PaperlayerView exact revision on arXiv

Source-linked AI summary

When the Algorithm Becomes the Brand Crisis: A Sociotechnical Theory of Distributed Responsibility and Accountable Transparency

Mohammad Saleh Torkestani, Taha Mansouri

arXiv:2609.00510v1cs.AI

TL;DR

AI-related episodes cannot be understood solely as machine events because multiple actors may become relevant to stakeholder judgment. The paper develops a proposed construct framework through a structured, federated scoping synthesis, distinguishing AI incidents, organisational crises, and scandals. The synthesis does not support a uniform claim that AI amplifies reputational harm; evidence is stronger for immediate appraisals such as attribution, trust, satisfaction, and firm evaluation.

  • Problem

    AI-related episodes cannot be understood solely as machine events because multiple actors may become relevant to stakeholder judgment.

    Source: §1 · p. 2

  • Method

    The paper develops a proposed construct framework through a structured, federated scoping synthesis, distinguishing AI incidents, organisational crises, and scandals.

    Source: §1 · p. 2, A nested account of incident, crisis,… · p. 4

  • Results

    The synthesis does not support a uniform claim that AI amplifies reputational harm; evidence is stronger for immediate appraisals such as attribution, trust, satisfaction, and firm evaluation.

    Source: §1 · pp. 2–3

  • Takeaways & Limitations

    The framework treats stakeholder judgment in AI-related episodes as involving actors beyond the machine.

    Source: §1 · p. 2

    The direct literature is strongest for immediate appraisals and does not establish estimates of brand equity, sales, employee trust, or durable reputation.

    Source: §1 · p. 3

Abstract

from arXiv · show

Artificial intelligence systems increasingly enact market-facing promises through chatbots, recommendation systems, automated decisions, and generative interfaces. Their failures, misuse, and misrepresentation raise a question that conventional brand-crisis models do not fully specify: how do stakeholders assign responsibility when technical causation, customer-facing control, and governance duties are distributed across an AI system, developer, deployer, vendor, and user? This conceptual paper develops a sociotechnical process theory from a structured, federated scoping synthesis of verified academic and primary sources. It distinguishes an AI/algorithmic incident from an AI-related organisational crisis and, in turn, from an AI-related organisational scandal. The framework proposes that incident configuration shapes actor-specific attribution; attribution informs capability, integrity, fairness, and relationship appraisals; and public moralisation may, but need not, escalate an incident into scandal. The theory offers a reconciliation of findings that algorithm involvement can buffer negative brand reactions in some settings while robot and chatbot failures can redirect responsibility to an associated firm in others. It introduces accountable transparency as a proposed response configuration that combines timely notice, an intelligible account, role-responsibility acknowledgement, remedy, evidence of correction, and recourse. The evidence supports conditional, proximal inferences about blame, trust, satisfaction, firm evaluation, and communication credibility more strongly than claims about durable reputation, brand equity, or market performance.

1 Introduction

The paper theorizes how AI-mediated brand promises and distributed actor roles shape the progression from incidents to organisational crises or scandals, and their market-facing consequences.

1 Introduction

  • AI-related harm may implicate multiple actors in stakeholder judgment, so an episode is neither merely a machine error nor necessarily a conventional single-firm crisis.The relevant actors can include developers, integrators, professionals, and consumer-facing firms.Source: §1 · p. 2
  • In robot and chatbot service-failure studies, respondents may assign less responsibility to the automated service provider but more to the associated firm.These results use different technologies, settings, comparison conditions, and judgment targets, so they are not commensurate estimates.Source: §1 · p. 2
  • The framework distinguishes AI/algorithmic incidents, AI-related organisational crises, and AI-related organisational scandals, then maps five actor-specific responsibility judgments.Those judgments are causal contribution, role responsibility, moral blame, perceived legal duty, and sanction preference.Source: §1 · p. 2
  • The paper proposes that output errors can convert capability judgments into appraisals of organisational integrity, and defines accountable transparency as a future-facing response construct.It identifies conditions for evaluating the construct rather than claiming that disclosure alone restores outcomes.Source: §1 · p. 3
  • Evidence is strongest for immediate appraisals, while durable reputation, brand equity, sales, employee trust, and spillover outcomes require field and longitudinal tests.Regulatory, tribunal, and company records document incidents and formal responses but generally do not estimate these longer-run outcomes.Source: §1 · p. 3

