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A study into the layers of automated decision-making: emergent normative and legal aspects of deep learning.

Karanasiou, A. and Pinotsis, D.A., 2017. A study into the layers of automated decision-making: emergent normative and legal aspects of deep learning. International Review of Law, Computers and Technology, 31 (2), 170- 187.

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IRLCT special issue (final ms) post proof.pdf - Accepted Version
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DOI: 10.1080/13600869.2017.1298499


The paper dissects the intricacies of automated decision making (ADM) and urges for refining the current legal definition of artificial intelligence (AI) when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. Whilst coming up with a toolkit to measure algorithmic determination in automated/semi-automated tasks might be proven to be a tedious task for the legislator, our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm. The paper offers a fresh look at AI, which exposes certain vulnerabilities in its current legal interpretation. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of human–machine interaction. Part 2 further discusses the intricate nature of AI algorithms and considers how one can utilize observed patterns in acquired data. Finally, the paper explores the legal challenges that result from user empowerment and the requirement for data transparency.

Item Type:Article
Uncontrolled Keywords:Machine learning; algorithmic accountability
Group:Faculty of Media & Communication
ID Code:28269
Deposited By: Symplectic RT2
Deposited On:27 Mar 2017 14:43
Last Modified:14 Mar 2022 14:03


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