skip to main content
research-article

Artificial intelligence assistants and risk: framing a connectivity risk narrative

Published:01 September 2020Publication History
Skip Abstract Section

Abstract

Abstract

Our social relations are changing, we are now not just talking to each other, but we are now also talking to artificial intelligence (AI) assistants. We claim AI assistants present a new form of digital connectivity risk and a key aspect of this risk phenomenon is related to user risk awareness (or lack of) regarding AI assistant functionality. AI assistants present a significant societal risk phenomenon amplified by the global scale of the products and the increasing use in healthcare, education, business, and service industry. However, there appears to be little research concerning the need to not only understand the changing risks of AI assistant technologies but also how to frame and communicate the risks to users. How can users assess the risks without fully understanding the complexity of the technology? This is a challenging and unwelcome scenario. AI assistant technologies consist of a complex ecosystem and demand explicit and precise communication in terms of communicating and contextualising the new digital risk phenomenon. The paper then argues for the need to examine how to best to explain and support both domestic and commercial user risk awareness regarding AI assistants. To this end, we propose the method of creating a risk narrative which is focused on temporal points of changing societal connectivity and contextualised in terms of risk. We claim the connectivity risk narrative provides an effective medium in capturing, communicating, and contextualising the risks of AI assistants in a medium that can support explainability as a risk mitigation mechanism.

