligence will affect the practice of law. University of Toronto Law Journal, 68, 106–124. https://doi.org/10.3138/utlj.2017-0052.CrossRefGoogle Scholar
Aletras, N., Tsarapatsanis, D., Preotiuc-pietro, D., & Lampos, V. (2016). Predicting judicial decisions of the European Court of Human Rights: A natural language processing perspective. PeerJ Computer Science, 2, e93.CrossRefGoogle Scholar
Arbib, M. A., & Fellous, J. M. (2004). Emotions: From brain to robot. Trends in Cognitive Sciences, 8(12), 554–561. https://doi.org/10.1016/j.tics.2004.10.004.CrossRefGoogle Scholar
Arruda, A. (2016). An ethical obligation to use artificial intelligence: An examination of the use of artificial intelligence in law and the model rules of professional responsibility. The American Journal of Trial Advocacy, 40, 443.Google Scholar
Aryabarzan, N., Minaei-Bidgoli, B., & Teshnehlab, M. (2018). negFIN: An efficient algorithm for fast mining frequent itemsets. Expert Systems with Applications, 105, 129–143. https://doi.org/10.1016/j.eswa.2018.03.041.CrossRefGoogle Scholar
Ashley, K. D. (2012). Teaching law and digital age legal practice with an AI and law seminar. Chicago-Kent Law Review, 88, 783.Google Scholar
Augello, A., Infantino, I., Manfre, A., Pilato, G., & Vella, F. (2016). Analyzing and discussing primary creative traits of a robotic artist. Biologically Inspired Cognitive Architectures, 17, 22–31. https://doi.org/10.1016/j.bica.2016.07.006.CrossRefGoogle Scholar
Barton, B. H. (2014). The Lawyer’s monopoly-what goes and what stays. Fordham Law Review, 82(6), 3067–3090.Google Scholar
Bast, C. M., & Pyle, R. C. (2001). Legal research in the computer age: A paradigm shift? Law Library Journal, 93(2), 285–302.Google Scholar
Ben-Shahar, O., & Porat, A. (2016). Personalizing negligence law. New York University Law Review, 91(3), 627–688.Google Scholar
Bench-Capon, T., Araszkiewicz, M., Ashley, K., Atkinson, K., Bex, F., Borges, F., . . . Wyner, A. Z. (2012). A history of AI and Law in 50 papers: 25 years of the international conference on AI and Law. Artificial Intelligence and Law, 20(3), 215–319.CrossRefGoogle Scholar
Bersoff, D. N., & Hofer, P. J. (1991). Legal Issues in Computerized Psychological Testing. Ethical conflicts in psychology. Retrieved from http://psycnet.apa.org/doi/10.1037/10329-000.Google Scholar
Bertolini, A., & Aiello, G. (2018). Robot companions: A legal and ethical analysis. Information Society, 34(3), 130–140. https://doi.org/10.1080/01972243.2018.1444249.CrossRefGoogle Scholar
Bintliff, B. (1996). From creativity to computerese: Thinking like a lawyer in the computer age. Law Library Journal, 88(3), 338–351.Google Scholar
Boynton, S. (2017). DoNotPay, ‘world’s first robot lawyer,’ coming to Vancouver to help fight parking tickets. Online News Producer Global News, p. 1. Retrieved 2018 from https://globalnews.ca/news/3838307/donotpay-robot-lawyer-vancouver-parking-tickets/.Google Scholar
Breazeal, C. (2003). Emotion and sociable humanoid robots. International Journal of Human-Computer Studies, 59(1–2), 119–155. https://doi.org/10.1016/s1071-5819(03)00018-1.CrossRefGoogle Scholar
Brougham, D., & Haar, J. (2018). Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239–257. https://doi.org/10.1017/jmo.2016.55.CrossRefGoogle Scholar
Brown, L. G. (1989). The strategic and tactical implications of convenience in consumer product marketing. Journal of Consumer Marketing, 6(3), 13–19.CrossRefGoogle Scholar
Bryson, J., & Winfield, A. (2017). Standardizing ethical design for Artificial Intelligence and autonomous systems. Computer, 50(5), 116–119. https://doi.org/10.1109/mc.2017.154.CrossRefGoogle Scholar
