The Attribution Problem in Corporate Communication

This week, three PR experts issued a pointed warning to corporate executives through Business Insider: do not blame AI for layoff decisions that belong to the C-suite. The immediate context is a pattern of executive communication in which terms like "lower-value human capital" appear alongside vague gestures toward algorithmic necessity, as if workforce reductions were outputs of a system rather than choices made by people with titles and compensation packages. The PR experts framed this primarily as a reputation management problem. I want to argue it is something more structurally interesting than that.

The advice these experts gave is essentially communicative hygiene: choose words that do not dehumanize employees, and take visible ownership of decisions. That is reasonable counsel. But the deeper issue is that the tendency to attribute layoff decisions to AI is not simply a clumsy rhetorical choice. It reflects a specific and consequential misunderstanding of what algorithms actually do inside organizations, and it has real implications for how organizations govern themselves in algorithmically mediated environments.

Misattribution as Organizational Symptom

Kellogg, Valentine, and Christin (2020) documented how algorithmic management systems create what they describe as a transfer of control from managers to code, allowing organizations to obscure the human authorship of labor decisions. When a CEO implies that AI recommended a workforce reduction, they are not simply spinning a narrative. They are enacting exactly the kind of accountability displacement that algorithmic management research has identified as a structural feature of these systems. The algorithm becomes a convenient principal, and the executive becomes, rhetorically at least, a subordinate carrying out instructions.

This matters for organizational theory because it blurs the boundary between what Sundar (2020) calls machine agency and human agency in consequential decision contexts. Sundar's framework for AI-mediated communication distinguishes between systems where machines are genuinely driving outputs and systems where machines are providing information that humans then act upon. Most corporate layoff processes fall clearly into the second category. Workforce reduction decisions involve human judgment about strategy, margins, investor relations, and competitive positioning. The AI component, if present at all, may be limited to data aggregation or scenario modeling. Executives who communicate otherwise are not describing a machine agent; they are constructing one rhetorically to absorb blame.

The Topology Problem in Executive Communication

My ALC framework draws a distinction between topology and topography: knowing the shape of a constraint differs from knowing how to navigate it. The PR experts cited in this week's reporting are offering topographic advice, specific phrasings to avoid, alternative framings to adopt. That is useful, but it does not address the topological question of why executives reach for AI attribution in the first place.

The topology here is a legitimacy structure. Organizations operating in AI-saturated environments have discovered that algorithmic attribution carries a kind of epistemic authority that human judgment alone does not. Decisions framed as data-driven or algorithmically informed face less scrutiny than decisions framed as executive choices, because the former implies objectivity and the latter implies interest. This is not a new observation, but the current wave of AI adoption has amplified the effect considerably. Executives are not simply making poor communication choices; they are rationally exploiting a structural feature of how their audiences interpret algorithmic versus human agency.

This connects to what Hancock, Naaman, and Levy (2020) described as the credibility transfer problem in AI-mediated communication: outputs associated with computational systems inherit a kind of neutral authority that the same outputs, attributed to a human, would not receive. The PR experts are right that this strategy tends to fail over time, because employees and journalists eventually identify the gap between the attributed cause and the visible decision-making chain. But the short-term incentive to exploit credibility transfer remains strong.

What Organizational Theory Predicts Here

Rahman's (2021) work on the invisible cage is instructive. Rahman argues that algorithmic management systems constrain worker behavior not through visible rules but through informational asymmetries that workers cannot directly observe or contest. The current pattern in executive layoff communication inverts this dynamic in a revealing way: the executives who design these asymmetric systems are now using the opacity of those same systems as rhetorical cover when the systems produce socially costly outcomes. The cage that was invisible to workers becomes, in the CEO's press statement, the agent responsible for putting people out of work.

The PR advice to take ownership of C-suite decisions is correct. But it will remain difficult to follow as long as organizations are structured to treat algorithmic outputs as authoritative and human judgment as a liability. The communication failure these experts are diagnosing is a downstream symptom of a governance structure that has not worked out who is actually accountable when algorithmically informed decisions produce bad outcomes for workers. Until that structural question is resolved, executives will keep reaching for the algorithm as a shield, regardless of how many PR consultants tell them not to.

References

Hancock, J. T., Naaman, M., and Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89-100.

Kellogg, K. C., Valentine, M. A., and Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410.

Rahman, H. A. (2021). The invisible cage: Workers' reactivity to opaque algorithmic evaluations. Administrative Science Quarterly, 66(4), 945-988.

Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human-AI interaction. Journal of Computer-Mediated Communication, 25(1), 74-88.