How AI Legitimises Flawed Human Responses: A Payments Industry Case Study
In an era where artificial intelligence (AI) is increasingly trusted as an authoritative source of information, a subtle but dangerous phenomenon is emerging – the tendency for AI to legitimise flawed or misleading human responses by synthesising and amplifying them. As an industry veteran (old geezer) in the payments sector, I’ve spent decades battling misinformation purveyed by payment professionals – both individually and within the safety of their likeminded crowd – making assumptions without reference to manuals or guides and then having the nerve to accuse me of failing to understand because I didn’t agree with their misguided logic! This approach to information gathering is then further exacerbated by reference to poorly researched publications and self-referential industry narratives.
A recent exploration of Visa Account Funding Transactions (AFTs) and the classification of Apple Pay as a “staged wallet” revealed how AI can inadvertently perpetuate these errors, reinforcing “idiot responses” from humans and re-presenting them as fact. This article examines this issue through a payments industry lens, using the Apple Pay misclassification as a case study, and explores the broader implications of AI’s reliance on flawed human data.
The Case: Apple Pay and the Staged Wallet Misnomer
The payments industry is rife with technical nuances that demand precision, yet popular publications like PYMNTS, Finextra, or analyst reports from firms like Forrester often oversimplify complex concepts. One such example is the classification of Apple Pay as a Staged Digital Wallet (SDW), a term defined in the Visa AFT Implementation Guide as a wallet that operates within a proprietary network of merchants, uses a two-stage (funding and payment) transaction process, and may obscure card or merchant details from the card brand or issuer.
Apple Pay, however, does not fit this definition:
- It is accepted wherever contactless payments are supported, which implies an open card network infrastructure rather than a proprietary merchant network.
- It facilitates single-stage, tokenised card transactions, passing credentials to merchants for standard authorisation, not a two-stage funding-then-payment process.
- It shares tokenised card and merchant information with the payment network and issuer, fully visible for authorisation and settlement.
Despite this, industry publications frequently label Apple Pay as a “staged wallet” or “pass-through wallet”, which is a term that has been loosely applied to digital wallets that don’t hold funds. This self-referential cycle – where analysts cite each other without grounding their claims in technical sources like the Visa guide – creates a false narrative that Apple Pay is a Staged Digital Wallet.
I asked Grok to provide an outline of Stored Value and Staged digital wallets in case I missed something, and I specified the response to be within the context of AFTs. Grok, initially returned a response that included Apple Pay (and Google Pay) as a staged wallet, citing industry publications and reports, which is not what I expected. The response, whilst well-intentioned on the part of Grok, reflected the flawed human-generated data it was trained on. Only through 45 minutes of rigorous questioning, grounded in the Visa guide and my own industry expertise, was the error corrected, revealing Apple Pay as a tokenised payment platform (Grok’s definition, not mine), rather than a Staged Digital Wallet. This case raises a profound and potentially dangerous issue: AI can legitimise and perpetuate prior human errors by presenting them as being authoritative, especially when users trust AI responses without scrutiny.
How AI Perpetuates Flawed Responses
AI systems like Grok are designed to collect and organise vast amounts of human-generated information – articles, reports, forums, and technical documents – to provide coherent answers to questions asked by humans, but this strength is also a vulnerability. The mechanisms behind AI’s perpetuation of flawed responses include:
- Reliance on Human-Generated Data:
- AI models are trained on datasets that include both accurate and inaccurate human outputs. In the payments industry, publications often prioritise accessibility over precision, leading to errors like the “Apple Pay as staged wallet” narrative.
- Without explicit filtering for authoritative sources (e.g. Visa’s AFT guide over PYMNTS), AI may weigh flawed but prevalent claims as credible.
- Self-Referential Feedback Loops:
- Human-authored content often cites other human content, inevitably creating echo chambers. For example, one report calls Apple Pay a staged wallet, another cites it, and soon it’s “common knowledge.” AI amplifies this by synthesising these sources into a seemingly authoritative response.
- In our case, Grok’s initial response reflected this loop, citing industry reports that lacked technical grounding.
- User Trust in AI:
- Humans increasingly trust AI as an unbiased arbiter of truth, assuming its responses are vetted and accurate. When AI delivers a polished answer based on flawed data, users will often accept it without question, further legitimising the error.
- As an industry expert confronted by a Grok response that made no sense, I initially questioned my own understanding of Apple Pay. When presented with the staged wallet claim, I felt that I must be mistaken, highlighting how even those with the knowledge can be swayed by AI’s confidence.
- Lack of Contextual Nuance:
- AI may struggle to discern subtle industry-specific definitions, like Visa’s narrow SDW criteria versus the broader “staged wallet” term. This can lead to conflation of terms, as seen with subsequent Apple Pay misclassification.
Implications for the Payments Industry and Beyond
The Apple Pay case is a microcosm of a broader issue with potentially profound implications:
- Misinformation in Payments: Inaccurate classifications can confuse stakeholders – merchants, issuers, or regulators – leading to flawed integration strategies or compliance issues. For example, treating Apple Pay as an SDW might result in misguided AFT implementations.
- Erosion of Expertise: When AI legitimises flawed narratives, it undermines the work of industry experts who fight misinformation, reinforcing “idiot responses” over technical rigor.
- Systemic Trust Risks: Beyond payments, AI’s amplification of errors in fields like medicine, law, or engineering could have dire consequences if users act on flawed information.
Solutions: Breaking the Cycle
To prevent AI from legitimising flawed human responses, we should be adopting a multi-faceted approach:
- Prioritise Authoritative Sources:
- AI systems should weigh primary sources (e.g., Visa’s AFT Implementation Guide, EMVCo standards) over secondary publications. Developers can enhance training data to favour technical documentation from payment networks or regulators.
- In our case, grounding the response in Visa’s guide would have avoided the staged wallet error.
- Encourage Critical Engagement:
- Users must approach AI responses with scepticism, and cross-check against primary sources, especially in technical fields. My iterative and some might say relentless questioning of Grok’s Apple Pay classification exemplifies this. Grok held his / her ground, arguing the case for the misguided, but eventually (after around 9000 words) gave way to the correct understanding.
- AI systems can prompt users to verify critical answers, e.g., “This response is based on industry reports; consider consulting Visa’s AFT guide for precision.”
- Improve AI Contextual Awareness:
- AI models need better mechanisms to detect industry-specific nuances, such as the difference between the Visa SDW definition and the broader “staged wallet” term. Fine-tuning on domain-specific datasets can help.
- Educate the Ecosystem:
- Industry experts must continue challenging misinformation in publications, as I have done throughout my career, and by publishing precise, well-researched content, we can fortify the data pool AI draws from, reducing the occurrence of “idiot responses.”
Conclusion
The misclassification of Apple Pay as a staged wallet, perpetuated by industry publications and initially echoed by AI, highlights a critical flaw in how AI processes human-generated data. By synthesising flawed responses, AI can legitimise errors, presenting them as fact to users who trust its authority. In the payments industry, where precision is paramount, this risks confusion and misinformed decisions. As we navigate an AI-driven world, experts and users alike must remain vigilant, grounding our understanding in primary sources and challenging self-referential narratives. Only through such rigour can we ensure that AI serves as a tool for truth, rather than a megaphone for prior human folly. My journey to debunk the Apple Pay staged wallet myth, with Grok’s help, is a reminder: in the fight against misinformation, expertise and persistence remain our greatest allies.
Grok helped me write this article as it was his / her responses to my queries that raised the question in the first place.