Artificial Data Inflicting Real Damage
In the realm of artificial intelligence (AI), synthetic data is gaining traction as a potential solution to data scarcity and privacy concerns. This new form of data, generated by mathematical models and algorithms, promises to preserve statistical properties without using personal information, thus alleviating regulatory compliance worries.
However, the use of synthetic data is not without its challenges. Proponents assume that its quality can be validated without extensive real-world testing, but to ensure its effectiveness, comparisons with real data are necessary. Yet, synthetic data creates a 'simulation-to-reality gap', as datasets may behave differently from the real world.
One of the significant concerns is the potential for bias and incompleteness in synthetic datasets. AI systems trained on such data could be inaccurate and unfair, systematically disadvantaging certain groups and encoding and amplifying existing inequalities in ever more sophisticated ways. To combat this, developers are tasked with creating fair and representative synthetic datasets, a challenge that demands specialized approaches to oversight and quality control.
The power to create new 'data realities' through synthetic data is a double-edged sword. Policymakers must ensure that laws apply not only to the use of data but also to the algorithmic construction of reality. Effective governance of synthetic data requires a focus on ethical training, involving ethicists, domain experts, synthetic data generation specialists, and privacy-preserving technique specialists.
Public engagement is crucial to understand how communities are represented in synthetic datasets, including the algorithmic choices about what constitutes a 'fair' and 'accurate' representation of their experiences. In domains like healthcare and finance, where data scarcity remains a fundamental bottleneck, synthetic data appears to offer a solution by augmenting sparse datasets with artificially generated examples.
Privacy-preserving machine learning approaches, such as differential privacy and federated learning, improve security but often at the cost of model performance or development. Quality assurance of synthetic data is another troubling gap, often relying on informal 'spot-checking' or 'eyeballing' instead of systematic evaluation.
Independent auditing frameworks will need to focus on the data generation algorithms themselves, require independent real-world testing, and use adversarial techniques to uncover hidden biases or privacy vulnerabilities.
Despite these challenges, synthetic data is already being employed by tech giants like Apple, Microsoft, Google, Meta, OpenAI, and IBM. The Berlin-based startup GreenMatterAI, in collaboration with DXC, is using synthetic data for training AI models, particularly in automated welding seam inspection in manufacturing, improving efficiency and reducing manual data labeling.
As we navigate the world of synthetic data, it's essential to remember that technical questions about data quality quickly become about justice, fairness, and human rights. It is crucial to ask what problem synthetic data is intended to solve, and to ensure that it serves broader social interests, not just the interests of the tech industry.
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