数据信托或成人工智能发展新关键

发布时间: 2021-04-06

       人工智能应用发展的最大障碍之一是缺乏高质量的原始数据。为了克服这个问题，公司往往需要共享他们的数据，但这又引入了围绕数据隐私的诸多监管限制和道德问题，导致共享数据变得极为困难。而数据信托则提供了可能的解决方案：一个独立组织，受托于数据提供商并管理其数据的正确使用。

       研究显示，公司越来越意识到共享数据的价值，并正在探索行业内或跨行业共享数据的可行方案。数据共享的典型案例包括金融服务的欺诈检测、供应链管理与可视性提升、改进产品开发和客户体验，以及结合遗传学、保险数据和患者数据开发新数字健康解决方案。事实上，研究显示，各行各业中有66%的公司愿意分享数据。然而，共享敏感的公司数据，特别是个人客户数据，受到严格的监管监督，并伴随着重大的财务和声誉风险。

       数据信托作为数据提供商的受托人，可以通过制定一种新的方式来管理数据的收集、处理、访问和使用，使公司更便捷安全地共享数据。该体系要求数据信托管理人在协商及制订数据使用规范时，优先考虑数据供应商的权利及利益，即代表数据供应商促成数据共享。

       数据信托还有助于数据交互以及数据的合规管理。例如，通过确保个人同意对其数据的各种用途，消除数据偏见和取消个人数据的识别。此外，通过采用联邦机器学习、同态加密和分布式账本技术等一系列前沿技术，数据信托机构可保证数据共享的透明度，以及审核谁在什么时间使用数据以及用于何种目的（跟踪数据保管链），从而消除了目前数据共享中存在的大量法律和技术摩擦。

       该体系下，与信托基金签订合同以获取数据的数据消费者可以获益于数据分析的结果以及使用数据培训的人工智能算法，而无需承担合规和声誉方面的风险。它们可以自己（即作为直接数据消费者）或通过组成“最小可行联盟”（MVC）——数据提供商和数据消费者共享数据资源和人才，专注于特定的业务案例。

       未来，数据信托要成为一个适合新兴人工智能经济的数据驱动组织依然任重而道远。数据信任是组织间协作的机会，可以加快速度、降低成本和降低风险。他们还可以通过共同开发适销的人工智能应用程序来控制和限制第三方对会员数据的访问，使数据货币化回报更丰厚。随着可穿戴设备、智能家电和5G网络的普及，以及“智能物联网”的广泛应用，数据共享和协作将成为常态，数据信托将是公司迎接新时代新挑战的强大助力。

原文：

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. They can do so either on their own (i.e., as direct data consumers) or — perhaps more powerfully — by forming “minimal viable consortia” (MVC) where data providers and data consumers share data resources and talent to focus on a specific business case.

The way forward. The journey to becoming a data-driven organization fit for the emerging AI economy is long and arduous. Data trusts are an opportunity for collaboration between organizations to make that journey faster, less costly, and less risky. And they can make data-monetization rewards more handsome by co-developing marketable AI applications and giving third parties controlled access to members’ data. Moreover, as we discovered during our pilot, a data trust can also help inspire creativity, cross-functional collaboration, and innovation, and can attract digital talent. As wearables, smart appliances, and 5G networks proliferate and combine into the “Intelligent Internet of Things,” data sharing and collaboration will become the norm. Data trusts can help companies make the leap to this new era.

本文内容转载自：Harvard Business Review  https://hbr.org/

原文作者：George Zarkadakis

原文地址：https://hbr.org/2020/11/data-trusts-could-be-the-key-to-better-ai

作者: George Zarkadakis