In recent corporate communications, the term “artificial intelligence” has become a cornerstone, promising innovation and transformation. Despite the excitement, many assertions about AI’s capabilities seem exaggerated, potentially misguiding stakeholders about realistic outcomes and associated risks.
Consider the example of Allbirds, a company known for sustainable footwear, which saw a 600% stock increase after announcing an AI-focused shift. This change will see the company rebrand as NewBird AI and abandon its public benefit status.
Experts in corporate sustainability draw parallels between this “AI washing” and previous instances of greenwashing, where companies overstated their sustainability efforts without substantial actions. Such misleading practices have historically led to backlash against both companies and their communities, and AI washing is poised to surpass greenwashing in scope.
Understanding the Trend
The rise of AI washing can be attributed to four significant challenges, similar to those seen in greenwashing.
Firstly, there is a lack of standardization in AI guidelines. By 2019, 84 AI ethical frameworks existed, a number that grew to over 200 by 2023. This proliferation results in a patchwork of non-binding guidelines from various organizations.
The U.S. regulatory environment contributes to this, relying on fragmented, voluntary AI regulations. The Trump administration’s stance has generally been to resist comprehensive regulation, favoring industry self-governance. In contrast, the European Union AI Act offers a more structured approach, though it will not be fully implemented until 2027.
In the past, sustainability efforts faced similar issues until the introduction of standardized metrics like ESG, allowing for meaningful comparisons and reducing deceptive practices.
AP Photo/Fernando Llano
Secondly, the U.S. lacks comprehensive frameworks to assess AI’s material impact on businesses. The current system often excludes community input, focusing on guidelines from major AI developers like Google and Microsoft.
In the sustainability sector, the EU’s Corporate Sustainability Reporting Directive sets a precedent by requiring companies to disclose material sustainability impacts, promoting transparency across supply chains.
Thirdly, AI claims suffer from a lack of third-party verification, making it easy for companies to mislead. Different auditing practices lead to inconsistent assessments, highlighting the need for independent accreditation systems.
In contrast, the adoption of ESG principles has seen progress in verification through organizations like the Carbon Disclosure Project, which provide independent audits under international standards.
The fourth challenge is the absence of strict enforcement. Previously, ESG relied on reputational pressure, but legal liabilities and financial penalties have since shaped corporate behavior, as seen in cases like Volkswagen’s emissions scandal and BP’s oil spill.
Current AI enforcement is weak, with the benefits of AI washing outweighing potential penalties. However, initiatives like the FTC’s Operation AI Comply aim to address deceptive practices, though recent political shifts have affected its scope.
Charting a Path Forward
For AI washing to be mitigated, companies must embrace stringent standards and audits. Without these, along with clear assessments of AI’s impact and accountability measures, misleading claims will persist. Lessons from sustainability practices show the importance of political support and standardized metrics, underscoring the urgency of adapting these principles to the rapidly evolving AI landscape.
