Regulatory Reform in Ontario: Machine Learning and Regulation

20 Pages Posted: 23 Apr 2018

See all articles by Anthony Niblett

Anthony Niblett

University of Toronto - Faculty of Law; Vector Institute for Artificial Intelligence

Date Written: March 29, 2018

Abstract

Government regulation of individual and business activity is part and parcel of modern society. But many businesses face difficulties in understanding and navigating the legal hurdles, rules, and uncertainty that come with modern regulation. Many governments in Canada have taken steps to reduce this burden by streamlining regulation and cutting unnecessary red tape.

In this Commentary, I explore how regulators can continue this trend toward more efficient and effective regulation: by embracing data analytics and machine-learning tools.

Big data, analytics and machine learning offer new and difficult challenges for regulators who oversee how many businesses make decisions. But regulators can also benefit from effective use of data science. Some of these benefits can be realized almost immediately by using data that the regulators already have.

First, regulators can better predict who should and should not be investigated. A regulator needs to make choices about how to allocate and prioritize scarce resources. With the right data and appropriate data analytics, predictions can be made about where to best place investigation resources.

Second, regulators must make choices over which cases to prosecute. Regulators should not waste resources litigating cases they are likely to lose. Instead, regulators should put resources only toward cases that they are likely to win. Regulators can turn to the data and use machine learning to predict how a court would resolve a particular problem.

Moving further into the future, big data and machine learning will change the way that laws and regulations will be consumed and produced. Lawmakers will have greater ability to provide relevant information before the individual or business acts, rather than waiting to adjudicate after they have acted. Businesses will seek prior authorization for many more regulated actions. Furthermore, the time and cost for regulators to respond to the queries will fall drastically. Instead of relying primarily on vague guidelines, regulators will be able to offer more expedient and personalized responses.

There are enormous benefits to regulators making decisions before individuals and business act. Advance rulings, given before investments are made, provide certain outcomes and reduce the likelihood of wasted investments. There are, of course, a number of potential barriers and issues that may arise. These include: the quality of the data, accountability and due process, the need for transparency, privacy and the reluctance to share data, the benefits of uncertainty, and the stability of social views and goals.

Keywords: Industry Regulation and Competition Policy; Competition; Consumers’ Interests and Protection; Labour Standards and Relations; Local Services and Governments; Regulatory Burden

JEL Classification: K2

Suggested Citation

Niblett, Anthony, Regulatory Reform in Ontario: Machine Learning and Regulation (March 29, 2018). C.D. Howe Institute Commentary 507, Available at SSRN: https://ssrn.com/abstract=3156646 or http://dx.doi.org/10.2139/ssrn.3156646

Anthony Niblett (Contact Author)

University of Toronto - Faculty of Law ( email )

78 and 84 Queen's Park
Toronto, Ontario M5S 2C5
Canada

Vector Institute for Artificial Intelligence ( email )

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