Data has become a valuable asset for organizations across various industries. However, in highly-regulated sectors like healthcare and finance, compliance is not just a legal obligation, but a fundamental aspect of building trust and integrity with customers. As machine learning models require diverse data from multiple sources, the need for a compliant solution becomes crucial. Computational governance has emerged as a potential solution to make data available for machine learning while ensuring governance, security, and privacy.
Computational governance allows data custodians to set privacy levels and access controls on computations. This ensures that only authorized computations that align with the custodian’s requirements are executed on the data, complying with privacy and AI regulations. By monitoring and tracking data usage, companies can comply with regulations like GDPR and HIPAA, protecting the privacy and security of personal data. Additionally, computational governance plays a vital role in developing ethical and responsible AI models, especially in healthcare, where privacy and compliance are critical.
Traditionally, data loses its value when it is moved outside its secure environment. However, federated learning is a secure approach to train AI models without moving the data itself. This allows data custodians to make their data available to developers in a secure environment, protecting proprietary data as a valuable asset. By retaining control over the data and meeting data residency requirements, custodians can derive further value and comply with regulations like GDPR and the EU AI Act.
Despite the potential benefits, many companies are unaware of computational governance as an option to retain data control while allowing algorithms to be sent to data. This leads to organizations keeping their data in silos to address regulatory concerns, hindering innovation and compliance. However, embracing computational governance can enable organizations to securely leverage their data assets, fostering innovation, compliance, and trustworthy AI.
In a changing regulatory landscape, being agile yet compliant is crucial. Computational governance can serve as a catalyst for organizations to securely leverage their data assets, enabling innovative and trustworthy AI. By making data available for machine learning and AI in a compliant manner, companies can differentiate themselves, remain competitive, and contribute to the development of products that benefit society. With computational governance, we can unlock the true potential of data and move towards a future where AI makes a real difference in solving societal problems.
The source of the article is from the blog myshopsguide.com