Why We Invested: Learning Machine
Did you go to school? Did you graduate from university? You probably have a record if you did: a high school diploma, a university degree, your medical license, etc. Today, most of these records are still paper-based, and we typically rely on educational institutions to issue, manage and verify these important credentials. Many prospective employers check these credentials as part of the hiring process.
This system is ripe for innovation for several reasons. For one, individuals today spend time and money presenting paper documents to potential employers or others. The process of sending transcripts often involves physical documents being sealed and sent in paper form, by mail. There is also an ongoing administrative burden on issuing institutions to respond to this need. As a result, individuals and organizations are looking for a private and secure way to verify and share such credentials digitally.
There are other major consequences for individuals who have earned credentials from institutions that cease to exist. Consider universities in war-torn countries, or those hit by natural disasters, where records might be destroyed or no one answers the phone to validate credentials anymore.
The underlying issue is that individuals have little control over these key credentials that are critical components of our identities. At Omidyar Network, we believe an effective digital identity is one that enables privacy, security, and individual control. As such, we are compelled by the idea that individuals should have “self-sovereignty” over our identities–that we should each be able to control our digital identity.
One promising model for self-sovereign digital identity is Learning Machine, a new investment of Omidyar Network’s Digital Identity initiative announced today. Omidyar Network has committed more than $1.2 billion to for-profit companies and nonprofit organizations across five continents that foster economic advancement and encourage individual participation across multiple initiatives, including Digital Identity, Education, Emerging Tech, Financial Inclusion, Governance & Citizen Engagement, and Property Rights. Learning Machine is a company that enables individuals to own their credentials digitally, and share them for verification themselves—thus reducing the dependency on the issuing authority and enhancing privacy.
Working with MIT Media Lab, the founders of Learning Machine developed an open-source standard called Blockcerts, which enables universities and other large enterprise organizations (such as employers tracking workplace learning) to create and record digital credentials that are handed to individuals. The records on the blockchain mean that anyone wishing to verify one of these credentials can now do so with a simple search, without having to return to the issuer. With this standard in the open source domain, credentials can be independent of Learning Machine too—this is not the case with some credential companies that require themselves to be part of the validation chain.
With the underlying standard built, the team created a user-friendly product for issuers to manage the credential issuance on their end. This is the simple beauty of the Learning Machine model: create the new standard and enable issuers to adopt it; empower the credential recipients to use it; and grow adoption and use.
While Learning Machine started in the education space, their product is applicable to any identity credential that is issued. There are many exciting potential uses. Imagine a doctor who fled a war zone who will be able to assert and validate the training and license that enabled her to practice in her home country, even if those institutions are no longer standing. She could continue her life’s work in her new country, because she would have control over the digital records that entitle her to do so.
With new standards of open, digital credentials, we will begin to break down some of the barriers that remain from a legacy system of institutional power and hand that power back to individuals. We are excited to work with Learning Machine in driving this change.