How Crowdtesters Reveal AI Chatbot Blind Spots

AI-powered chatbots are the talk of the town. They have quickly become the norm across industries — from financial services and healthcare to retail and beyond. In my work supporting clients who are integrating AI into customer-facing and high-stakes environments, I’ve consistently seen one difficult truth: Some of the most dangerous failures aren’t technical bugs — they’re blind spots.

Blind spots occur when AI models fail to understand the real-world nuances of the domains in which they operate. They might misinterpret user intent, offer misguided advice or miss regulatory details. What makes these AI chatbot blind spots so dangerous is that organizations often don’t know they exist until the damage is done.

And consumers will point the blame squarely at your brand. A YouGov survey revealed 71% of respondents globally believe companies are responsible for the mistakes their AI chatbots make. At best, it’s a black eye on your brand. At worst, it’s a regulatory liability.

AI chatbots don’t know what they don’t know

General-purpose AI models are persuasive and powerful. But out-of-the-box AI solutions simply aren’t trained to handle the depth and complexity required for specialized domains. Organizations might assume AI can handle common customer interactions, but the reality is that these models often lack:

• the ability to apply domain-specific knowledge
• awareness of edge cases or unusual scenarios
• sensitivity to cultural, ethical or regulatory contexts.

In our AI Training and Testing programs at Applause, we rely on two essential groups to uncover these blind spots:

1. Real-world testers – the voice of the customer

Crowdtesters mimic real-world customers. They represent the diversity of a brand’s user base —demographics, languages, cultures, accessibility needs and more. They can help document issues that might elude internal testing.

Real-world testers’ contributions go far beyond surface-level feedback. They often identify confusing or frustrating user flows, instances where the chatbot fails to show empathy and responses that don’t effectively resolve user issues. Thinking broader in connecting with customers, crowdtesters can help reveal problems such as poor accessibility, inadequate localization and cultural insensitivity. Additionally, these real-world testers can flag AI chatbot language that isn’t conversational or naturalistic, incomplete responses that leave users without next steps and ambiguous answers that create more confusion than clarity.

Ebook

2. Domain experts – the subject matter specialists

Domain experts bring specialized knowledge to assess AI systems against high-stakes, industry-specific scenarios. In my experience, this is where the most critical issues often emerge.

These experts identify blind spots that can be difficult or even impossible for generalist testers to detect. They spot misinterpretations of financial, legal or medical guidance that could expose organizations to regulatory risk — or the user to harm. They can reveal security vulnerabilities, such as when AI chatbots inadvertently share sensitive information, or provide unsafe recommendations. Experts are also critical for addressing ethical concerns, such as uncovering domain-specific instances of AI bias, including in chatbots for hiring, financial services or healthcare triage systems.

Domain experts can also examine the chatbot’s ability to meet evolving regulatory requirements and industry-specific safety standards. As you can imagine, sensitive domains like pharmaceuticals and chemicals introduce the possibility for direct harm to the user. These experts go beyond surface-level validation and provide deep context evaluation, analyzing not just whether a response is plausible, but whether it is responsible, compliant and appropriate for the end user.

These aren’t just usability problems — they’re business, legal, ethical and safety concerns that require specialized knowledge to uncover and mitigate.

Blind spots by industry

In our work, we support clients across some of the most critical and highly-regulated sectors. AI chatbots are becoming essential tools in those spaces, but the risks are as high as the reward — ranging from financial loss and regulatory violations to compromised patient safety and brand reputation.

Financial services

Our financial services clients frequently face challenges with chatbots designed for banking, retirement planning or wealth management. Expert testers help identify numerous blind spots, some of which might include:

• incomplete or inaccurate financial guidance
• misinterpretation of tax regulations
• missing contextual questions that a human advisor would naturally ask

For example, experts identified that one client’s chatbot failed to consider special tax rules applicable to high-income earners, which could have led clients to make financially harmful decisions.

Consider another client example, an AI chatbot designed to offer retirement planning advice. Retirement planning spans decades and is full of nuances that can change over time. An AI chatbot might be able to cite a textbook or two of helpful insight, but doesn’t come close to fully covering the subject matter. Involving additional domain experts can help validate and improve these models — for example, a financial services professional may offer valuable insight into how the output is interpreted by the end user.

Healthcare

First, do no harm. Healthcare clients turn to us to help balance AI utility with safety and compliance. Health tech and healthcare companies should work directly with experts to define clear guardrails — what a chatbot should and shouldn’t say in assessing patient wellbeing. Making incorrect diagnoses, implied or otherwise, can lead to poor health outcomes and liability risk.

Additionally, crowdtesters can help flag phrasing that might alienate or confuse patients. Consider patients from marginalized communities or with limited health literacy — these patients could be adversely affected by inaccurate or unhelpful interactions.