Automated Decision-Making and Administrative Law
Forthcoming, P. Cane et al. (eds), Oxford Handbook of Comparative Administrative Law, Oxford, Oxford University Press, 2020
25 Pages Posted: 23 Aug 2019
Date Written: August 5, 2019
Over the past few years, there has been much discussion regarding the potential of automated-decision making (‘ADM’) systems powered by mechanisms of computational intelligence such as machine learning or deep learning (commonly referred to as ‘Artificial Intelligence’ or ‘AI’). To date, such forms of (big) data analysis are most prominently relied on by the private sector, such as the search algorithms used by online search engines or the recommendation algorithms used by e-commerce and entertainment services platforms. These forms of data analysis in essence offer three main benefits, namely the speed and efficiency of decision-making as well as an ability to detect correlations that may be undetectable to the human brain.
The efficiency, speed and correlations offered by these forms of data analytics are also appealing in the public sector. Indeed, various products of computational learning are already being used in administrative processes and will likely become much more prominent in future years. Whereas these techniques offer important potential benefits, they have also been the cause of concern. Indeed, the use of ADM in administrative settings raises numerous important legal and ethical challenges. This paper introduces these new elements in the administrative toolbox and to survey related consequences, in particular possible implications for the principle of transparency.
Keywords: AI, machine learning, deep learning, administrative law, public law, transparency, explainable AI
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