Canamero, L. (2005). Emotion understanding from the perspective of autonomous robots research. Neural Networks, 18(4), 445–455. https://doi.org/10.1016/j.neunet.2005.03.003.CrossRefGoogle ScholarPubMed
Castell, S. (2018). The future decisions of RoboJudge HHJ Arthur Ian Blockchain: Dread, delight or derision? Computer Law & Security Review, 34(4), 739–753. https://doi.org/10.1016/j.clsr.2018.05.011.CrossRefGoogle Scholar
Cavallo, F., Semeraro, F., Fiorini, L., Magyar, G., Sincak, P., & Dario, P. (2018). Emotion modelling for social robotics applications: a review. Journal of Bionic Engineering, 15(2), 185–203. https://doi.org/10.1007/s42235-018-0015-y.CrossRefGoogle Scholar
Chandrinos, S. K., Sakkas, G., & Lagaros, N. D. (2018). AIRMS: A risk management tool using machine learning. Expert Systems with Applications, 105, 34–48. https://doi.org/10.1016/j.eswa.2018.03.044.CrossRefGoogle Scholar
D’Amato, A. (1976). Can/should computers replace judges. The Georgia Law Review, 11, 1277.Google Scholar
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.CrossRefGoogle Scholar
Deedman, C., & Smith, J. (1991). The nervous shock advisor: A legal expert system in case-based law. New York: Pergamon Press.Google Scholar
Dekker, F., Salomons, A., & van der Waal, J. (2017). Fear of robots at work: The role of economic self-interest. Socio-Economic Review, 15(3), 539–562. https://doi.org/10.1093/ser/mwx005.Google Scholar
Dzindolet, M. T., Peterson, S. A., Pomranky, R. A., Pierce, L. G., & Beck, H. P. (2003). The role of trust in automation reliance. International Journal of Human-Computer Studies, 58(6), 697–718. https://doi.org/10.1016/s1071-5819(03)00038-7.CrossRefGoogle Scholar
Evans, N., & Price, J. (2017). Managing information in law firms: Changes and challenges. Information Research, 22(1), 21.Google Scholar
Fells, R., Caspersz, D., & Leighton, C. (2018). The encouragement of bargaining in good faith - A behavioural approach. Journal of Industrial Relations, 60(2), 266–281. https://doi.org/10.1177/0022185617741925.CrossRefGoogle Scholar
Flower, L. (2018). Doing loyalty: defense lawyers’ subtle dramas in the courtroom. Journal of Contemporary Ethnography, 47(2), 226–254. https://doi.org/10.1177/0891241616646826.CrossRefGoogle Scholar
Fuller, M. A., Serva, M. A., & Baroudi, J. (2010). Clarifying the integration of trust and TAM in e-commerce environments: implications for systems design and management. IEEE Transactions on Engineering Management, 57(3), 380–393.Google Scholar
Gadanho, S. C., & Hallam, J. (2001). Robot learning driven by emotions. Adaptive Behavior, 9(1), 42–64. https://doi.org/10.1177/105971230200900102.CrossRefGoogle Scholar
Galeon, D., & Houser, K. (2017). An AI completed 360,000 hours of finance work in just seconds. Retrieved from https://futurism.com/an-ai-completed-360000-hours-of-finance-work-in-just-seconds/ Google Scholar
Gatteschi, V., Lamberti, F., Montuschi, P., & Sanna, A. (2016). Semantics-based intelligent human-computer interaction. IEEE Intelligent Systems, 31(4), 11–21. https://doi.org/10.1109/mis.2015.97.CrossRefGoogle Scholar
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. Mis Quarterly, 27(1), 51–90.CrossRefGoogle Scholar
Goodman, J. (2016). Meet the AI robot lawyers and virtual assistants. Retrieved from https://www.lexisnexis-es.co.uk/assets/files/legal-innovation.pdf.Google Scholar
Goodman-Delahunty, J., Granhag, P. A., Hartwig, M., & Loftus, E. F. (2010). Insightful or wishful: Lawyers’ ability to predict case outcomes. Psychology Public Policy and Law, 16(2), 133–157. https://doi.org/10.1037/a0019060.CrossRefGoogle Scholar
