A Conversation with Bright Data’s VP of Ethics and Compliance: Pioneering Ethical Practices, Regulatory Frameworks, and AI Training in the Industry

As VP of Compliance and Ethics, Rony Shalit is responsible for growing and shaping Bright Data’s ethical approach regarding web data collection and proxy usage for years to come which also sets the standard for the industry. Before Bright Data, he spent ten years at KPMG ensuring the highest compliance standards, ethics, risk management and increased corporate governance. Find out more in this exclusive interview with TechBullion.

What principles guide Bright Data in ensuring ethical data collection and usage?

Bright Data puts ethics, transparency and compliance first. We have developed a rigorous set of ethical guidelines that have set the industry bar. However, we also believe setting our guidelines as a member of the industry isn’t enough, so we are heavily involved in forming policies that will protect the web and its users.

How does Bright Data collaborate with regulatory bodies to find that balance of innovation and ethics?

Bright Data is actively involved in government body meetings and industry groups that discuss and develop the framework for AI regulation and the data collection that is essential to its development. Bright Data is committed to developing clear rules that will be enforced by agencies that create that balance of feeding innovation and protecting the internet, websites and users.

For Bright Data, how crucial are transparency and consent in building and sustaining trust with users regarding public web data for AI?

User’s trust is crucial. This is why we only support the collection of publicly available web data. Public web data is any information that a person or organization chooses to share publicly. If Bright Data collects someone’s (publicly available) information they would be notified the information was collected, given an option to opt-out and it would be erased from datasets.

As with any AI model, users must understand how it was built. The machine can only be as good as the data it was trained on.

“The machine can only be as good as the data it was trained on.” What constitutes good data?

There is a risk of bias in AI, as we’ve seen many examples in the media that can have devastating effects on entire groups of people. Bias is learned from humans, so it’s important machines are trained on a variety of datasets and not a single source, so that bias is not duplicated by the machine. Public web data from as many sources as possible allows for a better, more balanced point of view and historical knowledge.

What other industries does Bright Data serve?

Bright Data has more than 20,000 customers, globally in every industry. For consumers, public web data provides market competition, whether that means competitive pricing or knowing where an item is in stock. For businesses, it can provide a competitive edge, providing answers to questions that can only be found through examining external data about such things as climate, transportation routes or hotspots for specific activity.

Bright Data also operates a pro-bono program, Bright Initiative, that supports organizations with a do-good mission. Our program has resulted in life-saving research conducted with public web data on such topics as violence and human trafficking.

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