WHAT 'TRAINING DATA' MEANS: AI EXPLAINED FOR OLDER ADULTS

Introduction

You already know from Why AI sounds intelligent but isn't that AI learns from enormous datasets of text scraped from books, websites, and other sources, but what we haven't talked about yet is that this training data isn't neutral, accurate, or complete - and those problems get baked directly into the AI.

Understanding what's wrong with training data is key to understanding why AI sometimes gets things badly wrong, and why some of those problems can't be fixed.

When training data is biased

If the training data contains biased material, the AI will learn those biases because instead of distinguishing between accurate representation and prejudice, it just learns patterns. For example, if most of the text about engineers uses male pronouns, the AI might default to assuming engineers are male, and if the training data includes racist or sexist content (and the internet certainly does), the AI will learn those patterns too.

Companies try to filter out the worst of this during training and fine-tune models afterward to reduce harmful outputs, but you can't eliminate bias entirely because bias is woven into language itself. The AI learns from human writing, and human writing reflects human prejudices, assumptions, and blind spots.

This is as much about obvious prejudices as it is about perpectives that are missed entirely. If the training data is predominantly in English and reflects Western viewpoints, the AI will have learned those patterns far better than others, meaning it might struggle with or misrepresent non-Western contexts, languages, or cultural references.

When training data is outdated

If the training data is old, the AI's knowledge is old, and this is a particular problem for fast-moving fields like technology, medicine, or politics. An AI trained on data from 2022 won't know about events, discoveries, or changes that happened in 2023 or later, which means it might confidently give you outdated advice because that advice was correct at the time its training data was collected.

Some AI systems get around this by being given access to live search results. So that when you ask them a question, they search the web and use those live results to generate an answer, but that's a workaround, not a solution. The underlying AI still doesn't "know" anything beyond its training data; it's just looking things up for you.

When training data contains errors

The internet is full of mistakes - badly written Wikipedia edits, incorrect blog posts, forum threads where someone confidently states something wrong - and all of that ends up in the training data. The AI can't distinguish accurate information from nonsense because it doesn't understand what accuracy is; it just learns patterns.

So if a wrong answer appears often enough in the training data, the AI might reproduce it, and if multiple sources repeat the same error (which happens more often than you'd think), the AI will learn that error as if it were fact. This is particularly problematic when incorrect information sounds plausible or is repeated by sources that look authoritative.

The copyright problem

There's a legal and ethical issue here worth mentioning and that is a lot of the training data was used without permission, with books, articles, and other copyrighted material scraped and fed into AI training systems without asking the authors or publishers.

Some people argue this is fair use because the AI is learning patterns from it instead of copying the material directly, while others argue it's theft because the AI wouldn't exist without that material and the creators weren't compensated. This is still being fought out in courts, and I'm not going to pretend to know how it'll be resolved, but it's part of the picture.

The data that powers AI wasn't all freely given, and many of the people whose work was used to train these systems never consented to it and will never see any benefit from it.

Why this matters

Training data determines what biases the AI has, what mistakes it makes, and what gaps exist in its knowledge, which means that when you use AI, you're not getting objective truth but in reality you're getting a statistical reflection of whatever text it was trained on.

If you're evaluating what AI tells you, it's worth asking: what was this trained on, how recent is the data, what perspectives are missing, and what errors might have been learned? You won't always get clear answers (companies are secretive about this), but the questions are important.

And remember: AI doesn't give you facts; it gives you patterns learned from imperfect, biased, error-filled human writing. Sometimes that's useful, sometimes it's misleading, and I'm afraid it's up to you to know the difference.

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