Policy versus Practice: Conceptions of Artificial Intelligence
10 Pages Posted: 7 Aug 2019
Date Written: August 2, 2019
The recent growth of concern around issues such as social biases implicit in algorithms, economic impacts of artificial intelligence (AI), or potential existential threats posed by the development of AI technology call for consideration of regulatory action to forestall or constrain certain developments in the fields of AI and machine learning. However, definitional ambiguity hampers the possibility of conversation about these urgent topics of public concern. Legal and regulatory interventions require agreed-upon definitions, but consensus around a definition of AI has been elusive, especially in policy conversations. With an eye towards practical working definitions and a broader understanding of positions on these issues, we document variation in policy-maker and researcher conceptions of AI, as well as researchers’ views on concerns about AI. We execute a survey of AI practitioners, a review of definitions from the AI research literature, a review of existing AI-related policy documents, and an analysis of public commentary relating to AI from Twitter. We conclude from these data sources that, although there is substantial variation, many AI and machine learning researchers tend to favor definitions of AI that emphasize technical functionality while policy-makers favor definitions that emphasize comparison to human thinking and behavior. We point out that definitions that adhere closely to the functionality of AI systems are more inclusive of technologies in use today, whereas definitions that emphasize human-like capabilities are most applicable to hypothetical future technologies.
Keywords: Artificial Intelligence, Machine Learning, Tech Policy
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