Artificial Intelligence and Machine Learning growth regulation

Artificial Intelligence and Machine Learning growth regulation Should artificial intelligence be regulated? Can it be regulated? And if so, what should those regulations look like? These are difficult questions to answer for any technology still in development stages ? more mainstream regulations, like those on the food, pharmaceutical, automobile and airline industries, are typically applied after something bad has happened, or retroactively, and not in anticipation of a technology becoming dangerous. But AI has been evolving so quickly, and the impact of AI technology has the potential to be so great that legislators prefer not to wait and learn from mistakes, but plan ahead and regulate proactively. Lawmakers and regulators need to look at AI not as a homogenous technology, but instead as a set of techniques and methods that will be deployed in specific and increasingly diversified applications. To be sure, there is currently no generally agreed-upon definition of AI. What is important, however, to understand from a technical perspective is that AI is a rich set of sub disciplines, methods, and tools that bring together areas such as speech recognition, computer vision, machine translation, reasoning, attention and memory, robotics and control, etc. These techniques are used in a broad range of applications, spanning areas as diverse as health diagnostics, educational tutoring, autonomous driving, or sentencing in the criminal justice context. The possibilities are truly endless. But as a growing number of increasingly impactful AI technologies make their way out of research labs and turn into industry applications, legal and regulatory systems will be confronted with a multitude of issues of different levels of complexity that need to be addressed. Both lawmakers and regulators as well as other actors will be affected by the pressure that AI-based applications place on the legal system including courts, law enforcement, and lawyers, which highlights the importance of knowledge transfer and education. Given the speed of development, scale, and potential impact of AI development and deployment, lawmakers and regulators will have to prioritize among the issues to be addressed in order to ensure the quality of legal processes and outcomes?—?and to avoid unintended consequences. Different legal and regulatory regimes aimed at governing the same phenomenon are often closely linked to the idea of jurisdiction. In fact, the competition among jurisdictions and their respective regimes has positive effects by serving as a source of learning and potentially a force for a â€Å"race to the top.† However, discrepancies among legal regimes can also create barriers when harnessing the full benefits of the new technology. Examples include not only differences in law across nation states or federal and/or state jurisdictions, but also normative differences among different sectors. These differences might affect the application as well as the development of AI tech itself. As AI applies to the legal system itself, however, the rule of law might have to be re-imagined and the law re-coded in the longer run. The rise of AI leads not only to questions about the ways in which the legal system can or should regulate it in its various manifestations, but also the application of AI-based technologies to law itself. The future relationship between AI and the law is likely to become even more deeply intertwined. Implementations might take different forms, including â€Å"hardwiring† autonomous systems in such ways that they obey the law, or by creating AI oversight programs (â€Å"AI guardians†) to watch over operational ones. At least some of these scenarios might eventually require novel approaches and a reimagination of the role of law in its many formal and procedural aspects in order to translate them into the world of AI, and as such, some of today’s laws will need to be re-coded.