AI-washing, where companies falsely claim to use artificial intelligence in their products or services, is a growing issue. This article explores AI-washing’s origins, implications, and solutions, supported by case studies and examples. It also examines the role of outsourcing in AI-washing and proposes strategies to restore consumer trust and ethical standards in AI marketing.


Artificial intelligence (AI) has become a cornerstone of modern innovation, promising advancements across various sectors. However, this surge in AI interest has led to a phenomenon known as AI-washing, where companies make misleading claims about their use of AI to attract customers and investors. This article provides a comprehensive analysis of AI-washing, examining its causes, examples, and consequences, and proposes strategies to combat this deceptive practice.

Origins and Drivers of AI-Washing

AI-washing stems from companies’ desires to remain competitive in technology-driven markets. Key drivers include:

  1. Market Pressure: Companies adopt AI branding to stay relevant and secure investment.
  2. Consumer Expectations: Growing fascination with AI prompts businesses to exaggerate AI integration.
  3. Lack of Regulation: Ambiguous AI claims thrive due to the absence of standardized definitions and regulations.
  4. Outsourcing Dynamics: Outsourcing AI development can lead to overstated in-house AI capabilities.

Case Studies and Examples

IBM Watson

IBM’s Watson was promoted as a revolutionary AI for diagnosing diseases and providing treatment plans. Reports revealed that Watson frequently underperformed, highlighting a prominent example of AI-washing.

Chatbots in Customer Service

Many companies claim their customer service chatbots are AI-driven, capable of understanding and resolving complex issues. In reality, these chatbots often rely on simple keyword recognition and scripted responses, falling short of true AI functionalities.


Theranos, a health technology company, falsely claimed to use advanced AI for blood testing. The company’s downfall revealed extensive deceptive practices, including AI-washing, misleading investors and consumers alike.

Credit Scoring Systems

Some financial institutions marketed their credit scoring systems as AI-powered for enhanced accuracy and fairness. Investigations revealed these systems often used basic statistical methods, not advanced AI, leading to public backlash and regulatory scrutiny.

Role of Outsourcing in AI-Washing

Outsourcing significantly contributes to AI-washing, as companies outsource AI development but market these technologies as in-house innovations. Examples include:

  1. Volkswagen’s Emissions Scandal: Volkswagen outsourced software development to cheat emissions tests, falsely claiming advanced AI capabilities for clean technology.
  2. Smart Speaker Manufacturers: Several brands outsource voice recognition technology but advertise these features as proprietary AI innovations.

Individuals and Entities Involved in AI-Washing

Prominent examples of individuals and companies involved in AI-washing include:

  1. Elizabeth Holmes and Theranos: Holmes, the founder of Theranos, was central to the company’s misleading claims about AI-powered blood testing technology.
  2. Former Executives of Predictim: Predictim, a startup claiming to use AI for screening babysitters, faced backlash for overstating their AI algorithms’ capabilities, leading to executive resignations.
  3. Marketing Teams in Tech Companies: Marketing departments in various tech firms often exaggerate AI capabilities to boost product appeal.

Implications of AI-Washing

  1. Erosion of Consumer Trust: Misleading AI claims lead to consumer dissatisfaction and skepticism, undermining trust in AI technologies.
  2. Market Distortion: AI-washing distorts market dynamics by creating an uneven playing field where genuine AI innovations compete with exaggerated claims.
  3. Ethical Concerns: Ethical implications include potential harm to consumers relying on inaccurate AI capabilities and the broader impact on societal trust in technology.

Combating AI-Washing

  1. Regulatory Frameworks: Developing and enforcing regulations to define and standardize AI claims can mitigate AI-washing.
  2. Transparency and Accountability: Companies should adopt transparent practices, clearly outlining their AI systems’ capabilities and limitations.
  3. Consumer Education: Educating consumers about AI technologies and encouraging critical evaluation of AI claims can reduce susceptibility to AI-washing.
  4. Strengthening Outsourcing Contracts: Ensuring outsourcing agreements include clear terms regarding AI capabilities can help maintain accountability and transparency.


AI-washing presents a substantial challenge in the modern technological landscape, threatening consumer trust and the integrity of AI advancements. By understanding the origins, implications, and potential solutions to AI-washing, stakeholders can work towards a more transparent and accountable AI ecosystem. Future research should focus on developing robust frameworks to identify and mitigate AI-washing, ensuring the responsible and ethical deployment of AI technologies.


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  3. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The Ethics of Algorithms: Mapping the Debate. Big Data & Society.
  4. Carreyrou, J. (2018). Bad Blood: Secrets and Lies in a Silicon Valley Startup. Knopf.
  5. Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.
  6. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group.

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