Interpretive Debt: How Organisations Accumulate Hidden Liabilities When Humans Defer Sensemaking to Automated Systems
Keywords:
Freedom of expression Informational privacy Open justice Right to be forgottenAbstract
This paper develops the concept of interpretive debt: a latent organisational liability that accumulates when humans defer sensemaking to automated systems (dashboards, recommender systems, risk scores, and AI assistants) embedded in routine decision-making. Drawing on the technical-debt metaphor, Weickean organisational sensemaking, the human-factors tradition on automation, and the latent-conditions paradigm in safety science, I argue that organisations face a structural choice between performing interpretive work and outsourcing it. Each deferral is locally rational and immediately efficient, yet erodes the collective interpretive capacity required to notice when systems are wrong, to socialise newcomers into genuine understanding, and to reconstruct the rationale behind past decisions. Interpretive debt remains invisible until a repayment event (an audit, crisis, system failure, or generational turnover) forces it due at once. The paper differentiates interpretive debt from adjacent constructs (cognitive debt, epistemic debt, automation bias), specifies three accumulation mechanisms (deferral, opacity, atrophy), proposes three operationalisations, and outlines a research agenda. The contribution is to name an organisational asset that is currently nameless (interpretive capacity) and to integrate four siloed literatures around its erosion.
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[1] Cunningham, W. (1992). The WyCash portfolio management system. Addendum to the Proceedings of the Conference on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA ’92), 29–30. Association for Computing Machinery. https://doi.org/10.1145/157709.157715
[2] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 28, pp. 2503–2511). Curran Associates.
[3] Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task (arXiv preprint No. 2506.08872). arXiv. https://doi.org/10.48550/arXiv.2506.08872
[4] Storey, M.-A. (2026). From technical debt to cognitive and intent debt: Rethinking software health in the age of AI (arXiv preprint No. 2603.22106). arXiv. https://doi.org/10.48550/arXiv.2603.22106
[5] Sankaranarayanan, S. (2026). Mitigating “epistemic debt” in generative AI-scaffolded novice programming using metacognitive scripts. In Proceedings of the 13th ACM Conference on Learning at Scale (L@S ’26). Association for Computing Machinery. https://arxiv.org/abs/2602.20206
[6] Zuboff, S. (1988). In the age of the smart machine: The future of work and power. Basic Books.
[7] Weick, K. E., & Sutcliffe, K. M. (2015). Managing the unexpected: Sustained performance in a complex world (3rd ed.). Jossey-Bass.
[8] Fowler, M. (2009, October 14). Technical debt quadrant. MartinFowler.com. https://martinfowler.com/bliki/TechnicalDebtQuadrant.html
[9] Dudycz, O. (2024, June 13). Tech debt doesn’t exist, but trade-offs do. Architecture Weekly. https://www.architecture-weekly.com/p/tech-debt-doesnt-exist-but-trade
[10] Mosier, K. L., Skitka, L. J., Heers, S., & Burdick, M. D. (1998). Automation bias: Decision making and performance in high-tech cockpits. International Journal of Aviation Psychology, 8(1), 47–63. https://doi.org/10.1207/s15327108ijap0801_3
[11] Weick, K. E. (1995). Sensemaking in organizations. Sage.