2 Construct System and Evidence Boundary

The section defines a nested incident–crisis–scandal construct, treats AI-related problems as organisationally situated rather than merely technical, and bounds its claims as a federated scoping synthesis rather than a fully systematic review.

A nested account of incident, crisis, and scandal

  • An incident is a materially AI-related event; it becomes an organisational crisis when it threatens stakeholder relationships, reputation, or legitimacy and prompts a response.That response may involve explanation, remedy, correction, or governance action; incidents can be private service failures without public attention.Source: A nested account of incident, crisis,… · p. 3
  • An AI-related scandal is a crisis subset that crosses a public escalation threshold through wider diffusion, moral framing, and cross-audience condemnation or sustained controversy.Public salience and moralisation are escalation conditions, not requirements for every crisis; early organisational response may affect amplification.Source: A nested account of incident, crisis,… · p. 4
  • Public attention is neither a proxy for harm nor evidence of durable brand damage: a viral incident may be brief, while a quieter high-stakes incident can threaten legitimacy.The framework distinguishes incident, crisis, or scandal as objects of explanation from outcomes such as trust, firm evaluation, legitimacy, or durable brand equity.Source: A nested account of incident, crisis,… · p. 4
  • “AI” covers automated decision and interaction systems when materially relevant; probabilism, adaptivity, generativity, embodiment, and autonomy are potential moderators, not defining conditions.These properties may shape perceived agency, predictability, controllability, and appropriate reliance, so different systems need not produce identical stakeholder responses.Source: A nested account of incident, crisis,… · pp. 4–5

2.2 Why an AI

  • The analysis shifts from machine error to an organisationally attributable episode in a distributed human–AI arrangement, where governance and explanation shape judgments about the firm.Opacity can reflect an organisation’s inability or unwillingness to explain decisions and responsibilities, while system conduct can confirm or violate market-facing promises.Source: §2.2 · p. 5

Evidence synthesis and inferential discipline

  • The framework draws on a structured, federated scoping synthesis using scholarly and publisher/DOI records, search systems, and backward/forward citation chaining.Its working search logic combined technology, organisation, incident, and outcome terms, and its synthesis map classified direct, closely related, transferred-theory, and primary-case evidence.Source: Evidence synthesis and inferential… · pp. 5–6
  • The review is not a fully systematic database review and does not claim exhaustive coverage, a PRISMA flow, pooled effects, or prevalence.It lacked a prospectively registered protocol, independent duplicate screening, and inter-coder reliability assessment; evidence is marked [E] for direct, [T] for transferred theory, and [P] for proposed relationships.Source: Evidence synthesis and inferential… · p. 6

3 A Responsibility -Network Process Theory

This section develops a context-sensitive responsibility-network process theory linking incident configuration and anthropomorphism to actor-specific attribution and system and brand appraisals, and proposes accountable transparency and response-violation fit while treating scandal escalation and spillover as conditional and making longer-run outcomes and feedback through learning, revised expectations, and regulatory change explicit.

3 A Responsibility -Network Process Theory

  • Figure 1 models incident configuration, attribution, and appraisal as a process that can produce organisational responses and proximal outcomes without public scandal.Longer-run outcomes are theoretically relevant but largely unmeasured in direct AI–brand research; learning, revised expectations, and regulatory changes may alter later incidents.Source: §3 · p. 6, Figure 1 · p. 7