References

  1. Albrecht JPHow the GDPR will change the worldEur Data Prot L Rev20162287Google ScholarGoogle ScholarCross RefCross Ref
  2. Alzahrani H (2016) Artificial intelligence: uses and misuses. Glob J Comput Sci Technol 16(1)Google ScholarGoogle Scholar
  3. Amazon Press Release (2017) http://phx.corporate-ir.net/phoenix.zhtml?c=176060&p=irol-newsArticle&ID=2324045. Accessed June 2018Google ScholarGoogle Scholar
  4. Amazon.com Help: Alexa Terms of Use (2019) https://www.amazon.com/gp/help/customer/display.html?nodeId=201809740. Accessed July 2019Google ScholarGoogle Scholar
  5. Andrejevic MGates KBig data surveillance: introductionSurveill Soc2014122185196Google ScholarGoogle Scholar
  6. Awad NKrishnan MThe personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalizationMIS Q2006301132810.2307/25148715Google ScholarGoogle ScholarCross RefCross Ref
  7. Bellet TCunneen MMullins MMurphy FPütz FSpickermann FBraendle CBaumann MFFrom semi to fully autonomous vehicles: new emerging risks and ethico-legal challenges for human-machine interactionsTransp Res Part F Traffic Psychol Behav201963153164Google ScholarGoogle ScholarCross RefCross Ref
  8. Barth SDe Jong MDThe privacy paradox–investigating discrepancies between expressed privacy concerns and actual online behavior—a systematic literature reviewTelemat Inform201734710381058Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Barth A, Datta A, Mitchell JC, Nissenbaum H (2006) Privacy and contextual integrity: framework and applications. In: 2006 IEEE symposium on security and privacy (S&P'06). IEEE, p 15Google ScholarGoogle Scholar
  10. Bates DWSaria SOhno-Machado LShah AEscobar GJBig data in health care: using analytics to identify and manage high-risk and high-cost patientsHealth Aff201433711231131Google ScholarGoogle ScholarCross RefCross Ref
  11. Berson IRFerron JMBerson MJEmerging risks of violence in the digital ageJ Sch Violence2002125171Google ScholarGoogle ScholarCross RefCross Ref
  12. Bologa RBologa RFlorea ABig data and specific analysis methods for insurance fraud detectionDatabase Syst J2013443039Google ScholarGoogle Scholar
  13. Bottis MCBouchagiar GPersonal data v. Big data: challenges of commodification of personal dataOpen J Philos201883206215Google ScholarGoogle Scholar
  14. Canbek NGMutlu MEOn the track of artificial intelligence: learning with intelligent personal assistantsJ New Results Sci2016131592601Google ScholarGoogle Scholar
  15. Cate FHThe big data debateScience20143466211818Google ScholarGoogle Scholar
  16. Chung H, Park J, Lee S (2017) Digital forensic approaches for Amazon Alexa ecosystem. Digital investigation, vol 22. https://sciencedirect.com/science/article/pii/s1742287617301974. Retrieved 22 Aug 2019Google ScholarGoogle Scholar
  17. Crandall J, Song P (2013) A pointillism approach for natural language processing of social media. arXiv (Information Retrieval)Google ScholarGoogle Scholar
  18. Cunneen M, Mullins M, Murphy F, Shannon D, Furxhi I, Ryan C (2019) Autonomous vehicles and avoiding the trolley (dilemma): vehicle perception, classification, and the challenges of framing decision ethics. Cybern Syst 1–22Google ScholarGoogle Scholar
  19. Dale RThe limits of intelligent personal assistantsNat Lang Eng2015212325329Google ScholarGoogle Scholar
  20. Dale RThe pros and cons of listening devicesNat Lang Eng2017236969973Google ScholarGoogle Scholar
  21. de Wit JBDas EVet RWhat works best: objective statistics or a personal testimonial? An assessment of the persuasive effects of different types of message evidence on risk perceptionHealth Psychol2008271110115Google ScholarGoogle Scholar
  22. Dhar VEquity, safety, and privacy in the autonomous vehicle eraIEEE Comput201649118083Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Downs JSPrescriptive scientific narratives for communicating usable scienceProc Natl Acad Sci USA20141111362713633Google ScholarGoogle Scholar
  24. Doyle THelen Nissenbaum, privacy in context: technology, policy, and the integrity of social lifeJ Value Inqui2011451971021112650Google ScholarGoogle Scholar
  25. Floridi L (2019) Translating principles into practices of digital ethics: five risks of being unethical. Philos Technol 1–9Google ScholarGoogle Scholar
  26. Fumero AMarrero RJVoltes DPenate WPersonal and social factors involved in internet addiction among adolescents: a meta-analysisComput Human Behav201886387400Google ScholarGoogle Scholar
  27. Golding DKrimsky SPlough AEvaluating risk communication: narrative vs. Technical presentations of information about radonRisk Anal19921212735Google ScholarGoogle Scholar
  28. Gray S (2016) Always on: privacy implications of microphone-enabled devices. In: Future of privacy forumGoogle ScholarGoogle Scholar
  29. Gunkel DSocial contract 2.0: terms of service agreements and political theoryJ Media Crit20141145168Google ScholarGoogle Scholar
  30. Guzman AMaking AI safe for humans: a conversation with Siri2017LondonRoutledge6985Google ScholarGoogle Scholar
  31. Hasebrink U, Goerzig A, Haddon L, Kalmus V, Livingstone S (2011) Patterns of risk and safety online: in-depth analyses from the EU Kids Online survey. https://core.ac.uk/download/pdf/221597.pdf. Accessed Apr 2019Google ScholarGoogle Scholar
  32. Helbing DFrey BSGigerenzer GHafen EHagner MHofstetter YZwitter AWill democracy survive big data and artificial intelligence? Towards digital enlightenment2019ChamSpringer7398Google ScholarGoogle Scholar
  33. Henwood K, Pidgeon N, Parkhill K, Simmons P (2011) Researching risk: Narrative, biography, subjectivity. Hist Soc Res/Historische Sozialforschung 36(4):251–272Google ScholarGoogle Scholar
  34. Heyvaert MMaes BOnghena PMixed methods research synthesis: definition, framework, and potentialQual Quant2013472659676Google ScholarGoogle Scholar
