AI Washing

 The article detailing the issue is titled "Artificial Intelligence (AI) Washing in Taxation: Ethics and Transparency", authored by Dr. Preeti Jain, an Academician.

AI washing is defined as the practice of exaggerating or falsifying the application of artificial intelligence (AI) in goods, services, or solutions to make them appear more sophisticated. This term is derived from "greenwashing".

The article investigates the impact of AI washing on taxation, focusing on how it diminishes public trust, disrupts tax authorities’ efforts, and leads to unethical practices.

Key Concepts and Findings of the Article

Definition and Mechanism AI washing occurs when tax-related software or services make claims about using advanced AI in tax preparation, escalation, and fraud detection, but actually rely on simple algorithms and analytical methods.

• The problem is pervasive; studies suggest that up to 70% of startup companies falsely claim AI capabilities to secure funding.
• Companies often use vague language and exaggerated success claims.
• The "black-box" technology nature of AI makes it simpler for businesses to engage in AI washing by hiding the real functioning of their systems, making it challenging for consumers or authorities to confirm whether AI is truly being applied.

Impact on Trust and Decision-Making AI washing in taxation is a major problem because:

• It diminishes shareholders’ trust and can lead to potential regulatory breaches.
• It causes businesses and taxpayers to place too much confidence in faulty AI systems, potentially leading to poor decision-making and increased exposure to risks.
• The complexity of AI makes it difficult for tax authorities to identify and control AI washing, especially since regulators often lack the expertise to examine these claims carefully.

Ethical Implications AI washing raises several ethical concerns:

1. Privacy and Data Security: AI tax systems require financial and personal data, necessitating strict data protection guidelines.
2. Bias and Impartiality: If AI systems are trained on biased past data, they may make judgments that unfairly affect particular taxpayer groups.
3. Transparency and Accountability: The "black-box" nature challenges accountability, making it crucial for tax authorities to provide clear interpretations of how AI systems function. Strategies like Explainable AI (XAI), auditable logs, and Human-in-the-loop (HITL) are recommended to ensure transparency.

Case Studies (Unethical Implications) The article cites examples of potential misconduct:

• A business offering tax filing services allegedly diverted users from free file programs to premium products, seeking gain from AI-enhanced marketing rather than AI-driven tax preparation benefits.
• Certain tax preparation firms were accused of misleading information regarding their AI-powered software capabilities, where anticipations did not meet marketing claims.
• A financial services company was accused of exaggerating AI capabilities in its tax preparation services as a marketing strategy primarily to attract users and gather data.

Regulatory Measures in India While there are currently no formal standards specifically on AI washing, AI systems used in tax operations in India are bound by the CBDT’s standards. Additionally, the Digital Personal Data Protection Act (DPDPA), passed in August 2023, sets guidelines for AI systems handling personal and financial data.