3.1 Stage 1: Incident configuration

  • Incident configuration—not a uniform “AI failure” category—includes harm, risk, severity, affected stakeholders, prior promises, governance, visibility, recurrence, exposure, and control.Control includes selecting and configuring the system, setting tasks, reviewing outputs, designing interfaces, monitoring performance, and providing redress.Source: §3.1 · p. 7
  • Perceived foreseeability of a failure class may increase organisational role responsibility through perceived preventability, beyond whether the specific output was predictable.This proposed relationship is expected to be stronger for high-duty-of-care tasks and where the organisation has meaningful safeguards or redress control; it is not a claim of strict legal liability.Source: §3.1 · p. 8

3.2 Stage 2: Actor -specific attribution

  • Attribution is not a zero-sum human–AI contest: causal contribution, role responsibility, moral blame, perceived legal duty, and sanction preference are distinct judgments.Perceived legal duty is a lay inference, not a determination of law.Source: §3.2 · p. 8
  • Perceived deployer control over task selection, configuration, presentation, monitoring, and redress is proposed to predict role responsibility through preventability, conditioned by perceived duty of care.AI-attributed causal contribution may coexist with, rather than reduce, deployer responsibility.Source: §3.2 · p. 9

Stage 3: Capability, integrity, and relationship appraisal

  • Technical errors may damage perceived capability, while negligent delegation, known limitations, concealment, absent recourse, or misleading AI claims may prompt a distinct integrity appraisal.The theory proposes that perceived integrity violations mediate links between governance cues and organisational moral blame; an AI claim–capability mismatch can itself be an integrity-relevant incident.Source: Stage 3: Capability, integrity, and… · pp. 9–10

Stage 4: Anthropomorphism and system agency

Stage 5: Organisational response and accountable transparency

  • Accountable transparency combines timely notice, an intelligible account of facts and uncertainty, role acknowledgement, immediate remedy, correction evidence, and accessible recourse, distinguishing it from explanation alone.Source: Stage 5: Organisational response and… · p. 11
  • The proposed response configuration is expected to improve actor-specific trust-repair evaluations over source disclosure alone through fairness and credibility, with response fit varying by violation type and capability versus integrity concerns.Capability-dominant incidents call for technical correction and risk calibration paired with accurate acknowledgement; integrity-dominant incidents call for role acknowledgement, remedy, and governance reform beyond technical correction alone.Source: Stage 5: Organisational response and… · p. 12

Stage 6: Optional scandal escalation and outcome differentiation

  • A crisis becomes a scandal when it diffuses beyond affected parties and involves moralised condemnation and demands for institutional accountability or sanction, not merely high view counts or negative headlines.Source: Stage 6: Optional scandal escalation… · p. 12
  • Scandal escalation is proposed as more likely when cross-audience moralisation and diffusion combine with organisational attribution and institutional demands, strengthened by victim vulnerability, absent recourse, recurrence, and public-risk narratives.Source: Stage 6: Optional scandal escalation… · pp. 12–13
  • Immediate message credibility, actor-specific trust, firm evaluation, complaint, and withdrawal intentions are distinct from durable brand equity, market behaviour, institutional legitimacy, and ecosystem spillover; direct evidence of AI-related spillover remains limited.Product-harm research finds category spillover in some markets, but direct evidence after AI-related incidents is limited.Source: Stage 6: Optional scandal escalation… · p. 13
  • After a publicly escalated AI-related scandal, negative evaluation may spill over to associated developers, deployers, or the AI category insofar as observers perceive technological and governance linkage.Perceived shared control, co-brand salience, and coordinated responses are proposed mediators.Source: Stage 6: Optional scandal escalation… · p. 13

4 Illustrative Cases and the Limits of Case Inference

Cases make the sociotechnical actor chain observable, but they do not prove proposed mechanisms, estimate prevalence, or demonstrate durable reputation effects. They support a careful analysis of deployment responsibilities, stakeholder attribution, and remedy without establishing uniform vendor non-shielding or inevitable brand damage.

