Artificial data inflicting genuine damage
In the rapidly evolving world of Artificial Intelligence (AI), synthetic data is gaining traction as a potential solution to data scarcity and privacy concerns. However, its use comes with unique challenges that require careful consideration.
Synthetic data, generated using mathematical models or algorithms, offers a promising approach to addressing data scarcity, fairness, and privacy issues in machine learning. By augmenting sparse datasets with artificially generated examples, it preserves statistical properties and enables unlimited scaling at a low cost. Companies like Apple, Microsoft, Google, Meta, OpenAI, and IBM are already leveraging synthetic data in their AI development.
However, the quality and trustworthiness of synthetic data is a concern for many. Quality assurance practitioners grapple with defining what makes synthetic data useful and trustworthy, often relying on informal 'spot-checking' or 'eyeballing' instead of systematic evaluation. This lack of rigorous evaluation can lead to data pollution, contaminating training pipelines and creating feedback loops with models learning from increasingly artificial representations of reality.
Regulators are also taking notice. They will need to ensure that laws apply to the design choices embedded in data generation systems and that synthetic data serves broader social interests rather than simply enabling more efficient value extraction from limited real-world information. Regulations like the GDPR can make collecting, storing, and processing personal data for AI training difficult and expensive.
To address these challenges, companies are implementing risk management systems including compliance audits, algorithm monitoring, privacy impact assessments, and adherence to legal frameworks like the AI Act. Additionally, ISO/IEC 27001:2022-certified companies maintain trusted and secure data processes through rigorous information security management systems.
However, synthetic data's void of concrete referents concentrates subjectivity and makes it less visible, placing unprecedented power in the hands of developers. Without quality assurance frameworks specifically designed for synthetic data's unique challenges, organizations risk deploying models trained on flawed datasets, undermining both performance and fairness objectives.
Moreover, synthetic data transparency requirements must address the use of synthetic validation data, potential circular validation problems, privacy protection measures, re-identification risks, and provenance tracking. The quest for high fidelity in synthetic data can inadvertently reveal private information, eroding privacy protection as the simulation-to-reality gap narrows.
In conclusion, while synthetic data offers a promising solution to data scarcity and privacy concerns, it demands specialized approaches to oversight and quality control that current AI governance and assurance frameworks aren't fully equipped to handle. Effective governance of synthetic data must address the power to create new 'data realities' and ensure that laws apply not only to the use of data but to the algorithmic construction of reality through synthetic data. Public engagement is necessary to understand how communities are represented in synthetic datasets and to grapple with whether synthetic data can democratize AI development or just replace real-world data relationships with algorithmic intermediaries.
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