#2 The Need for Collective Intelligence

Beth Simone Noveck in Analyse & Kritik (2018)

Read #1 Introduction: The Need for Smarter Institutions here.

Despite no special training in technology or innovation, public officials are expected to make the policies about when it is safe to deploy innovations like driver- less cars in real world settings. Public managers are expected to be master of all trades and jack of none; that is to say well-versed in an impossibly wide range of issues. It is no wonder that most Americans today have lost confidence in government and that trust is only declining, especially toward national government. The public’s trust in the federal government continues to be at historically low levels. According to Pew Research, only 19% of Americans today say they can trust the government in Washington to do what is right ‘just about always’ (3%) or ‘most of the time’ (16%) (Pew 2015). The same phenomenon is true globally. In 2018, the average level of trust in government among 28 countries surveyed was 43 percent (Edelman 2018).

To take just the issue of new technology regulation (but the same concerns apply whether we are talking about global warming or nuclear security), public officials have to confront variations of the so-called “trolley problem” (Cassani Davis 2015). While it is usually believed that AI systems, such as self-driving cars or robots, will commit fewer errors of the kind that humans make, and that gene manipulation will cure many diseases, there are still countless unanticipated risks and safety problems for which we have not yet developed consensual legal or ethical answers. These making the determinations about when and what is the right time to test these technologies in the wild. What is an acceptable level of risk? How exactly can we weigh different moral goods or different moral evils?

The advances of the 21st century present policy as well as ethical challenges. Although autonomous vehicles, for example might decrease traffic accidents, they also mean fewer parking tickets and parking fines, reduced gas taxes and money potentially diverted away from public transportation infrastructure. Other innovations raise comparable questions about the consequences for public revenue models. All are potentially solvable problems but ones that require grappling with the economic implications of new technologies and data-driven assessment of whether, when and by how much we need to change the basis for the levying of taxes to make up for new shortfalls.

Then there are the related questions of job losses and economic dislocation for individuals. What jobs will be lost? Which are created? What needs to be the policy response to these threats and how, in turn, to ensure that workers are trained and ready to assume the jobs created by the advent of new technologies.

And all of these dilemmas presuppose a clear view as to who should regu- late and how to smooth out conflicts between international, national, state, local and non-governmental organizations. No such clear view exists. Although these technologies — whether Tesla’s driverless cars or Uber’s autonomous vehicle fleets or Starship’s sidewalk-navigating delivery robots or Amazon’s drones — often first get introduced in cities, as we know from scholars like Richard Briffault, cities are being systematically stripped of their regulatory power by states (Briffault 1996). Then there are those such as former New York Mayor Michael Bloomberg or the late political theorist Benjamin Barber who see the only way forward is by having mayors exercise greater power (Barber 2013; Bloomberg/Pope 2017). We need to decide who should decide.

Public officials must navigate a morass of concerns while stewarding the pub- lic interest and safeguarding the taxpayer dollar. Yet if the ignorance of technology on display when congressional leaders interviewed Facebook CEO Mark Zuckerberg in April 2018 after the Cambridge Analytica scandal was any indication, pub- lic officials do not possess the expertise necessary to tackle these questions (Kang 2018).

Thus, to make policy and legislation that will, at once, protect the public while stimulating innovation and creating jobs, demands that more expertise be brought to bear. The same is true for other complex issues from climate change to immigration, where there is either a dearth of good information or so much information and often from biased and ideological or self-interested sources that policymakers have a hard time making sense of it all under constraints. Even the most capable politicians and public servants do not possess all the expertise needed to understand the root causes of problems and then turn available information into coherent and effective policy.

Hayek argued that the challenge policymakers face in making order out of complex and distributed information is doomed to failure. He argued that market pricing mechanisms are the best way to make sense of available knowledge. But what if other collective intelligence mechanisms beyond that of supply and demand market mechanisms could help? What if new technology could unlock new approaches that enable more individuals — not only interest groups — to weigh in both on how to advance stakeholder interest, but also how to solve our collective problems? What if a city council or parliament could get rapid counsel from university professionals, for example, to help improve their understanding of science and technology?

We need what Dan Esty terms ‘green’ rather than ‘red lights’, namely regulations that assist with the growth of new technological tools while ensuring that the public is also protected. These new methods can loosely be described as what NESTA describes as “anticipatory regulation” (Esty 2017; Mulgan 2017). Anticipatory regulation implies that, rather than top-down prescriptions, the fast- paced and ever-changing nature of new technology (and other complex social is- sues) calls for more open dialogue with innovators and entrepreneurs as well as consumers to ensure that regulations are neither overly burdensome nor under- protective. Getting this admixture right, demands regulations that are iterative rather than final with a more data-driven feedback loop to assess what is work- ing and to know where and how to target scarce regulatory resources.

But for law and policymaking to become more flexible, evolutionary and agile, policymakers need to be able to interact with a broader public with more expertise and more diverse values to improve the quality of lawmaking. We need more innovative, creative yet implementable know how to enable our regulations and policies to keep up with the pace of technological change.

This calls for re-imagining the processes by which we make laws and regulations. augmenting our representative and administrative rulemaking processes with more robust, frequent and disinterested advice-getting. This is less a prescription for more deliberation to ensure greater procedural legitimacy by having better inputs into lawmaking processes than a practical demand for more collaborative approaches to problem solving that will yield better outputs, namely policies that achieve their intended aims. Advances in science and technology are set to transform the way we live together, with profound and frightening consequences. Lest we are to become subjugated to technological systems we cannot understand and few of us can control, we need platforms and processes for connecting public officials and institutions to robust sources of collective intelligence (Susskind 2018).

Read the next part- #3 The Maturing Field of Online Citizen Engagement: From Process Norms to Policy Effectiveness