Regulation is an industry, but effective Regulation is an art. There are a number of recognised principles that should be considered when regulating an activity, such as Efficiency, stability and regulatory structure, general principles, and the resolution of conflicts between these various competing principles. With the regulation of artificial intelligence (AI) Technology, a number of factors make the centralised application of these principles difficult to realise – but AI should be considered as a part of any relevant regulatory regime.
Because AI Technology is still developing, it is difficult to discuss the regulation of AI without reference to a specific technology, field or application where these principles can be more readily applied. For example, optical character recognition (OCR) was considered to be AI technology when it was first developed, but today, few would call it AI.
• Predictive technology for marketing and for navigation; • Technology for ridesharing applications; • Commercial flights routing; • And even email spam filters.
These technologies are as different from each other as they are from OCR technology. This demonstrates why the regulation of AI technology (from a centralised regulatory authority or based on a centralised regulatory principle) is unlikely to truly work.
Efficiency-related principles include the promotion of competition between participants by avoiding restrictive practices that impair the provision of new AI-related technologies. This subsequently lowers barriers of entry for such technologies, providing the freedom of choice between AI technologies and creating competitive neutrality between existing AI technologies and new AI technologies (i.e. a level playing field). OCR technology was initially unregulated, at least from a central authority, and it was therefore allowed to develop and become faster and more efficient, even though there are many situations where OCR documents contained a large number of errors.
In a similar manner, a centralised regulation regime that encompasses all uses of AI mentioned above from a central authority or based on a single focus (e.g. avoiding privacy violations) would be inefficient.
The reason for this inefficiency is clear: the function and markets for these technologies are unrelated.
Strict regulations that require all AI applications to evaluate and protect the privacy of users might not only result in the failure to achieve any meaningful goals to protect privacy, but could also render those AI applications commercially unacceptable for reasons that are completely unrelated to privacy. For example, a regulation that requires drivers to be assigned based on privacy concerns could result in substantially longer wait times for riders if the closest drivers have previously picked up the passenger at that location. However, industry-specific regulation to address privacy issues might make sense, depending on the specific technology and specific concern within that industry.
Stability-related principles include providing incentives for the prudent assessment and management of risk, such as minimum standards, the use of regulatory requirements that are based on market values and taking prompt action to accommodate new AI technologies.
Using OCR as an example, if minimum standards for an acceptable number of errors in a document had been implemented, then the result would have been difficult to police, because documents have different levels of quality and some documents would no doubt result in less errors than others.