[12] Weick, K. E. (1993). The collapse of sensemaking in organizations: The Mann Gulch disaster. Administrative Science Quarterly, 38(4), 628–652. https://doi.org/10.2307/2393339
[13] Weick, K. E. (1990a). The vulnerable system: An analysis of the Tenerife air disaster. Journal of Management, 16(3), 571–593. https://doi.org/10.1177/014920639001600304
[14] Maitlis, S., & Christianson, M. (2014). Sensemaking in organizations: Taking stock and moving forward. Academy of Management Annals, 8(1), 57–125. https://doi.org/10.5465/19416520.2014.873177
[15] Gioia, D. A., & Chittipeddi, K. (1991). Sensemaking and sensegiving in strategic change initiation. Strategic Management Journal, 12(6), 433–448. https://doi.org/10.1002/smj.4250120604
[16] Pratt, M. G. (2000). The good, the bad, and the ambivalent: Managing identification among Amway distributors. Administrative Science Quarterly, 45(3), 456–493. https://doi.org/10.2307/2667106
[17] Stigliani, I., & Ravasi, D. (2012). Organizing thoughts and connecting brains: Material practices and the transition from individual to group-level prospective sensemaking. Academy of Management Journal, 55(5), 1232–1259. https://doi.org/10.5465/amj.2010.0890
[18] Weick, K. E. (1990b). Technology as equivoque: Sensemaking in new technologies. In P. S. Goodman, L. S. Sproull, & Associates (Eds.), Technology and organizations (pp. 1–44). Jossey-Bass.
[19] Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779. https://doi.org/10.1016/0005-1098(83)90046-8
[20] Christoffersen, K., & Woods, D. D. (2002). How to make automated systems team players. In E. Salas (Ed.), Advances in human performance and cognitive engineering research (Vol. 2, pp. 1–12). Elsevier.
[21] Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253. https://doi.org/10.1518/001872097778543886
[22] Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. https://doi.org/10.1177/0018720810376055
[23] Endsley, M. R. (2017). From here to autonomy: Lessons learned from human–automation research. Human Factors, 59(1), 5–27. https://doi.org/10.1177/0018720816681350
[24] Reason, J. (1990). Human error. Cambridge University Press.
[25] Reason, J. (2000). Human error: Models and management. BMJ, 320(7237), 768–770. https://doi.org/10.1136/bmj.320.7237.768
[26] Perrow, C. (1984). Normal accidents: Living with high-risk technologies. Basic Books.
[27] Vaughan, D. (1996). The Challenger launch decision: Risky technology, culture, and deviance at NASA. University of Chicago Press.
[28] Dekker, S. (2011). Drift into failure: From hunting broken components to understanding complex systems. Ashgate.
[29] Walsh, J. P., & Ungson, G. R. (1991). Organizational memory. Academy of Management Review, 16(1), 57–91. https://doi.org/10.5465/amr.1991.4278992
[30] Polanyi, M. (1966). The tacit dimension. Doubleday.
[31] Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.
[32] Braverman, H. (1974). Labor and monopoly capital: The degradation of work in the twentieth century. Monthly Review Press.
[33] Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
[34] Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive consequences of having information at our fingertips. Science, 333(6043), 776–778. https://doi.org/10.1126/science.1207745
[35] Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12. https://doi.org/10.1177/2053951715622512
[36] Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645
[37] Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
[38] Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005
[39] Pandey, J. K., & Kumar, R. (2025). Governing the algorithmic agent: Confronting overt and covert challenges to justice and the future of work. International Journal of Law Management & Humanities, 8(4), 1974–1984. https://doij.org/10.10000/IJLMH.1110648
[40] Casner, S. M., Geven, R. W., Recker, M. P., & Schooler, J. W. (2014). The retention of manual flying skills in the automated cockpit. Human Factors, 56(8), 1506–1516. https://doi.org/10.1177/0018720814535628
[41] National Transportation Safety Board. (2014). Descent below visual glidepath and impact with seawall, Asiana Airlines Flight 214, Boeing 777-200ER, HL7742, San Francisco, California, July 6, 2013 (Aircraft Accident Report NTSB/AAR-14/01). https://www.ntsb.gov/investigations/AccidentReports/Reports/AAR1401.pdf
[42] Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121–127. https://doi.org/10.1136/amiajnl-2011-000089
[43] Lalitha Kumari, P., & Pandey, J. K., et al. (2021). Impact of artificial intelligence on employees’ performance in information technology sector. AIP Conference Proceedings, 2587(1). https://doi.org/10.1063/5.0150403
[44] Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32–64. https://doi.org/10.1518/001872095779049543
[45] Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
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