Gray, K., & Wegner, D. M. (2012). Feeling robots and human zombies: Mind perception and the uncanny valley. Cognition, 125(1), 125–130. https://doi.org/10.1016/j.cognition.2012.06.007.CrossRefGoogle ScholarPubMed
Greenleaf, G., Mowbray, A., & Chung, P. (2018). Building sustainable free legal advisory systems: Experiences from the history of AI & law. Computer Law & Security Review, 34(2), 314–326. https://doi.org/10.1016/j.clsr.2018.02.007.CrossRefGoogle Scholar
Hak, R., & Zeman, T. (2017). Consistent categorization of multimodal integration patterns during human-computer interaction. Journal on Multimodal User Interfaces, 11(3), 251–265. https://doi.org/10.1007/s12193-017-0243-1.CrossRefGoogle Scholar
Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, 53(5), 517–527. https://doi.org/10.1177/0018720811417254.CrossRefGoogle ScholarPubMed
Handel, B., & Schwartzstein, J. (2018). Frictions or mental gaps: what’s behind the information we (don’t) use and when do we care? Journal of Economic Perspectives, 32(1), 155–178. https://doi.org/10.1257/jep.32.1.155.CrossRefGoogle ScholarPubMed
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. J. I. S. (2015). The rise of ‘big data’ on cloud computing: Review and open research issues. 47, 98–115.CrossRefGoogle Scholar
Hibbeln, M., Jenkins, J. L., Schneider, C., Valacich, J. S., & Weinmann, M. (2017). How is your user feeling? Inferring emotion through human-computer interaction devices. Mis Quarterly, 41(1), 1.CrossRefGoogle Scholar
Hildebrandt, M. (2018). Algorithmic regulation and the rule of law. Philosophical Transactions of the Royal Society A, 376(2128), 11. https://doi.org/10.1098/rsta.2017.0355.Google ScholarPubMed
Hilt, K. (2017). What does the future hold for the law librarian in the advent of artificial intelligence? Canadian Journal of Information and Library Science, 41(3), 211–227.Google Scholar
Hofree, G., Ruvolo, P., Reinert, A., Bartlett, M. S., & Winkielman, P. (2018). Behind the robot’s smiles and frowns: In social context, people do not mirror android’s expressions but react to their informational value. Frontiers in Neurorobotics, 12, 11. https://doi.org/10.3389/fnbot.2018.00014.CrossRefGoogle Scholar
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459.CrossRefGoogle Scholar
Kim, J. B. (2012). An empirical study on consumer first purchase intention in online shopping: Integrating initial trust and TAM. Electronic Commerce Research, 12(2), 125–150.CrossRefGoogle Scholar
Kralik, J. D., Mao, T., Cheng, Z., & Ray, L. E. (2016). Modeling incubation and restructuring for creative problem solving in robots. Robotics and Autonomous Systems, 86, 162–173. https://doi.org/10.1016/j.robot.2016.08.025.CrossRefGoogle Scholar
Kurzweil, R. (2000). The age of spiritual machines: When computers exceed human intelligence. New York: Penguin Books.Google Scholar
LawGeex (2018). Ai vs lawyers. Retrieved 2018 from https://www.lawgeex.com/AIvsLawyer/.Google Scholar
Lee, W. H., & Kim, J. H. (2018). Hierarchical emotional episodic memory for social human robot collaboration. Autonomous Robots, 42(5), 1087–1102. https://doi.org/10.1007/s10514-017-9679-0.CrossRefGoogle Scholar
Lopez, A. C. (2017). The evolutionary psychology of war: Offense and defense in the adapted mind. Evolutionary Psychology, 15(4), 23 https://doi.org/10.1177/1474704917742720.CrossRefGoogle ScholarPubMed
Marcus, R. L. (2008). The electronic lawyer. DePaul Law Review, 58, 263.Google Scholar
