Synthetic data ethics: Navigating privacy, bias, and compliance in AI

We’re surrounded by data everywhere we look, whether it’s recommendations on our favourite streaming platforms or the algorithms that help self-driving cars navigate the road. But here’s the catch: the data we rely on isn't always perfect. Sometimes it's incomplete, biased, or even downright inaccurate. That’s where synthetic data comes into play.

But what exactly is synthetic data, and why is it becoming such a hot topic in conversations about ethics and bias?

What is synthetic data and why do we need it?

In simple terms, synthetic data is artificial data that's been generated by algorithms, rather than gathered from real-world events. It can be a useful alternative to traditional data collection methods.

Compared to real-world data, synthetic data offers a key advantage: privacy protection. Since it doesn't contain real user information, it reduces the risk of data breaches and regulatory violations.

Types of synthetic data

Synthetic data can take many forms, including:

• Image and video generation: Used for facial recognition and object detection models
• Text generation: Helps train chatbots and language models
• Data anonymisation: Creates safe, shareable datasets while preserving patterns
• Time-series data: Simulates trends for financial forecasting or healthcare research

Why is synthetic data becoming more popular in research?

Overcoming data limitations

Many industries face challenges in accessing high-quality, real-world data. Either it's scarce, costly to collect, or comes with restrictions due to privacy laws. Synthetic data provides a workaround, ensuring businesses can conduct research and development without hitting these roadblocks.

Addressing privacy and compliance concerns

Privacy laws like GDPR in Europe, CCPA in the US, and the Australian Privacy Act, all sets out how organisations can collect, use, and store personal information. It’s designed to protect individuals while making sure businesses handle data responsibly. For many organisations, this can make working with real-world data tricky, especially when it involves sensitive details like health or financial information.

This is where synthetic data offers real value. Because it doesn’t contain actual personal details, it avoids the risks tied to handling sensitive information. Businesses can still test models, run research, and develop products without breaching privacy rules.

Enabling greater flexibility

Unlike real-world data, synthetic data can be tailored to specific needs. Businesses can create datasets that cover rare scenarios, edge cases, or underrepresented groups—helping improve AI fairness and accuracy.

Accelerating AI and machine learning development

Training AI models often requires massive datasets. In fields like finance and healthcare, where sensitive data can’t always be shared, synthetic data helps reduce time to market by enabling safe and efficient model training.

How do businesses use synthetic data in their research?

• Generating diverse and comprehensive datasets

Synthetic data helps businesses prepare for situations that are uncommon but important. In the insurance industry, for instance, real-world data may not have enough examples of multi-vehicle accidents during extreme weather. By generating such scenarios, synthetic data allows insurers to improve their risk assessment models. Similarly, in fraud detection, rare but sophisticated fraudulent transactions can be simulated to strengthen security measures, helping businesses anticipate and counter emerging threats before they become critical issues.

• Model training and validation

AI models require vast amounts of data. Synthetic data helps AI models train on diverse and comprehensive datasets. While real-world data is abundant, it often has gaps. It may not cover rare events, be restricted by privacy laws, or be expensive to collect in large volumes. Synthetic data fills these gaps by creating additional examples that improve model accuracy and generalisation.

• Scenario testing

Companies can simulate cyberattacks to test their security systems, create models of rare equipment failures to improve maintenance, generate financial fraud scenarios to strengthen detection systems, or develop emergency response simulations for better crisis management.

Ethical implications of using synthetic data

If the original dataset used to generate synthetic data is biased, the synthetic data can carry those biases forward. This could lead to AI models that disadvantage certain groups or misrepresent their subject. This is why it's important that businesses should take care to generate and use datasets that eliminate biases rather than reinforce them.

Best practices for businesses to ensure ethical use of synthetic data

• Data audits and validation
  Regularly audit synthetic datasets to identify biases and inaccuracies. Ensure that each set of synthetic data generated represents a wide range of demographics and scenarios.
• Collaborating with experts
  Work with data scientists, ethicists, and legal teams to ensure synthetic data aligns with ethical standards.
• Transparency and documentation
  Keep records of how synthetic data is generated and used for research and decision making, ensuring accountability and trustworthiness.
• Ongoing monitoring
  Continuously review how synthetic data impacts AI models and business decisions to improve ethical standards over time.

Final thoughts  

The ability to create synthetic data is definitely a game-changer in the world of AI and machine learning. It offers so much potential, from improving data privacy to reducing bias in critical systems. But like any new technology, it comes with its own set of ethical challenges.

As we move forward, the key will be to handle synthetic data with care—ensuring it's used responsibly, with full awareness of its potential for bias. If we can do that, we just might be able to unlock a future where AI and machine learning systems are not only smarter but also fairer and more just for everyone.