4 Illustrative Cases and the Limits of Case Inference

  • Cases neither prove the proposed mechanisms or estimate prevalence nor establish inevitable durable brand damage, which remains a theoretical outcome motivating P2.Source: §4 · p. 13
  • Primary and authoritative records make the actor chain observable across interfaces, safety programmes, data practices, vendor relationships, representation, and post-incident accounts.Each source is used only for the fact type it can establish.Source: §4 · p. 13
  • Cases do not show uniform vendor non-shielding; vendor ties do not remove the need to analyze deployment responsibilities, stakeholder attribution, and remedy.Source: §4 · p. 13

5 Implications for Marketing Theory

The framework specifies how AI-enabled exchange distributes responsibility across technical and governance relationships, and proposes governability and accountable transparency as marketing-relevant concepts. It identifies research questions about stakeholder relationships, service oversight, and the effects of disclosure and corrective processes.

From a focal firm to a responsibility network

  • AI-enabled exchange adds a missing granularity: technical and governance relationships among actors matter because capability, decision rights, customer access, information, and redress can cue distinct responsibility judgments.Source: From a focal firm to a responsibility… · p. 14
  • A brand-facing AI creates stakeholder–organisation and stakeholder–agent relationships, sometimes alongside a developer relationship, prompting research on trust transfer and when bots or vendors become salient.Source: From a focal firm to a responsibility… · p. 14

From reliable products to governable services

  • Because stochastic outputs and changing deployment contexts make universal error elimination implausible, the paper proposes governability: an organisation’s capacity to identify risks, constrain use, document decisions, monitor performance, correct harm, and provide recourse.Governability is offered as a future construct, not measured or validated here, and is proposed for scale development and comparative research.Source: From reliable products to governable… · p. 14

From disclosure to accountable transparency

  • Rather than treating AI disclosure alone as sufficient, accountable transparency asks whether a brand provides an intelligible, challengeable account linked to procedural fairness, corrective action, and stakeholder relationships.Disclosure can signal honesty but can also lower expectations of care or raise authenticity concerns.Source: From disclosure to accountable… · pp. 14–15
  • Future studies should test whether accountable transparency is formative, whether components substitute for one another, and how effects vary when AI involvement is material, legally required, expected, or irrelevant.Source: From disclosure to accountable… · p. 15

6 Managerial and Policy Implications

The section offers precautionary governance guidance for managers, developers, and policymakers, while emphasizing that these practices are not a universal repair playbook or proven crisis-prevention remedy.

6 Managerial and Policy Implications

  • Before deployment, map actors and control, foreseeable failures and affected groups, escalation and recourse needs, AI claims, and monitoring, versioning, and vendor responsibilities as governance evidence—not crisis guarantees.Source: §6 · p. 15
  • At detection, distinguish technical causation, organisational responsibility, and legal fault while acknowledging needs, preserving evidence, remedying where possible, and accurately communicating known facts.These actions need not prejudge liability or root cause; disclosure varies by jurisdiction, task, contract, and harm.Source: §6 · p. 15
  • Blaming a vendor or AI without explaining the organisation’s customer-facing role may raise integrity concerns, but this remains an empirical prediction, not a universal effect.Source: §6 · p. 15
  • Developers’ and platforms’ downstream governance support can help deployers provide stakeholders with a more credible account.Support can include limitations information, context-appropriate instructions, monitoring hooks, incident-notification pathways, safe fallbacks, and version traceability.Source: §6 · p. 15
  • Policymakers should clarify cross-actor duties and remedies; the paper aligns with risk-based governance frameworks but does not test their effectiveness or establish that compliance prevents crises or repairs reputation.This is consistent with the NIST AI Risk Management Framework and the EU AI Act (European Parliament and Council of the European Union, 2024; Tabassi, 2023), not evidence of effectiveness.Source: §6 · p. 15

7 Limitations and Conclusion

The framework argues that AI changes how responsibility is explained, not that AI failures are inherently more scandal-prone or inevitably damaging to brand equity. It distinguishes contained service failures, organisational crises, and public scandals while calling for direct validation of its distinctive claims.

