In 2018, Amazon’s engineering team had a problem. Their new AI-powered recruiting tool, designed to find the best software developers, was systematically penalizing female candidates. Resumes that included the word “women’s,” as in “women’s chess club captain,” were ranked lower than their male counterparts. The team was baffled. They hadn’t programmed any gender bias into the system.
They didn’t have to. The data did it for them.
The AI had been trained on ten years of resumes submitted to Amazon. Because the tech industry has historically been male-dominated, most of those resumes came from men. The AI learned a simple, toxic pattern: successful candidates in the past were overwhelmingly male. It concluded that being male was a predictor of success and that female-associated words were a predictor of failure.
Amazon scrapped the project, but the lesson was clear. The AI wasn’t malicious. It was just a mirror, reflecting the biases present in its training data. This is the iron law of artificial intelligence: Garbage In, Garbage Out (GIGO).
The Crisis of Bad Data
Amazon’s hiring AI is not an isolated incident. The history of AI implementation is littered with disasters caused by poor data quality.
A 2019 study found that a widely used healthcare AI was systematically underserving Black patients. The AI was trained on insurance claims data to predict which patients needed extra care. But due to systemic inequalities in the healthcare system, Black patients historically had lower healthcare spending. The AI learned that lower spending meant a patient was healthier. It concluded that Black patients were less sick than equally ill white patients, and therefore needed less care. Millions of patients were affected.
Facial recognition systems have been shown to have error rates up to 35% for dark-skinned women, compared to less than 1% for white men. The reason? The systems were trained on datasets that were overwhelmingly white and male. The result has been wrongful arrests and accusations based on faulty AI identification.
In every case, the root cause is the same: the AI system was built on a foundation of bad data. Biased data, incomplete data, inaccurate data—it all leads to the same outcome: an AI that is biased, unreliable, and in some cases, dangerous.
What is “Data Quality”?
Data quality isn’t just about having a lot of data. It’s about having the right data, and ensuring that data is:
Accurate: The data must correctly reflect the real world. A dataset of product prices is useless if the prices are out of date.
Complete: The data must not have missing values. An AI trained on customer profiles with missing demographic information might draw incorrect conclusions about those groups.
Representative: The data must accurately reflect the diversity of the population it will be used on. If you’re building a facial recognition system, your training data needs to include faces from all races, genders, and age groups.
Unbiased: The data must not contain historical or systemic biases that you don’t want the AI to learn. This is the hardest and most important part of data quality. It requires a deep understanding of where your data comes from and what societal biases it might reflect.
Fresh: For many applications, data must be up-to-date. An AI that gives stock recommendations based on last year’s financial data is worse than useless.
The Foundation of Responsible AI
There is no responsible AI without high-quality data. You can have the most sophisticated model, the most ethical guidelines, the most rigorous testing—if the data you train it on is garbage, the results will be garbage.
This is why data governance has become one of the most critical functions in any organization that uses AI. Data governance is the process of managing the availability, usability, integrity, and security of data. It’s about asking hard questions before you ever start building a model:
Where does this data come from? Who collected it, and for what purpose? What biases might be present in the collection process? Is the dataset representative of the population we want to serve? How will we ensure the data stays accurate and up-to-date?
These are not just technical questions. They are ethical questions. And they are business questions. The cost of getting them wrong is not just a poorly performing AI. It’s lawsuits, reputational damage, and real harm to real people.
The Role of Data APIs
For many AI applications, especially those that rely on information from the outside world, data quality starts with the data sources. If you’re building an AI that needs to understand news, financial markets, or public opinion, you can’t just scrape random websites. The data will be messy, unreliable, and full of biases.
This is where high-quality data APIs, like the one from SearchCans, become essential. A good data API doesn’t just provide data. It provides clean, structured, and reliable data. It does the hard work of collecting information from thousands of sources, cleaning it up, structuring it, and making it available in a consistent, machine-readable format.
Using a trusted data API is the first step in building a responsible AI system. It ensures that the foundation of your AI is solid. It doesn’t solve all data quality problems—you still need to ensure the data is representative and used ethically—but it solves the foundational problem of accuracy and completeness.
Building on Solid Ground
The AI revolution is not about building bigger models. It’s about building better data foundations. The companies that succeed in the long run will be the ones that take data quality seriously.
This means investing in data governance. It means creating dedicated teams to source, clean, and manage data. It means being transparent about where your data comes from and what its limitations are. It means prioritizing data quality over model complexity.
Amazon’s hiring AI failed not because the model was bad, but because the data was a reflection of a biased past. A responsible AI system must be built on data that reflects the future we want to create, not the past we want to leave behind.
Garbage In, Garbage Out is not a suggestion. It’s a law. And in the age of AI, it’s the most important law there is.
Resources
Building Responsible AI:
- SearchCans API - Access high-quality, structured web data
- AI Ethics - The challenge of bias
- Human-in-the-Loop - The role of human oversight
Learn More About Data Quality:
- Data Pipeline Efficiency - Building for quality
- Content Extraction Guide - From messy web to clean data
- The New Moat - Why data is defensible
Get Started:
- Free Trial - Evaluate our data quality
- Documentation - API reference
- Pricing - For projects of all sizes
Responsible AI starts with responsible data. The SearchCans API provides the clean, reliable, and structured web data needed to build AI systems you can trust. Build on a solid foundation →