“Data Trusts” Could Be the Key to Better AI
One of the challenges in developing AI applications is obtaining the vast amount of data that’s required. Making matters worse, regulations and privacy issues pose obstacles to firms’ sharing their data. A possible solution is for firms to form a “data trust.” that serves as a fiduciary for the data providers and governs their data’s proper use. Willis Towers Watson recently piloted a data trust together with several of its clients. This article shared what they learned about how to create such a trust.
One of the greatest barriers to adopting and scaling AI applications is the scarcity of varied, high-quality raw data. To overcome it, firms need to share their data. But the many regulatory restrictions and ethical issues surrounding data privacy pose a major obstacle to doing this. A novel solution that my firm is piloting that could solve this problem is a data trust: an independent organization that serves as a fiduciary for the data providers and governs their data’s proper use.
Research shows that companies are becoming increasingly aware of the value of sharing data and are exploring ways to do so with other players in their industry or across industries. Typical use cases for data sharing are fraud detection in financial services, getting greater speed and visibility across supply chains, improving product development and customer experience, and combining genetics, insurance data, and patient data to develop new digital health solutions and insights. Indeed, the research has shown that 66% of companies across all industries are willing to share data. Nevertheless, sharing sensitive company data, particularly personal customer data, is subject to strict regulatory oversight and prone to significant financial and reputational risks.
A data trust that is set up as a fiduciary for the data providers could make it much easier for firms to safely share data by instituting a new way for governing the collection, processing, access, and utilization of the data. That legal and governance setup obliges the data trust administrators (the “fiduciaries”) to represent and prioritize the rights and benefits of the data providers when negotiating and contracting access to their data for use by data consumers, such as other private companies and organizations.
Data trusts also can encourage data interoperability as well as the ethical and compliant governance of data — for example, by ensuring that individuals have consented to the various uses of their data (as required by regulation in several jurisdictions around the world), removing data bias, and de-identifying personal data. Moreover, by adopting a new cohort of cutting-edge technologies such as federated machine learning, homomorphic encryption, and distributed ledger technology, a data trust can guarantee transparency in data sharing as well as auditing of who is using the data at any time and for what purpose (i.e. tracking chain of custody for data), thus removing the considerable legal and technological friction that currently exists in data sharing.
Data consumers who sign contracts with the trust to gain access to its data can then focus on the utility that can be derived from analyzing the data or using it to train AI algorithms without undertaking the compliance and reputational risk.
