This is part 3 in a series of posts I’m developing into a white paper on “Regulation 2.0” for the Program on Municipal Innovation Harvard Kennedy School of Government. For many tech industry readers of this blog, these ideas may seem obvious, but they are not intended for you! They are meant to help bring a fresh perspective to public policy makers who may not be familiar with the trust and safety systems underpinning today’s social/collaborative web platforms.
Twice a year, a group of regulators and policymakers convenes to discuss their approaches to ensuring trust, safety and security in their large and diverse communities. Topics on the agenda range from financial fraud, to bullying, to free speech, to transportation, to child predation, to healthcare, to the relationship between the community and law enforcement.
Each is experimenting with new ways to address these community issues. As their communities grow (very quickly in some cases), and become more diverse, it’s increasingly important that whatever approaches they implement can both scale to accommodate large volumes and rapid growth, and adapt to new situations. There is a lot of discussion about how data and analytics are used to help guide decisionmaking and policy development. And of course, they are all working within the constraints of relatively tiny staffs and relatively tiny budgets.
As you may have guessed, this group of regulators and policymakers doesn’t represent cities, states or countries. Rather, they represent web and mobile platforms: social networks, e-commerce sites, crowdfunding platforms, education platforms, audio & video platforms, transportation networks, lending, banking and money-transfer platforms, security services, and more. Many of them are managing communities of tens or hundreds of millions of users, and are seeing growth rates upwards of 20% per month. The event is Union Square Ventures’ semiannual “Trust, Safety and Security” summit, where each company’s trust & safety, security and legal officers and teams convene to learn from one another.
In 2010, my colleague Brad Burnham wrote a post suggesting that web platforms are in many ways more like governments than traditional businesses. This is perhaps a controversial idea, but one thing is unequivocally true: like governments, each platform is in the business of developing policies which enable social and economic activity that is vibrant and safe.
The past 15 or so years has been a period of profound and rapid “regulatory” innovation on the internet. In 2000, most people were afraid to use a credit card on the internet, let alone send money to a complete stranger in exchange for some used item. Today, we’re comfortable getting into cars driven by strangers, inviting strangers to spend an evening in our apartments (and vice versa), giving direct financial support to individuals and projects of all kinds, sharing live video of ourselves, taking lessons from unaccredited strangers, etc. In other words, the new economy being built in the internet model is being regulated with a high degree of success.
Of course, that does not mean that everything is perfect and there are no risks. On the contrary, every new situation introduces new risks. And every platform addresses these risks differently, and with varying degrees of success. Indeed, it is precisely the threat of bad outcomes that motivates web platforms to invest so heavily in their “trust and safety” (i.e., regulatory) systems & teams. If they are not ultimately able to make their platforms safe and comfortable places to socialize & transact, the party is over.
As with the startup world in general, the internet approach to regulation is about trying new things, seeing what works and what doesn’t work, and making rapid (and sometimes profound) adjustments. And in fact, that approach: watch what’s happening and then correct for bad behavior, is the central idea.
So: what characterizes these “regulatory” systems? There are a few common characteristics that run through nearly all of them:
Built on information: The foundational characteristic of these “internet regulatory systems” is that they wouldn’t be possible without large volumes of real-time data describing nearly all activity on the platform (when we think about applying this model to the public sector this raises additional concerns, which we’ll discuss later). This characteristic is what enables everything that follows, and is the key distinguishing idea between these new regulatory systems from the “industrial model” regulatory systems of the 20th century.
Trust by default (but verify): Once we have real-time and relatively complete information about platform/community activity, we can radically shift our operating model. We can then, and only then, move from an “up front permission” model, to a “trust but verify” model. Following from this shift are two critical operating models: a) the ability to operate at a very large scale, at low cost, and b) the ability to explicitly promote “innovation” by not prescribing outcomes from the get go.
Busier is better: It’s fascinating to think about systems that work better the busier they are. Subways, for instance, can run higher-frequency service during rush hour due to steady demand, thereby speeding up travel times when things are busiest. Contrast that to streets which perform the worst when they are needed most (rush hour). Internet regulatory systems — and eventually all regulatory systems that are built on software and data — work better the more people use them: they are not only able to scale to handle large volumes, but they learn more the more use they see.
Responsive policy development: Now, given that we have high quality, relatively comprehensive information, we’ve adopted a “trust but verify” model that allows for many actors to begin participating, and we’ve invited as much use as we can, we’re able to approach policy development from a very different perspective. Rather than looking at a situation and debating hypothetical “what-ifs”, we can see very concretely where good and bad activity is happening, and can begin experimenting with policies and procedures to encourage the good activity and limit the bad.
If you are thinking: wow, that’s a pretty different, and powerful but very scary approach, you are right! This model does a lot of things that our 20th century common sense should be wary of. It allows for widespread activity before risk has been fully assessed, and it provides massive amounts of real-time data, and massive amounts of power, to the “regulators” who decide the policies based on this information.
So, would it be possible to apply these ideas to public sector regulation? Can we do it in such a way that actually allows for new innovations to flourish, pushing back against our reflexive urge to de-risk all new activities before allowing them? Can & should the government be trusted with all of that personal data? These are all important questions, and ones that we’ll address in forthcoming sections. Stay tuned.