Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Samek, Wojciech (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Montavon, Grégoire (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Vedaldi, Andrea (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Hansen, Lars Kai (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Müller, Klaus-Robert (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Lecture Notes in Artificial Intelligence ; 11700
• Θέματα: Artificial intelligence.; Optical data processing.; Computers.; Computer security.; Computer organization.; Artificial Intelligence.; Image Processing and Computer Vision.; Computing Milieux.; Systems and Data Security.; Computer Systems Organization and Communication Networks.
• Φυσική περιγραφή: XI, 439 p. 152 illus., 119 illus. in color. online resource.
• ISBN: 9783030289546
• DOI: 10.1007/978-3-030-28954-6

The development of "intelligent" systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to "intelligent" machines. Forsensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.