Avoid These 5 Web Data Pitfalls When Developing AI Models

Learn how to avoid common pitfalls in web data collection for AI model development and leverage Bright Data’s solutions for reliable data.
7 min read
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In this article, we briefly discuss the top pitfalls to avoid when collecting web data for AI, and outline how to overcome them.

Data Bias

Data bias occurs when the web data used to train an AI model is not representative of the real-world population or scenarios it is supposed to predict, leading to skewed or unfair outcomes. This can be caused by sampling bias, where certain groups or features are overrepresented or underrepresented; historical bias, which reflects past prejudices or inequities; measurement bias, arising from errors or inconsistencies in data collection from various websites; and confirmation bias, which involves selecting data that supports preconceived notions.

The Solution

To address data bias, collect data from diverse web sources, apply robust preprocessing to correct biases, and use thorough validation to ensure data accuracy. Employ systematic collection methods to avoid reinforcing existing biases.

Example: In 2018, it was discovered that Amazon’s recruitment AI was biased against women. The AI was trained on resumes submitted over a 10-year period, which were predominantly from men. As a result, the model learned to prefer male candidates and downgraded resumes that included the word “women” or were from women’s colleges.

Bright Data’s Premium Proxy Services offer a robust solution by using real-user IPs from any location, ensuring accessibility and coverage. This allows for the collection of diverse data globally, thereby overcoming bias within AI models. By leveraging Premium Proxies, data scientists can source information from a wide range of regions and demographics, significantly reducing the risk of sampling bias.

Insufficient Data Variety

Insufficient data variety means the data does not cover the full range of scenarios, inputs, or variations it might encounter in real-world use. Causes include limited data sources, reliance on homogeneous data, and focus on niche use cases. AI models require diverse data to understand various scenarios and conditions. Homogeneous datasets can limit the model’s ability to generalize and perform well in diverse real-world situations.


Addressing insufficient data variety involves leveraging diverse web data solutions. This includes sourcing data from multiple, varied websites to ensure a wide range of inputs. Implementing robust data preprocessing techniques can enhance the quality and usability of the collected data. Collecting comprehensive metadata ensures context is maintained, while thorough data validation processes help maintain data integrity.

Example: A financial company develops an AI model to determine credit limits for Apple Card applicants. If the training dataset predominantly includes data from a specific demographic or geographic region, the model might fail to accurately predict credit limits for applicants from diverse backgrounds, leading to biased or unfair credit assessments.

Bright Data’s Custom Scraper APIs provide an effective way to tackle the issue of insufficient data variety. These customizable scrapers can scrape and validate fresh data from any website on demand, offering immediate access to highly specific data. By using Custom Scraper APIs, AI models can be continuously updated with diverse data from multiple, varied sources across the internet. This ensures the datasets are comprehensive and cover a wide range of real-world scenarios, enhancing the model’s ability to generalize and perform well in diverse conditions.

Overfitting and Underfitting

Overfitting happens when a model is too complex and learns to fit the training data too closely, failing to generalize to new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data. When information gets inadvertently into the model during development, data leakage occurs, leading to overly optimistic performance estimates. AI models may appear to perform well during cross-validation but fail in real-world applications due to reliance on leaked information.


To address overfitting and underfitting in AI models, leverage diverse web data from multiple sources and regions. This helps create balanced and representative datasets, reducing the risk of overfitting to specific patterns and underfitting by missing out on key variations. Use techniques like cross-validation with diverse web-scraped data to build robust models and ensure rigorous preprocessing to prevent data leakage.

Example: An e-commerce platform uses an AI model to recommend products. If the model is overfitted, it might only suggest niche products that past users have bought but fail to recommend relevant new items to different user groups. Conversely, an underfitted model might recommend generic products that don’t cater to individual preferences.

Bright Data’s Datasets are an ideal solution. These datasets are ready for immediate use. The validated, parsed, and clean data provided in these datasets ensure that AI models are trained with balanced and representative web data. This reduces the risk of overfitting to specific patterns and underfitting by missing key variations. By using validated datasets, data scientists can save time and ensure the reliability and consistency of their models, leading to improved model performance.

Poor Data Quality

Data quality and quantity are critical for training robust models. Insufficient data can lead to overfitting, where the model captures noise rather than underlying patterns, while poor-quality data (e.g., noisy, incomplete, or mislabeled) can degrade model performance.

When AI models are trained on training data that is full of errors, inconsistent, or poorly labeled, their performance can be greatly affected. Poor training data results in unreliable and inaccurate AI models.


Ensure the web data collected for training AI models is thoroughly cleaned and validated. Implement stringent preprocessing techniques to filter out noisy, incomplete, or mislabeled data. Regularly update and cross-verify data from diverse sources to maintain its accuracy and relevance. By focusing on high-quality web data, you can significantly improve the reliability and performance of AI models.

Example: In 2016, Microsoft launched an AI chatbot named Tay on Twitter. Tay was designed to engage in conversation and learn from interactions with users. However, Tay was fed a lot of offensive and inappropriate content by users shortly after its launch. Due to the poor quality of the training data it received from these interactions, Tay began to produce racist, sexist, and inflammatory tweets. Microsoft had to shut down Tay within 24 hours of its launch. This incident demonstrated how poor-quality and unfiltered data can lead to the failure of AI systems.

Bright Data addresses the challenge of poor data quality with its Validated Datasets. These datasets are thoroughly cleaned and validated, providing parsed, clean, and reliable data ready for immediate consumption. By using Validated Datasets, data scientists can save time and avoid the frustration of data cleaning, allowing them to focus on feature engineering and model training. The high-quality and validated data improve the reliability and performance of AI models, ensuring they are trained on accurate and relevant information.

Data Drift

Over time, the real-world data that an AI model encounters may change or drift from the data on which it was trained. Ignoring data drift can render your models less effective or even obsolete. The dynamic nature of real-world environments means that the statistical properties of input data can change over time, a phenomenon known as data drift. Failure to continuously update and retrain models with new data can lead to outdated models.


Regularly monitor for data drift by comparing current input data with historical data. Implement continuous data collection from diverse web sources to capture the latest trends and patterns. Periodically retrain your models with updated data to ensure they remain accurate and relevant in changing environments.

Example: A retail company uses an AI model for inventory management based on pre-pandemic shopping patterns. As consumer behavior shifts post-pandemic, ignoring data drift could result in overstocking or understocking certain products, leading to lost sales and increased costs.

Bright Data’s Proxies and Automated Web Unlocker offer continuous data collection capabilities. This allows for comprehensive web data collection and ensures stable delivery. By regularly updating datasets with current data, data scientists can retrain their models to maintain accuracy and relevance in changing environments. Bright Data’s solutions ensure that AI models are continuously fed with the latest data trends and patterns, mitigating the effects of data drift and maintaining model performance over time.

How Bright Data Can Help

Bright Data equips data and AI teams with a powerful platform to streamline web data collection, ensuring a scalable flow of reliable data, complete with automated parsing, validation, and structuring features.

By avoiding these common data pitfalls and leveraging Bright Data’s robust data solutions, you can develop more effective and accurate AI models.