  35. Hildebrandt MSlaves to big data. Or are we?Rev Internet Derecho Política201317744Google ScholarGoogle Scholar
  36. Hildebrandt MSmart technologies and the end (s) of law: novel entanglements of law and technology2015LondonEdward Elgar PublishingGoogle ScholarGoogle ScholarCross RefCross Ref
  37. Hildebrandt MO’Hara KWaidner MThe value of personal data. Digital enlightenment yearbook 20132013AmsterdamIOS PressGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  38. Janeček V (2018) Ownership of personal data in the internet of things (December 1, 2017). Comput Law Secur Rev 34(5):1039–1052. 10.2139/ssrn.3111047Google ScholarGoogle Scholar
  39. Kshetri NVoas JCyberthreats under the BedComputer20185159295Google ScholarGoogle Scholar
  40. Lopatovska I, Rink K, Knight I, Raines K, Cosenza K, Williams H, Sorsche P, Hirsch D, Li Q, Martinez A (2018) Talk to me: exploring user interactions with the Amazon Alexa. J Librariansh Inf Sci 96100061875941Google ScholarGoogle Scholar
  41. Lupton DBurgess AAlemanno AZinn JDigital risk societyThe Routledge hand-book of risk studies2016LondonRoutledge301309Google ScholarGoogle Scholar
  42. Mairal GNarratives of riskJ Risk Res200811141542439792Google ScholarGoogle Scholar
  43. Marchant GE, Allenby BR, Herkert JR (2011) The growing gap between emerging technologies and legal-ethical oversight: the pacing problem. In: The international library of ethics, law and technologyGoogle ScholarGoogle Scholar
  44. Martin ADigital literacy and the digital societyDigit Literacies Concepts Policies Pract200830151176Google ScholarGoogle Scholar
  45. Matzner TWhy privacy is not enough privacy in the context of “ubiquitous computing” and “big data”J Inf Commun Ethics Soc201412293106Google ScholarGoogle ScholarCross RefCross Ref
  46. McLean GOsei-Frimpong KHey Alexa… examine the variables influencing the use of artificial intelligent in-home voice assistantsComput Hum Behav2019992837Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Middleton CAIllusions of balance and control in an always-on environment: a case study of BlackBerry usersContinuum2007212165178Google ScholarGoogle ScholarCross RefCross Ref
  48. Mitchell MC, Egudo M (2003) A review of narrative methodology (no. DSTO-GD-0385). Def Sci Technol Organ Edinb (Australia) Land Oper DivGoogle ScholarGoogle Scholar
  49. Mote K (2012) Natural language processing - a survey. Computation and language. arXiv:1209.6238Google ScholarGoogle Scholar
  50. Nadkarni PMOhno-Machado LChapman WWNatural language processing: an introductionJ Am Med Inform Assoc2011185544551Google ScholarGoogle ScholarCross RefCross Ref
  51. Nissenbaum HPrivacy as contextual integrityWash Law Rev2004791119157Google ScholarGoogle Scholar
  52. Nissenbaum H (2017) Deregulating collection: must privacy give way to use regulation? Soc Sci Res NetwGoogle ScholarGoogle Scholar
  53. Otway HThomas KReflections on risk perception and policy 1,2Risk Anal1982226982Google ScholarGoogle Scholar
  54. Paefgen JStaake TThiesse FEvaluation and aggregation of pay-as-you-drive insurance rate factorsDecis Support Syst201356192201Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Papacharissi ZPrivacy as a luxury commodityFirst Monday20101582Google ScholarGoogle Scholar
  56. Parthasarathy SRegulating risk: defining genetic privacy in the United States and BritainSci Technol Hum Values2004293332352Google ScholarGoogle Scholar
  57. Pierson JHeyman RSocial media and cookies: challenges for online privacyInfo20111363042Google ScholarGoogle ScholarCross RefCross Ref
  58. Preece AAsking ‘Why’ in AI: explainability of intelligent systems—perspectives and challengesIntell Syst Account Financ Manag201825637210.1002/isaf.1422Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Rosen J (2012) The right to be forgotten. Stanford Law Review. Available from http://www.stanfordlawreview.org/online/privacy-paradox/right-to-be-forgotten. Accessed 14 Nov 2018Google ScholarGoogle Scholar
  60. Sciutti AMara ATagliasco VSandini GHumanizing human–robot interaction: on the importance of mutual understandingIEEE Technol Soc Mag20183712229Google ScholarGoogle ScholarCross RefCross Ref
  61. Turkle S (2006) Always-on/Always-on-you: the tethered self. Handbook of mobile communication studies, 121Google ScholarGoogle Scholar
  62. Turkle SIn good company? On the threshold of robotic companions. Close engagements with artificial companions: key2010AmsterdamJohn BenjaminsGoogle ScholarGoogle Scholar
  63. Turkle SThe tethered self: technology reinvents intimacy and solitudeContin High Educ Rev20117529Google ScholarGoogle Scholar
  64. Van Loon J (2003) Risk and technological culture: towards a sociology of virulenceGoogle ScholarGoogle Scholar
  65. Venkatadri G, Andreou A, Liu Y, Mislove A, Gummadi KP, Loiseau P, Goga O (2018) Privacy risks with facebook’s PII-based targeting: auditing a data broker’s advertising interface. In: 2018 IEEE symposium on security and privacy (SP). IEEE, pp 89–107Google ScholarGoogle Scholar
  66. Weizenbaum JELIZA—a computer program for the study of natural language communication between man and machineCommun ACM1966913645Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. Zuboff S (1988) Dilemmas of transformation in the age of the smart machine. PUB TYPE 81Google ScholarGoogle Scholar
  68. Zuboff SThe emperor’s new information economyInformation technology and changes in organizational work1996Boston, MASpringer1317Google ScholarGoogle Scholar
  69. Zuboff S (2019) Surveillance capitalism and the challenge of collective action. In: New labor forum, vol 28, No. 1. SAGE Publications, Sage, Los Angeles, CA, pp 10–29Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Article Metrics

    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0

    Other Metrics