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    Accepted author manuscript, 75.9 KB, PDF document

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Explainable-by-design Deep Learning

Research output: Contribution to conference - Without ISBN/ISSN › Speech

Published

Standard

Explainable-by-design Deep Learning. / Angelov, Plamen.
2021. IEEE Pervasive Computing.

Research output: Contribution to conference - Without ISBN/ISSN › Speech

Harvard

Angelov, P 2021, 'Explainable-by-design Deep Learning', IEEE Pervasive Computing, 1/01/00.

APA

Angelov, P. (2021). Explainable-by-design Deep Learning. IEEE Pervasive Computing.

Vancouver

Angelov P. Explainable-by-design Deep Learning. 2021. IEEE Pervasive Computing.

Author

Angelov, Plamen. / Explainable-by-design Deep Learning. IEEE Pervasive Computing.

Bibtex

@conference{d67c74d07d8a4c22b39062bc0238e569,
title = "Explainable-by-design Deep Learning",
abstract = "MACHINE and AI justifiably attract the attention and interest not only of the wider scientific community and industry, but also society and policy makers. However,even the most powerful (in terms of accuracy) algorithms such as deep learning (DL) can give a wrong output, which may be fatal. Due to the opaque and cumbersome model structure used by DL, some authors started to talk about a dystopian “black box” society. Despite the success in this area, the way computers learn is still principally different from the way people acquire new knowledge, recogniseobjects and make decisions.",
keywords = "Explainable AI",
author = "Plamen Angelov",
year = "2021",
month = mar,
language = "English",
note = "IEEE Pervasive Computing ; Conference date: 01-01-1900",

}

RIS

TY - CONF

T1 - Explainable-by-design Deep Learning

AU - Angelov, Plamen

PY - 2021/3

Y1 - 2021/3

N2 - MACHINE and AI justifiably attract the attention and interest not only of the wider scientific community and industry, but also society and policy makers. However,even the most powerful (in terms of accuracy) algorithms such as deep learning (DL) can give a wrong output, which may be fatal. Due to the opaque and cumbersome model structure used by DL, some authors started to talk about a dystopian “black box” society. Despite the success in this area, the way computers learn is still principally different from the way people acquire new knowledge, recogniseobjects and make decisions.

AB - MACHINE and AI justifiably attract the attention and interest not only of the wider scientific community and industry, but also society and policy makers. However,even the most powerful (in terms of accuracy) algorithms such as deep learning (DL) can give a wrong output, which may be fatal. Due to the opaque and cumbersome model structure used by DL, some authors started to talk about a dystopian “black box” society. Despite the success in this area, the way computers learn is still principally different from the way people acquire new knowledge, recogniseobjects and make decisions.

KW - Explainable AI

M3 - Speech

T2 - IEEE Pervasive Computing

Y2 - 1 January 1900

ER -