Inconsistencies in AI

Understanding Data Errors and Inconsistencies in AI: A Cautionary Tale

Artificial Intelligence (AI) has rapidly become a tool for solving complex problems, providing insights, and generating content. While AI offers significant advantages, it is far from perfect.

Over the past few months, I've been exploring various AI applications. I often have conversations with AI while driving, and my preferred application for this is Copilot due to its user-friendly interface.

During my interactions with Copilot, I've noted three common types of errors: inconsistent data, inconsistent answers, and false data. Let's explore these in more detail.

1. Inconsistent Data: Different Answers for the Same Question

One of the most frustrating experiences for AI users is receiving different answers when asking the same question multiple times. For instance, when I asked about the fertility rate for the US, I was told 1.6 children per woman. The next day, using the exact same prompt, I was told 1.8. This is a form of inconsistent data. A difference of 0.2 may not seem significant, but why would the answer change from one day to the next, especially if the dataset used to generate the response remained the same?

Why does this happen (according to Copilot)?

AI models rely on vast datasets from various sources, which may be outdated, conflicting, or inconsistent. Additionally, the way AI interprets questions or retrieves information can vary slightly due to inherent randomness or uncertainty in how it prioritizes different data sources. If the model is trained on multiple misaligned datasets, this can result in different answers for the same question.

2. Inconsistent Answers: Contradictions Within a Single Response

Another common error involves internally inconsistent answers. For example, when I asked Copilot about the average height of an American male, it stated that the average was 5 feet 7.5 inches (69 inches)—two measurements that should be equivalent but aren't. When I pointed out the inconsistency, it acknowledged the error and responded that 5 feet 9 inches (69 inches) was the correct answer. These inconsistencies can be jarring, especially when they occur in a single response.

Why does this happen (according to Copilot)?

AI models often piece together information from multiple sources, which can lead to contradictory or poorly harmonized outputs. The model might not always have the capability to cross-check every statement it generates, especially when dealing with nuanced or overlapping data. This can result in inconsistencies in how it expresses or interprets facts within the same answer.

3. Made-Up Data: References to Non-Existent Studies or Entities

Perhaps the most concerning error is when AI generates entirely fabricated data. This might include references to non-existent studies, institutions, or businesses. You may have seen this mentioned in news articles. For example, an AI might cite a study from a university that doesn't exist or create a business name that has no real-world counterpart. In my case, I asked about worker-owned co-ops in Southern California, and Copilot told me about one in Los Angeles that was a branch of a company I already knew of in San Francisco. When I checked the company website, Yelp, and Google Maps, there was no mention of it. I asked Copilot to confirm, and it admitted its error, stating that there was no Los Angeles branch of the company. The question is: why would it make something like that up?

Why does this happen (according to Copilot)?

AI models are not inherently fact-checkers; they're designed to predict and generate plausible text based on patterns in their training data. Sometimes, when they lack necessary information, they may generate something that "sounds" correct but is entirely fabricated. This is particularly common when the model attempts to fulfill a request for citations, studies, or statistics that it doesn't have in its data bank.

Common Reasons for AI Errors

Across all these types of errors, there are several common reasons why AI models falter:

• Training Data Limitations: AI relies on the data it has been trained on. If that data is incomplete, outdated, or inaccurate, it will reflect those flaws in its answers.
• Complex Queries: When asked nuanced or specific questions, AI can struggle to synthesize information accurately, leading to contradictory or fabricated answers.
• Overgeneralization: AI tries to predict the "right" answer based on probabilities and past data, sometimes producing answers that are statistically plausible but factually incorrect.
• Lack of Verification Mechanisms: AI models are not inherently capable of verifying the accuracy of the information they provide. They generate responses based on patterns, not factual rigor.

The Cautionary Takeaway for AI Users

AI is a powerful tool, but it's not infallible. The presence of inconsistent data, contradictory answers, and made-up facts underscores the need for vigilance. Users should always review and verify information provided by AI, especially for factual or detailed queries. This means cross-referencing answers with trusted sources and relying on personal knowledge or experience where possible.

There is no substitute for human expertise, critical thinking, and research. While AI can assist with insights and boost productivity, it shouldn't be the sole source of truth. Whether using AI for business, education, or casual inquiries, remember that AI outputs can be unreliable. Use it as a starting point, but always apply your own judgment to ensure accuracy.