The Regulator of Tomorrow

Technology is advancing at an exponential pace. Government will need to adapt. It’s not just a question of regulating new technologies like artificial intelligence and gene editing to prevent harm. Technology is changing the capabilities of regulators in ways that the founders of regulatory agencies would never have dreamed of when they first armed inspectors with clipboards.

Five major trends could underpin the future of regulation and enforcement: 

1. Risk-based regulation: RBR focuses resources where data anticipates a need. Algorithms flag potential problems and prioritize likely offenders or hot spots. Enforcement budgets get relief and violations are more likely to be spotted.

Regulators have already built RBR with technology like data analytics and artificial intelligence. For example, food inspectors can use data collected about everything from temperature readings to reports of illness to target restaurants most likely to be violating safe food handling practices. Algorithmic red flags could also be used to identify potential tax fraud, money laundering, or exploitative supply chains.

2. Positive enforcement: Positive enforcement is a strategy of not just punishing bad behavior but rewarding progress beyond the bare minimum. Positive enforcement is still in its infancy in most jurisdictions. The most common form rewards a record of good compliance with greater trust. For example, a multi-site business that passes several in-person inspections could then be trusted with virtual inspections in the future. 

3. Regulatory technology: Regulators now have tools to reinvent their own jobs. RegTech touches everything from compliance to policymaking. Autonomous drones with gas sensors and hyperspectral cameras can inspect miles of pipeline. Natural Language Processing and machine learning can sort and compare hundreds of thousands of patent applications. Robotic Process Automation can autofill data for businesses applying for permits. The Internet of Things can use sensors to save exponential amounts of time, like earthquake sensors on buildings in Wellington, New Zealand, that indicate which building experienced the most damage, and which are safe to return to.

4. Rules as code: When computers administer benefits or licenses, programmers can literally encode the rules in code. Some laws naturally end up translated to computer code. Take, for example, an online hunting license order. A well-designed state Fish and Wildlife website will simply make it impossible to purchase conflicting tags. This spares citizens a trip through the fine print. When the law itself is written as code, it spares the fine print for everyone. Similarly, updates to New Zealand’s Rates Rebate Act and Holiday Act are written in if-then statements that a computer can read as code to ensure computer systems distribute funds exactly as required. Judges needn’t argue over the meaning of the law—the intent is clear enough for a computer. Encoding laws for enforcement will become increasingly important when the laws underpin AI or when a proposed law could benefit from A/B testing.

5. Touchless compliance: When drivers cross the Golden Gate Bridge, they don’t have to stop for a tollbooth. Either a machine charges a pre-funded device in their cars, or a camera photographs a license plate and sends a bill to the address where the vehicle is registered. This is an early application of touchless compliance—a strategy for regulation that minimizes the hassle for users. Automated speeding tickets or red-light cameras are not only more fair and effective at cutting traffic accidents, they also reduce the need for human police enforcement, cutting costs and limiting situations that might escalate into a liability. 

Ethical Application of Technology

These advances are not without ethical concerns. Technological changes can revolutionize government’s ability to equally enforce laws and better serve citizens, but they can also create ethical issues. Proper implementation of new technologies cannot become careless box-checking. It should protect privacy rights and avoid the pitfalls of algorithmic bias. For example, machine learning that examines potential illegal financial transactions can turn up evidence of money laundering. (Imagine it in your best digital assistant recommendation voice: “shell companies that registered at this address also do business with these foreign entities.”) Meanwhile, similar scrutiny of contacts and purchases would be unethical when directed at normal law-abiding civilians.

A future court battle could someday revolve around the substantive difference between indiscriminately collecting license plate data and indiscriminately collecting data about international money transfers. For now, regulators should continue to be mindful of citizen privacy, even while identifying corporate misbehavior. 

The five future shifts noted above could revolutionize regulation. As the economy grows ever more complex, remote, and digital, a new realm of regulatory compliance could emerge from the seeds of change that agencies plant now. 

William Daniel Eggers is the executive director of the Deloitte Center for Government Insights. His most recent publication is: “Creating the government of the future: Uncovering the building blocks of change to become more anticipatory, human-centered, and resilient.”

Avijeet Sinha is a principal in Deloitte Consulting LP. He leads the financial regulatory practice for Deloitte’s Government and Public Services industry.