Masuyama, N., Loo, C. K., & Seera, M. (2018). Personality affected robotic emotional model with associative memory for human-robot interaction. Neurocomputing, 272, 213–225. https://doi.org/10.1016/j.neucom.2017.06.069.CrossRefGoogle Scholar
McClure, P. K. (2018). ’You’re Fired,’ says the robot: The rise of automation in the workplace, technophobes, and fears of unemployment. Social Science Computer Review, 36(2), 139–156. https://doi.org/10.1177/0894439317698637.CrossRefGoogle Scholar
McGinnis, J. O., & Pearce, R. G. (2014). The great disruption: How machine intelligence will transform the role of lawyers in the delivery of legal services. Fordham Law Review, 82(6), 3041–3066.Google Scholar
McNally, P., & Inayatullah, S. J. F. (1988). The rights of robots: Technology, culture and law in the 21st century. Futures, 20(2), 119–136.CrossRefGoogle Scholar
Mcnamar, R. T. (2009). Methods, systems and computer software utilizing xbrl to identify, capture, array, manage, transmit and display documents and data in litigation preparation, trial and regulatory filings and regulatory compliance. United States Patent No.: 20090030754. U. S. Patent.Google Scholar
Menne, I. M., & Schwab, F. (2018). Faces of emotion: Investigating emotional facial expressions towards a robot. International Journal of Social Robotics, 10(2), 199–209. https://doi.org/10.1007/s12369-017-0447-2.CrossRefGoogle Scholar
Mommers, L., Voermans, W., Koelewijn, W., & Kielman, H. (2009). Understanding the law: Improving legal knowledge dissemination by translating the contents of formal sources of law. Artificial Intelligence and Law, 17(1), 51–78.CrossRefGoogle Scholar
Moses, L. B., & Chan, J. (2014). Using big data for legal and law enforcement decisions: Testing the new tools. UNSWLJ, 37, 643.Google Scholar
Nissan, E. (2018). Computer tools and techniques for lawyers and the judiciary. Cybernetics and Systems, 49(4), 201–233. https://doi.org/10.1080/01969722.2018.1447766.CrossRefGoogle Scholar
Ojha, S., Williams, M. A., & Johnston, B. (2018). The essence of ethical reasoning in robot-emotion processing. International Journal of Social Robotics, 10(2), 211–223. https://doi.org/10.1007/s12369-017-0459-y.CrossRefGoogle Scholar
Olteteanu, A. M., Falomir, Z., & Freksa, C. (2018). Artificial cognitive systems that can answer human creativity tests: An approach and two case studies. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 469–475. https://doi.org/10.1109/tcds.2016.2629622.CrossRefGoogle Scholar
Oskamp, A., & Lauritsen, M. (2002). AI in law practice? So far, not much. Artificial Intelligence and Law, 10(4), 227–236.CrossRefGoogle Scholar
Papakonstantinou, V., & De Hert, P. (2018). Structuring modern life running on software. Recognizing (some) computer programs as new ‘digital persons’. Computer Law & Security Review, 34(4), 732–738. https://doi.org/10.1016/j.clsr.2018.05.032.CrossRefGoogle Scholar
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.Google Scholar
Pham, Q. C., Madhavan, R., Righetti, L., Smart, W., & Chatila, R. (2018). The Impact of robotics and automation on working conditions and employment. IEEE Robotics & Automation Magazine, 25(2), 126–128. https://doi.org/10.1109/mra.2018.2822058.CrossRefGoogle Scholar
Pointeau, G., & Dominey, P. F. (2017). The role of autobiographical memory in the development of a robot self. Frontiers in Neurorobotics, 11, 18 https://doi.org/10.3389/fnbot.2017.00027.CrossRefGoogle ScholarPubMed
Popple, J. (1991). Legal expert systems: The inadequacy of a rule-based approach. Australian Computer Journal, 23, 8.CrossRefGoogle Scholar
