In the age of the smart artificial intelligence: AI’s dual capacities for automating and informating work

Abstract

Recent developments in artificial intelligence (AI) have generated tidal waves, which are shaking the foundation of organizations and businesses. Even though AI is considered an unprecedented disruptive force for work automation, much can be learned from current research on the computerization of work. Drawing on the seminal work of Shoshana Zuboff, this article provides a balanced perspective on the dual affordances of AI systems for automating and informating work. Whereas AI offers unique capacities for automating cognitive work that once required high-skill workers, it may be a source of unintended consequences such as cognitive complacency or de-skilling workers. To overcome such effects, the informating capacities of AI systems can be invoked to augment work, generate a more comprehensive perspective on organization, and finally equip workers with new sets of intellectual skills.

Keywords

Algorithmic management artificial intelligence augmentation automating informating intelligent machine

Get full access to this article

View all access options for this article.

References

Adomavicius

Bockstedt

Curley

, et al. (2018) Effects of online recommendations on consumers’ willingness to pay. Information Systems Research 29: 84–102.

Agrawal

Gans

Goldfarb

(2017a) How AI will change the way we make decisions. Available at: https://hbr.org/2017/07/how-ai-will-change-the-way-we-make-decisions (accessed 12 January 2019).

Agrawal

Gans

Goldfarb

(2017b) What to expect from artificial intelligence. MIT Sloan Management Review. epub ahead of print 7 February, 2017. Available at: http://mitsmr.com/2jZdf1Y (accessed 07 February 2017).

Barrett

Oborn

Orlikowski

, et al. (2012) Reconfiguring boundary relations: robotic innovations in pharmacy work. Organization Science 23: 1448–1466.

Bean

(2018) The state of machine learning in business today. Forbes. Available at: https://www.forbes.com/sites/ciocentral/2018/09/17/the-state-of-machine-learning-in-business-today/. (accessed 15 March 2019).

Bhargava

(2018) Take control of your social feed. Available at: https://gobo.social/ (accessed 23 January 2019).

Bozdag

Gao

Houben

, et al. (2014) Does offline political segregation affect the filter bubble? An empirical analysis of information diversity for Dutch and Turkish Twitter users. Computers in Human Behavior 41: 405–415.

Brynjolfsson

McAfee

(2017) The business of artificial intelligence: what it can – and cannot – do for your organization. Harvard Business Review Digital Articles 7: 3–11.

Brynjolfsson

Mitchell

(2017) What can machine learning do? Workforce implications. Science 358: 1530–1534.

10.

Brynjolfsson

Rock

Syverson

(2018) Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics. In: Agrawal

Gans

Goldfarb

(eds) The Economics of Artificial Intelligence: An Agenda. Chicago: University of Chicago Press, pp. 23–57.

11.

Bughin

(2018) Why AI isn’t the death of jobs. MIT Sloan Management Review 59: 42–46.

12.

Burton-Jones

(2014) What have we learned from the Smart Machine? Information and Organization 24: 71–105.

13.

Chui

Manyika

Miremadi

(2018) What AI can and can’t do (yet) for your business. McKinsey Quarterly, January 2018.

14.

Davenport

(2018) AI is a linear – not exponential – technology. Available at: https://www.iianalytics.com/blog/2018/12/20/ai-is-a-linearnot-exponentialtechnology (accessed 3 January 2019).

15.

Diakopoulos

(2016) Accountability in algorithmic decision making. Communications of the ACM 59: 56–62.

16.

Ekbia

Nardi

(2014) Heteromation and its (dis)contents: the invisible division of labor between humans and machines. First Monday 19(6): 1–15.

17.

Fan

(2018) Thinking like a human: what it means to give AI a theory of mind. Singularity Hub. Available at: https://singularityhub.com/2018/09/19/thinking-like-a-human-what-it-means-to-give-ai-a-theory-of-mind/#sm.00001tc5j9s5q6dfkzo66lfwcwj3d (accessed 2 January 2019).

18.

Faraj

Pachidi

Sayegh

(2018) Working and organizing in the age of the learning algorithm. Information and Organization 28: 62–70.

19.

Frey

Osborne

(2017) The future of employment: how susceptible are jobs to computerisation? Technological Forecasting and Social Change 114: 254–280.

20.

Frischmann

Selinger

(2017) Robots have already taken over our work, but they’re made of flesh and bone. The Guardian. Available at: https://www.theguardian.com/commentisfree/2017/sep/25/robots-taken-over-work-jobs-economy?CMP=share_btn_tw (accessed 10 October 2017).

21.

Gray

Suri

(2017) The humans working behind the AI curtain. Harvard Business Review. Available at: https://hbr.org/2017/01/the-humans-working-behind-the-ai-curtain (accessed 1 October 2017).

22.

(2017) HubSpot acquires Kemvi to bring more AI into its sales and marketing platform. Available at: https://techcrunch.com/2017/07/25/hubspot-acquires-kemvi/ (accessed 13 January 2019).

23.

Hamilton

(2018) Amazon built an AI tool to hire people but had to shut it down because it was discriminating against women. Available at: https://www.businessinsider.com/amazon-built-ai-to-hire-people-discriminated-against-women-2018-10?utm_source=linkedin&utm_medium=referral&utm_content=topbar&utm_term=desktop&referrer=linkedin (accessed 5 January 2019).

24.

Hoffman

(2016) Using artificial intelligence to set information free. MIT Sloan Management Review 58: 20.

