A Practical Overview of Safety Concerns and Mitigation Methods for Visual Deep Learning Algorithms

Saeed Bakhshi Germi, Esa Rahtu

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

5 Lataukset (Pure)

Abstrakti

This paper proposes a practical list of safety concerns and mitigation methods for visual deep learning algorithms. The growing success of deep learning algorithms in solving non-linear and complex problems has recently attracted the attention of safety-critical applications. While the state-of-the-art methods achieve high performance in synthetic and real-case scenarios, it is impossible to verify/validate their reliability based on currently available safety standards. Recent works try to solve the issue by providing a list of safety concerns and mitigation methods in generic machine learning algorithms from the standards’ perspective. However, these solutions are either vague, and non-practical when dealing with deep learning methods in real-case scenarios, or they are shallow and fail to address all potential safety concerns. This paper provides an in-depth look at the underlying cause of faults in a visual deep learning algorithm to find a practical and complete safety concern list with potential state-of-the-art mitigation strategies.
AlkuperäiskieliEnglanti
OtsikkoSafeAI 2022: Proceedings of the Workshop on Artificial Intelligence Safety 2022 (SafeAI 2022)
ToimittajatGabriel Pedroza, José Hernández-Orallo, Xin Cynthia Chen, Xiaowei Huang, Huáscar Espinoza, Mauricio Castillo-Effen, John McDermid, Richard Mallah, Seán Ó hÉigeartaigh
TilaJulkaistu - 17 helmik. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaSafeAI: The AAAI's Workshop on Artificial Intelligence Safety - Virtual
Kesto: 28 helmik. 20221 maalisk. 2022
https://safeai.webs.upv.es/

Julkaisusarja

NimiCEUR Workshop Proceedings
KustantajaCEUR-WS
Vuosikerta3087
ISSN (elektroninen)1613-0073

Workshop

WorkshopSafeAI
Ajanjakso28/02/221/03/22
www-osoite

Julkaisufoorumi-taso

  • Jufo-taso 1

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