Here's How to Scrape the Web with Python—Ethically

ChatGPT is definitely allowed—in the right way

TL;DR: Web scraping remains a powerful tool for collecting real-time, structured data at scale, even in an age dominated by AI tools like ChatGPT. Ethical web scraping is crucial, requiring respect for site policies, responsible use of resources, and compliance with legal frameworks. Python libraries like Scrapy, BeautifulSoup, and Selenium enable scalable and efficient scraping, making it invaluable for applications such as corporate reporting, market research, and sustainability analysis. By combining ethical practices with technical expertise, professionals can harness web scraping to drive smarter decisions and create lasting value.

Harvesting the web for data is hard work—but when it’s done right, it’s also very rewarding. Image generated with Leonardo AI

Data is a centerpiece of modern decision-making, whether you're tracking corporate sustainability reports, analyzing market trends, or building models to inform high-stakes investment decisions.

At Wangari Global, we face this challenge daily as we sift through vast amounts of corporate reporting to power our sustainability insights. Tools like ChatGPT and other LLMs offer impressive capabilities. However, they often rely on pre-existing datasets, and this leaves gaps for those seeking real-time, domain-specific, or structured information. This is where web scraping shines.

That being said, web scraping can be a double-edged sword. When done recklessly, it can strain servers, breach terms of service, and undermine hard-earned trust. But when approached ethically, it enables scalable, precise, and responsible data collection.

In this article, we’ll explore how to scrape the web effectively and ethically using Python, covering everything from technical tips to practical guidelines for ensuring your practices respect the digital ecosystem we all rely on.

Web Scraping Still Matters in the Age of AI

With the rise of AI tools like ChatGPT, some might question whether web scraping has become obsolete. After all, large language models provide instant, conversational answers across a vast range of topics. But while these tools are powerful, they come with significant limitations that make web scraping not just relevant, but essential for many data-driven tasks.

AI models rely on pre-trained datasets, which are often static and can quickly become outdated. They’re excellent for summarizing existing knowledge but struggle to capture the most recent developments, such as changes to corporate reporting or regulatory updates. Furthermore, they are often generalized, making it difficult to extract the granular, domain-specific data that professionals in industries like asset management or sustainability require.

Web scraping allows you to go directly to the source to gather fresh, structured, and context-specific information. Need a company’s latest sustainability disclosure? Web scraping can fetch it from their website. Want to analyze trends in a particular market segment? Scraping lets you aggregate data from multiple sources in real time.

Unlike AI tools, many of which are black boxes of aggregated information, web scraping provides transparency and control over the data you collect.

The Ethical Dimension of Web Scraping