• Aucun résultat trouvé

Good AI and Law

N/A
N/A
Info
Télécharger
Protected

Academic year: 2022

Partager "Good AI and Law"

Copied!
1
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

Good AI & Law

Bart VERHEIJ

Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University of Groningen

AI's successes are these days so prominent that—if we believe reports in the news—the times seem near that machines perform better at any human task than humans themselves. At the same time the prominent AI technique of neural networks—today typically called deep learning—is often considered to lead to black box results, hindering transparency, explainability and responsibility, values that are central in the domain of law. So in that specific sense, the distance between neural network AI and the needs of the law is vast. In this talk, it is claimed that for good AI & Law we need an AI that can provide good answers to our questions, has good reasons for them and makes good choices. It is argued that the path towards good AI & Law requires the integration of data-driven and knowledge-based AI, and that argumentation as it occurs in the law can show the way to such integration.

Reference

Verheij, B. (2018). Arguments for Good Artificial Intelligence. Groningen: University of Groningen. www.ai.rug.nl/~verheij/oratie.

Références

Télécharger maintenant ( PDF - 1 Page - 117.41 KB )

Documents relatifs

Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch

Yinlong Qian, Jing Dong, Wei Wang, and Tieniu Tan, ”Deep Learning for Steganalysis via Convolutional Neural Networks,” in Proceedings of SPIE Media Watermarking, Security, and

Deep Model Compression and Architecture Optimization for Embedded Systems: A Survey

[73] developed a method called BinaryConnect to train deep neural networks using binary weights dur- ing the forward and backward propagation, while storing the true precision of

Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

When data is abundant, the most powerful learning techniques are the ones based on the use of neural networks, given place to the so-called deep-learning (see [ 6 ] and [ 7 ]

Deep learning applied to data-driven dynamic characterization of hysteretic piezoelectric micromanipulators

In a previous work by the authors a 2-DOF piezoelectric actuator is modeled in [12], using shallow artificial neural networks at high frequency rates in a black-box approach..

Identification of non linear fractional systems using continuous time neural networks

Contrary to the classical recurent neural networks, that use delay blocks and only give a black box model of the process, Ctnn , by separating the non linear static part of the

AI ML NIT Patna at HASOC 2019: Deep Learning Approach for Identification of Abusive Content

In this paper, we have used the multi-lingual HASOC Corpus [10] and pro- posed a deep learning model based on Convolutional Neural Networks (CNN) to identify Hate Speech and

Deep learning applied to data-driven dynamic characterization of hysteretic piezoelectric micromanipulators

In a previous work by the authors a 2-DOF piezoelectric actuator is modeled in [12], using shallow artificial neural networks at high frequency rates in a black-box approach..

Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

When data is abundant, the most powerful learning techniques are the ones based on the use of neural networks, given place to the so-called deep-learning (see [ 6 ] and [ 7 ]