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AI Washing and the Erosion of Digital Legitimacy: A Socio-Technical Perspective on Responsible Artificial Intelligence in Business

Nelly Elsayed

TL;DR

This paper frames AI washing as a socio-technical form of digital misrepresentation that erodes trust and legitimacy in AI-enabled business contexts. Building on greenwashing and IS signaling/trust literature, it defines AI washing, offers a four-type typology, and develops a socio-technical framework highlighting how technical opacity and symbolic signaling jointly sustain legitimacy narratives. It analyzes multi-level impacts—firm, industry, and system—and discusses governance, ethics, and responsible innovation as central to counteracting AI washing. The work lays out future directions for measurement, regulation, detection, and governance to promote credible AI adoption and protect the integrity of AI-driven transformation.

Abstract

The rapid evolution of artificial intelligence (AI) systems, tools, and technologies has opened up novel, unprecedented opportunities for businesses to innovate, differentiate, and compete. However, growing concerns have emerged about the use of AI in businesses, particularly AI washing, in which firms exaggerate, misrepresent, or superficially signal their AI capabilities to gain financial and reputational advantages. This paper aims to establish a conceptual foundation for understanding AI washing. In this paper, we draw on analogies from greenwashing and insights from Information Systems (IS) research on ethics, trust, signaling, and digital innovation. This paper proposes a typology of AI washing practices across four primary domains: marketing and branding, technical capability inflation, strategic signaling, and governance-based washing. In addition, we examine their organizational, industry, and societal impacts. Our investigation and analysis reveal how AI washing can lead to short-term gains; however, it also proposes severe long-term consequences, including reputational damage, erosion of trust, and misallocation of resources. Moreover, this paper examines current research directions and open questions aimed at mitigating AI washing practices and enhancing the trust and reliability of legitimate AI systems and technologies.

AI Washing and the Erosion of Digital Legitimacy: A Socio-Technical Perspective on Responsible Artificial Intelligence in Business

TL;DR

This paper frames AI washing as a socio-technical form of digital misrepresentation that erodes trust and legitimacy in AI-enabled business contexts. Building on greenwashing and IS signaling/trust literature, it defines AI washing, offers a four-type typology, and develops a socio-technical framework highlighting how technical opacity and symbolic signaling jointly sustain legitimacy narratives. It analyzes multi-level impacts—firm, industry, and system—and discusses governance, ethics, and responsible innovation as central to counteracting AI washing. The work lays out future directions for measurement, regulation, detection, and governance to promote credible AI adoption and protect the integrity of AI-driven transformation.

Abstract

The rapid evolution of artificial intelligence (AI) systems, tools, and technologies has opened up novel, unprecedented opportunities for businesses to innovate, differentiate, and compete. However, growing concerns have emerged about the use of AI in businesses, particularly AI washing, in which firms exaggerate, misrepresent, or superficially signal their AI capabilities to gain financial and reputational advantages. This paper aims to establish a conceptual foundation for understanding AI washing. In this paper, we draw on analogies from greenwashing and insights from Information Systems (IS) research on ethics, trust, signaling, and digital innovation. This paper proposes a typology of AI washing practices across four primary domains: marketing and branding, technical capability inflation, strategic signaling, and governance-based washing. In addition, we examine their organizational, industry, and societal impacts. Our investigation and analysis reveal how AI washing can lead to short-term gains; however, it also proposes severe long-term consequences, including reputational damage, erosion of trust, and misallocation of resources. Moreover, this paper examines current research directions and open questions aimed at mitigating AI washing practices and enhancing the trust and reliability of legitimate AI systems and technologies.
Paper Structure (19 sections, 5 figures, 4 tables)

This paper contains 19 sections, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Greenwashing as a Conceptual Precursor to AI Washing
  4. Conceptual Framework: Defining and Categorizing AI Washing
  5. Conceptual Differentiation Between AI Washing and Greenwashing: Toward a Socio-Technical Understanding of Digital Legitimacy
  6. From Environmental to Digital Legitimacy
  7. Mechanisms of Misrepresentation: Symbolic, Technical, and Organizational Dimensions
  8. Mechanisms of Legitimacy Construction
  9. A Socio-Technical Framework of Digital Legitimacy
  10. The Socio-Technical Nature of AI Washing
  11. AI Adoption in Business Contexts
  12. Types of AI Washing in Business
  13. Insights from IS Research on Ethics, Trust, and Innovation
  14. Challenges and Impacts of AI Washing on Businesses and Information Systems
  15. Firm-Level Impacts
  16. ...and 4 more sections

Figures (5)

  • Figure 1: The AI washing within the broader context of deceptive corporate practices, using greenwashing as the historical reference point.
  • Figure 2: A socio-technical framework of digital legitimacy and AI washing.
  • Figure 3: A dynamic framework for AI washing in business contexts.
  • Figure 4: Multi-level impacts of AI washing on firms, industries, and socio-technical systems.
  • Figure 5: Conceptual overview of future research directions and open questions on AI washing.