7 Limitations and Conclusion

  • The theory-building synthesis is not exhaustive and lacks duplicate screening, formal risk-of-bias assessment, database denominators, and a reproducible numerical flow.The authors characterize these omissions as falling short of a full systematic review.Source: §7 · p. 16
  • The evidence base is heterogeneous, concentrated in experimental, customer-service, chatbot, and robot settings, and illustrated by purposively selected rather than representative cases.Legal decisions, regulatory actions, settlements, and company accounts have different evidentiary status and cannot serve as a uniform measure of stakeholder judgment.Source: §7 · p. 16
  • The framework’s distinctive claims—including foreseeability, multi-actor responsibility, accountable transparency, scandal escalation, governability, and governance-linkage spillover—require direct validation.The limitations justify restraint, not inaction.Source: §7 · p. 16
  • AI outputs can be linked to a model while stakeholders judge a brand’s control, role, candour, and capacity to remedy the problem.The framework shifts analysis from whether “the AI” or “the brand” is blamed to how responsibility networks shape market relationships under technological uncertainty.Source: §7 · p. 16
  • An error may remain a service failure, develop into an organisational crisis, or become a public scandal when audiences moralise and amplify it.These pathways distinguish escalation without claiming that AI failures are more scandal-prone than ordinary software failures or inevitably erode brand equity.Source: §7 · p. 16

A Structured Federated Scoping Protocol and Source -Role · Regis ter · A. 1 Search and screening logic

The federated scoping protocol combined a broad AI-and-organisation query with topic-specific searches, relevance-based screening, documented case criteria, and source-level coding.

A. 1 Search and screening logic

  • The conceptual query combined AI and automated-system terms with organisational or service terms and crisis, failure, reputation, trust, blame, or accountability outcomes.Its search blocks also included harm, misconduct, backlash, and legitimacy.Source: A. 1 Search and screening logic · p. 16
  • Additional searches paired algorithmic systems with responsibility, controllability, agency, service failure and recovery, disclosure, AI washing, privacy, bias, safety, and crisis communication.Source: A. 1 Search and screening logic · p. 16
  • Sources were retained for direct AI-and-organisational evidence, relevant AI mechanisms, transferable brand or crisis theory, or authoritative case documentation.Source: A. 1 Search and screening logic · pp. 16–17
  • Purely technical benchmark papers without organisational or stakeholder relevance were excluded.Source: A. 1 Search and screening logic · p. 17
  • Case records required material AI involvement, an identifiable organisation, and authoritative documentation of the incident, response, or legal or regulatory posture.Source: A. 1 Search and screening logic · p. 17
  • Each source was coded by evidence role, outcome target, design or posture, and safe inferential use.Source: A. 1 Search and screening logic · p. 17

A. 2 Coding distinction used in the paper · intelligence. https: //www.sec.gov/newsroom/press -releases/2024-36

The paper separates direct evidence, transferred theoretical foundations, and new propositions, limiting claims to what sources actually support and what remains to be tested.

A. 2 Coding distinction used in the paper

  • The coding distinguishes direct evidence ([E]), transferred foundation ([T]), and new proposition ([P]).These categories separate measured outcomes from theoretical mechanisms and relationships newly introduced by the paper.Source: A. 2 Coding distinction used in the… · p. 17
  • Direct evidence supports only the outcomes actually measured.The coding therefore does not extend a measured result to unmeasured outcomes.Source: A. 2 Coding distinction used in the… · p. 17
  • Transferred sources support candidate mechanisms and are treated as theoretical foundations.They provide a qualified basis for mechanisms rather than direct evidence of outcomes.Source: A. 2 Coding distinction used in the… · p. 17
  • New propositions are distinctive relationships introduced by the paper and require empirical testing.The coding distinguishes these proposed relationships from findings already established by source evidence.Source: A. 2 Coding distinction used in the… · p. 17
  • This coding prevents case records or product-harm results from proving a long-run AI-specific reputation effect.The paper thus avoids treating such evidence as support for a durable reputation claim.Source: A. 2 Coding distinction used in the… · p. 17
Loading 2609.00510v1…