The Privacy Paradox: Synthetic data's role in the AI revolution

There has been a noticeable surge in attention to using copyrighted content for training generative AI models, particularly this year. This heightened interest has resulted in some legal disputes, and it appears that this year, AI firms could be embroiled in legal battles with content owners.

More so, governmental legislative processes, such as the EU AI Act, are poised to complicate real-world data collection further. There is no one-size-fits-all solution to navigating this ethical terrain, and businesses will likely approach it differently depending on their specific circumstances.

This underscores the need for governments and legislators to provide clear guidance in the years ahead. Despite the inherent complexities, the emergence of synthetic data offers a potential pathway to resolving numerous issues associated with real-world data collection.

The Privacy Paradox refers to individuals who express concern about their privacy but behave in ways that do not protect it. This paradox also extends to the development and use of technology, where there's a constant tension between leveraging personal data for innovation and advancements in AI and the imperative to protect individual privacy rights.

How synthetic data addresses privacy concerns

On the other hand, such legal struggles have been at the heart of major obstacles to making real-world data available for use in training AI models. If for no other reason, we will see a sharp rise in interest in training data and synthetic images in the coming year. This would mean that instead of relying only on real-world data, synthesizing data allows for a cheap and quick process of meeting the necessity to conform to privacy.

This becomes necessary in ID verification tasks, especially AI models since engineers can review the data to correct any incorrect information. This is important for the industry, which relies heavily on ChatGPT and other generative AI technologies.

But what's the mechanism behind it?

Synthetic data sounds exactly like what it is: data that is artificially generated by a computer system to reflect statistical and structural likeness to real-world data.

This is done without reference to concrete data reflecting real-world patterns, structures, or surroundings, using the power of statistical modeling and algorithms.

But it also eliminates developers' need to disclose or submit Personally Identifiable Information (PII). Hence, they may produce large-scale, diverse, and specific data, including the kind that would be expensive or impossible to collect in real life.

Spotting the distinctions

The other focal point is the legitimacy of synthetic data, convincing businesses and governments of the potential abundance they briefly describe. The main job here is to persuade the interested parties that this is a new way of doing things—not always getting stuck to the old way. For example, organizations will not know the newer capabilities in synthetic data if they are all used to knowing the old versions.

The realism of synthetic data has really ratcheted up over the past months, showing how far this field has come. If their improvements to synthetic image detail hold up this year or next, some synthetic data could already be indistinguishable from real-world photographs.

Some usage instances may, therefore, not position them to be visually distinguished as genuine or synthetic from others; for instance, 2D faces are used to verify the rightful owner of a person's identity.

Techniques used by the underlying software include hardware-accelerated ray tracing techniques, which have been more broadly adopted and have increased massively in processing capability. Individual items have good visual fidelity, but the overall scene complexity takes several years to achieve since many synthetic visualizations are required for the actual implementation.

Now, computing power has reached a level where even tasks that require photorealistic animation or predictions of human behavior are feasible.

Forming digital and physical realities

Real-world data is frequently biased. Much of this training data comes from the internet and is, therefore, reflective of social prejudices and possibly also of socio-economic categories represented on the social media platform collecting it. Data scientists have resorted to artificial intelligence and digital humans to counteract these biases.

This will allow data scientists to build more representative and diverse datasets by combining them with real-world data. Of course, one would not dare use images and videos with real people in them, considering PII exposure and the exploitation of the image rights of the people in the media.

There was a construction company aspiring to make autonomous construction vehicles. The company's goal was to increase the safety of these cars and gather more data so they could be trained. These models were all trained to detect people of different sizes, shapes, genders, ethnicities, and attire that would appear within the scene. The experiment trains these vehicles with the synthetic data that, in turn, trains for the detection of the different persons appearing in the scene, also varying in size, shape, gender, ethnicity, and attire.

If someone obstructs the vehicles' path, they could stop moving. However, the organization was able to test a substantially larger set of scenarios than it could have possibly done using conventional data, as there are infinite permutations of lighting, weather, objects, people's motion, and others.

Such breakthroughs are possible thanks to products like İnnovAI-BigData. This comprehensive product elevates the art of Big Data analytics, seamlessly processing vast arrays of structured or unstructured data in real time. By analyzing this extensive data, organizations can gain valuable insights, enabling them to make informed decisions, uncover new revenue possibilities, lower their expenses, and innovate excitingly.

Authenticating identity

Identity document recognition systems are also trained using synthetic data. However, most of these systems require large quantities of precise and varied training data to be successful, which is very difficult to obtain in the current climate.

Training the model with synthetic data capturing plenty of scenarios would enable developers to develop a robust model, which works best under diverse visual conditions involving lighting changes and image distortions.

The developer can easily create such papers using synthetic data without risking personal information. This protects data privacy and allows the developer to create a wide range of international identity documents, including passports and driving licenses. This implies that the system can correctly recognize any region's documents once it has been trained.

Building AI on the bedrock of synthetic data

It should be emphasized that humans produce and verify synthetic data. Data scientists might verify that the data satisfactorily represents reality, transform datasets to help reduce bias and increase accuracy, and even evaluate possible privacy risks when necessary.

Real-world data will still be required to train AI models and create synthetic data, though synthetic data may significantly reduce reliance on real-world data and eliminate any risk of PII and copyright violation compared with using only real-world data. Synthetic data can be more accurate than it would represent reality if only real-world data were used.

The industry is grappling with ways to construct products complying with privacy laws to survive in AI. Synthetic data provides a way through.