25.

Irani

(2016) The hidden faces of automation. XRDS: Crossroads, The ACM Magazine for Students 23: 34–37.

26.

Jarrahi

(2018) Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Business Horizons 4: 577–586.

27.

Kallinikos

(2010) The “age of smart machine”: a 21st century view. In: Encyclopedia of Software Engineering. New York: Taylor and Francis, pp. 1097–1103.

28.

King

Grudin

(2016) Will computers put us out of work? Computer 49: 82–85.

29.

Kling

Dunlop

(1993) Controversies about computerization and the character of white collar worklife. The Information Society 9: 1–29.

30.

Lacity

Willcocks

(2018) Robotic Process and Cognitive Automation: The Next Phase. Ashford: Steve Brookes.

31.

Lupton

Jutel

(2015) ‘It’s like having a physician in your pocket!’ A critical analysis of self-diagnosis smartphone apps. Social Science & Medicine 133: 128–135.

32.

Mackenzie

(2018) From API to AI: platforms and their opacities. Information, Communication & Society 22(13): 1–18.

33.

Manyika

Chui

Miremadi

, et al. (2017) A future that works: automation, employment, and productivity. Available at: https://www.mckinsey.com/∼/media/mckinsey/featured%20insights/Digital%20Disruption/Harnessing%20automation%20for%20a%20future%20that%20works/MGI-A-future-that-works-Executive-summary.ashx (accessed 15 January 2019).

34.

McAfee

Brynjolfsson

(2017) Machine, Platform, Crowd: Harnessing Our Digital Future. New York: WW Norton & Company.

35.

Mckendrick

(2018) For IBM, reskilling the workforce for AI takes on a new urgency. Available at: https://www.forbes.com/sites/joemckendrick/2018/12/04/for-ibm-reskilling-the-workforce-for-ai-takes-on-a-new-urgency/#52421a1c763e (accessed 23 January 2019).

36.

Miller

(2018) Want less-biased decisions? Use algorithms. Available at: https://hbr.org/2018/07/want-less-biased-decisions-use-algorithms (accessed 3 January 2019).

37.

Nguyen

Yosinski

Clune

(2015) Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 427–436.

38.

O’Reilly

(2016) Managing the bots that are managing the business. Available at: https://sloanreview.mit.edu/article/managing-the-bots-that-are-managing-the-business/ (accessed 10 January 2019).

39.

Parasuraman

Manzey

(2010) Complacency and bias in human use of automation: an attentional integration. Human Factors 52: 381–410.

40.

Park

Han

(2018) Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 286: 800–809.

41.

Pettersen

(2019) Why artificial intelligence will not outsmart complex knowledge work. Work, Employment and Society 33: 1058–1067.

42.

Phillips

Collins

(2019) Automation – it does involve people. Business Information Review 36: 125–129.

43.

Rabinowitz

Perbet

Song

, et al. (2018) Machine theory of mind. arXiv preprint arXiv:1802.07740.

44.

Rejcek

(2017) Can futurists predict the year of the singularity? Available at: https://singularityhub.com/2017/03/31/can-futurists-predict-the-year-of-the-singularity/#sm.00001tc5j9s5q6dfkzo66lfwcwj3d (accessed 15 January 2019).

45.

Remus

Levy

(2017) Can robots be lawyers: computers, lawyers, and the practice of law. Georgetown Journal of Legal Ethics 30: 501.

46.

Riemer

(2018) New A.I. computing paradigm needs new quality thinking. University of Sydney Business School. Available at: http://sbi.sydney.edu.au/new-a-i-computing-paradigm-needs-new-quality-thinking/ (accessed 20 October 2018).

47.

Rosenblat

Stark

(2016) Algorithmic labor and information asymmetries: a case study of Uber’s drivers. International Journal of Communication 10: 3758–3784.

48.

Rouse

Spohrer

(2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation 1–21. epub ahead of print 8 February, 2018. DOI: https://doi.org/10.1080/19488289.2018.1424059.

49.

Sachs

(2019) The algorithm at work? Explanation and repair in the enactment of similarity in art data. Information, Communication & Society 1–17. epub ahead of print 6 May, 2019. DOI: https://doi.org/10.1080/1369118X.2019.1612933.

50.

Shank

Desanti

Maninger

(2019) When are artificial intelligence versus human agents faulted for wrongdoing? Moral attributions after individual and joint decisions. Information, Communication & Society 22: 648–663.

51.

Simon

(1960) The New Science of Management Decision. New York: Harper.

52.

The Economist (2016) Automation and anxiety: will smarter machines cause mass unemployment? Available at: https://www.economist.com/special-report/2016/06/25/automation-and-anxiety (accessed 15 June 2018).

53.

Tredinnick

(2017) Artificial intelligence and professional roles. Business Information Review 34: 37–41.

54.

UC Berkeley (2018) Mortgage algorithms perpetuate racial bias in lending, study finds. Available at: https://news.berkeley.edu/story_jump/mortgage-algorithms-perpetuate-racial-bias-in-lending-study-finds/ (accessed 5 January 2019).

55.

Zheng

Liu

Ren

, et al. (2017) Hybrid-augmented intelligence: collaboration and cognition. Frontiers of Information Technology & Electronic Engineering 18: 153–179.

56.

Zuboff

(1988) In the Age of the Smart Machine: The Future of Work and Power. New York: Basic books.