Prakken, H. (2005). AI & law, logic and argument schemes. Argumentation, 19(3), 303–320.CrossRefGoogle Scholar
Ramirez-Gallego, S., Fernandez, A., Garcia, S., Chen, M., & Herrera, F. (2018). Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce. Information Fusion, 42, 51–61. https://doi.org/10.1016/j.inffus.2017.10.001.CrossRefGoogle Scholar
Reif, J. A. M., & Brodbeck, F. C. (2017). When do people initiate a negotiation? The role of discrepancy, satisfaction, and ability beliefs. Negotiation and Conflict Management Research, 10(1), 46–66. https://doi.org/10.1111/ncmr.12089.CrossRefGoogle Scholar
Remus, D., & Levy, F. (2017). Can robots be lawyers: Computers, lawyers, and the practice of law. Georgetown Journal of Legal Ethics, 30(2017), 501.Google Scholar
Riesen, M., & Serpen, G. (2008). Validation of a Bayesian belief network representation for posterior probability calculations on national crime victimization survey. Artificial Intelligence and Law, 16(3), 245–276.CrossRefGoogle Scholar
Rissland, E. L., Ashley, K. D., & Loui, R. P. (2003). AI and law: A fruitful synergy. Artificial Intelligence, 150(1–2), 1–15.CrossRefGoogle Scholar
Roca, J. C., Chiu, C. M., & Martinez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683–696. https://doi.org/10.1016/j.ijhcs.2006.01.003.CrossRefGoogle Scholar
Schoenick, C., Clark, P., Tafjord, O., Turney, P., & Etzioni, O. (2017). Moving beyond the Turing test with the Allen AI science challenge. Communications of the ACM, 60(9), 60–64. https://doi.org/10.1145/3122814.CrossRefGoogle Scholar
Strnad, J. (2007). Should legal empiricists go Bayesian? American Law and Economics Review, 9(1), 195–303.CrossRefGoogle Scholar
Valente, A., & Breuker, J. (1994). Ontologies: The missing link between legal theory and AI & law. Legal knowledge Based Systems JURIX, 94, 138–150.Google Scholar
von der Lieth Gardner, A (1987). An artificial intelligence approach to legal reasoning. MA, USA: MIT Press.Google Scholar
Vonhippel, E. (1994). Sticky information and the locus of problem-solving - Implications for innovation. Management science, 40(4), 429–439. https://doi.org/10.1287/mnsc.40.4.429.CrossRefGoogle Scholar
Wang, D., Wang, P., & Shi, J. Z. (2018). A fast and efficient conformal regressor with regularized extreme learning machine. Neurocomputing, 304, 1–11. https://doi.org/10.1016/j.neucom.2018.04.012.CrossRefGoogle Scholar
Wichmann, A., Korkmaz, T., & Tosun, A. S. (2018). Robot control strategies for task allocation with connectivity constraints in wireless sensor and robot networks. IEEE Transactions on Mobile Computing, 17(6), 1429–1441. https://doi.org/10.1109/tmc.2017.2766635.CrossRefGoogle Scholar
Wiese, E., Metta, G., & Wykowska, A. (2017). Robots as intentional agents: Using neuroscientific methods to make robots appear more social. Frontiers in Psychology, 8, 19. https://doi.org/10.3389/fpsyg.2017.01663.CrossRefGoogle ScholarPubMed
Yang, Z. L., & Peterson, R. T. (2004). Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology & Marketing, 21(10), 799–822. https://doi.org/10.1002/mar.20030.CrossRefGoogle Scholar
Zeleznikow, J. (2002). An Australian perspective on research and development required for the construction of applied legal decision support systems. Artificial Intelligence and Law, 10(4), 237–260.CrossRefGoogle Scholar
Zlotowski, J., Yogeeswaran, K., & Bartneck, C. (2017). Can we control it? Autonomous robots threaten human identity, uniqueness, safety, and resources. International Journal of Human-Computer Studies, 100, 48–54. https://doi.org/10.1016/j.ijhcs.2016.12.008.CrossRefGoogle Scholar