Computational Governance: Paving the Way for AI Innovation in Highly-Regulated Industries
Companies operating in highly-regulated industries, such as healthcare and finance, are well aware of the importance of compliance. Not only is it a legal requirement, but it also plays a crucial role in building trust and integrity between organizations and their customers. However, as these companies strive to develop advanced artificial intelligence (AI) models, they face the challenge of accessing diverse data from multiple sources, often across different organizations, while ensuring compliance. This is where computational governance comes into play.
Computational governance refers to the ability to control, supervise, and track all aspects of computations performed on data. For organizations sitting on valuable data assets, computational governance offers a pathway to making that data available for machine learning (ML) purposes while ensuring governance, security, and privacy. This emerging solution has the potential to unlock the true value of data for its owners.
By implementing computational governance, data custodians, who are the organizations that own the data, can determine the required level of privacy and define access controls at the computational level. This means they can dictate which computations can be run on their data assets, by whom, and for what purpose. Essentially, only authorized computations that align with the custodian’s requirements can be executed on the data, ensuring compliance with privacy and AI regulations.
The benefits of computational governance are immense. Firstly, it enables organizations to comply with strict regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), which mandate the protection of personal data. Through computational governance, organizations can ensure that only authorized individuals have computational access to the data, that data is only used for approved purposes, and that raw data is never directly shared.
Furthermore, computational governance plays a vital role in the development of ethical and responsible AI models. In the healthcare sector, for instance, it enables AI models to be trained solely on data that meets compliance requirements, ensuring privacy protection.
The ability to leave data in its secure location without ever moving it is where federated learning comes into play. Federated learning allows AI models to be trained on data without the need for it to leave its protected environment. This approach empowers data custodians to make their data available to developers in a secure environment, ensuring that proprietary data remains protected as a valuable asset. By retaining control over their data, custodians can meet data residency and sovereignty requirements, while also deriving commercial or productization value from the data.
Many organizations, however, may not be aware of computational governance methods and may, therefore, choose to keep their data in silos rather than making it available for ML and AI development. This reluctance is often due to concerns about compliance. To truly reap the benefits of innovative AI solutions, a mindset shift is required.
While centralizing data or entering into data-sharing agreements may have facilitated data collaboration to some extent, these methods are often cumbersome, expensive, and not sustainable in the face of rapid regulatory changes and technological advancements.
Organizations operating in highly-regulated industries find themselves at a crossroads where they must choose between prioritizing compliance or innovation. However, in today’s changing regulatory landscape, it is becoming increasingly important to find a balance between agility and compliance. Computational governance offers a way for these organizations to securely leverage their data assets, enabling innovative, compliant, and trustworthy AI.
By securely making their data available for ML and AI purposes, companies can differentiate themselves and stay competitive while contributing to the development of products that benefit society. Improved data quality made possible through computational governance paves the way for AI to make a real difference in various sectors, such as medical research.
To be at the forefront of innovation and push the boundaries of AI responsibly, organizations must productize their customers’ data in a compliant manner. This approach allows them to harness the power of data while maintaining trust and adhering to regulations.
In conclusion, computational governance is emerging as a crucial enabler of AI innovation in highly-regulated industries. By implementing computational governance methods, organizations can securely make their data available for ML and AI development, unlocking the true potential of data while ensuring compliance. With computational governance as a catalyst, data custodians can confidently contribute to real-world solutions to pressing challenges, including advancements in medical research.
