Academic literature on the topic 'Artificial Intelligence, Explainable AI'
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Journal articles on the topic "Artificial Intelligence, Explainable AI"
Raikov, Alexander N. "Subjectivity of Explainable Artificial Intelligence." Russian Journal of Philosophical Sciences 65, no. 1 (June 25, 2022): 72–90. http://dx.doi.org/10.30727/0235-1188-2022-65-1-72-90.
Full textChauhan, Tavishee, and Sheetal Sonawane. "Contemplation of Explainable Artificial Intelligence Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 4 (April 30, 2022): 65–71. http://dx.doi.org/10.17762/ijritcc.v10i4.5538.
Full textDarwish, Ashraf. "Explainable Artificial Intelligence: A New Era of Artificial Intelligence." Digital Technologies Research and Applications 1, no. 1 (January 26, 2022): 1. http://dx.doi.org/10.54963/dtra.v1i1.29.
Full textZednik, Carlos, and Hannes Boelsen. "Scientific Exploration and Explainable Artificial Intelligence." Minds and Machines 32, no. 1 (March 2022): 219–39. http://dx.doi.org/10.1007/s11023-021-09583-6.
Full textAlufaisan, Yasmeen, Laura R. Marusich, Jonathan Z. Bakdash, Yan Zhou, and Murat Kantarcioglu. "Does Explainable Artificial Intelligence Improve Human Decision-Making?" Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 6618–26. http://dx.doi.org/10.1609/aaai.v35i8.16819.
Full textDikmen, Murat, and Catherine Burns. "Abstraction Hierarchy Based Explainable Artificial Intelligence." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 319–23. http://dx.doi.org/10.1177/1071181320641073.
Full textGunning, David, and David Aha. "DARPA’s Explainable Artificial Intelligence (XAI) Program." AI Magazine 40, no. 2 (June 24, 2019): 44–58. http://dx.doi.org/10.1609/aimag.v40i2.2850.
Full textOwens, Emer, Barry Sheehan, Martin Mullins, Martin Cunneen, Juliane Ressel, and German Castignani. "Explainable Artificial Intelligence (XAI) in Insurance." Risks 10, no. 12 (December 1, 2022): 230. http://dx.doi.org/10.3390/risks10120230.
Full textEsmaeili, Morteza, Riyas Vettukattil, Hasan Banitalebi, Nina R. Krogh, and Jonn Terje Geitung. "Explainable Artificial Intelligence for Human-Machine Interaction in Brain Tumor Localization." Journal of Personalized Medicine 11, no. 11 (November 16, 2021): 1213. http://dx.doi.org/10.3390/jpm11111213.
Full textThakker, Dhavalkumar, Bhupesh Kumar Mishra, Amr Abdullatif, Suvodeep Mazumdar, and Sydney Simpson. "Explainable Artificial Intelligence for Developing Smart Cities Solutions." Smart Cities 3, no. 4 (November 13, 2020): 1353–82. http://dx.doi.org/10.3390/smartcities3040065.
Full textDissertations / Theses on the topic "Artificial Intelligence, Explainable AI"
Vincenzi, Leonardo. "eXplainable Artificial Intelligence User Experience: contesto e stato dell’arte." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23338/.
Full textGjeka, Mario. "Uno strumento per le spiegazioni di sistemi di Explainable Artificial Intelligence." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Find full textHammarström, Tobias. "Towards Explainable Decision-making Strategies of Deep Convolutional Neural Networks : An exploration into explainable AI and potential applications within cancer detection." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424779.
Full textCosta, Bueno Vicente. "Fuzzy Horn clauses in artificial intelligence: a study of free models, and applications in art painting style categorization." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673374.
Full textLa presente tesis doctoral contribuye al estudio de las cláusulas de Horn en lógicas difusas, así como a su uso en representación difusa del conocimiento aplicada al diseño de un algoritmo de clasificación de pinturas según su estilo artístico. En la primera parte del trabajo nos centramos en algunas nociones relevantes para la programación lógica, como lo son por ejemplo los modelos libres y las estructuras de Herbrand en lógica matemática difusa. Así pues, probamos la existencia de modelos libres en clases universales difusas de Horn y demostramos que toda teoría difusa universal de Horn sin igualdad tiene un modelo de Herbrand. Asimismo, introducimos dos nociones de minimalidad para modelos libres, y demostramos que estas nociones son equivalentes en el caso de las fully named structures. En la segunda parte de la tesis doctoral, utilizamos cláusulas de Horn combinadas con el modelado cualitativo como marco de representación difusa del conocimiento para la categorización de estilos de pintura artística. Finalmente, diseñamos un clasificador de pinturas basado en cláusulas de Horn evaluadas, descriptores cualitativos de colores y explicaciones. Este algoritmo, que llamamos l-SHE, proporciona razones de los resultados obtenidos y obtiene porcentajes competitivos de precisión en la experimentación.
This PhD thesis contributes to the systematic study of Horn clauses of predicate fuzzy logics and their use in knowledge representation for the design of an art painting style classification algorithm. We first focus the study on relevant notions in logic programming, such as free models and Herbrand structures in mathematical fuzzy logic. We show the existence of free models in fuzzy universal Horn classes, and we prove that every equality-free consistent universal Horn fuzzy theory has a Herbrand model. Two notions of minimality of free models are introduced, and we show that these notions are equivalent in the case of fully named structures. Then, we use Horn clauses combined with qualitative modeling as a fuzzy knowledge representation framework for art painting style categorization. Finally, we design a style painting classifier based on evaluated Horn clauses, qualitative color descriptors, and explanations. This algorithm, called l-SHE, provides reasons for the obtained results and obtains percentages of accuracy in the experimentation that are competitive.
Universitat Autònoma de Barcelona. Programa de Doctorat en Ciència Cognitiva i Llenguatge
Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.
Full textNilsson, Linus. "Explainable Artificial Intelligence for Reinforcement Learning Agents." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294162.
Full textEfter framgångarna inom maskininlärning de senaste årtiondet har förstärkningsinlärning blivit ett primärt forskningsämne för att lösa komplexa uppgifter och inom automation. Tillämpningarna är många, allt från att spela datorspel på en professionell nivå till robotar som samarbetar för att plocka varor i ett lager. Dock så är systemen väldigt komplexa och förståelsen kring varför en agent väljer att lösa en uppgift på ett specifikt sätt är okända för en mänsklig observatör. Detta gör att de praktiska tillämpningarna av dessa agenter är begränsade till icke-kritiska system och den information som kan användas för att lära ut nya sätt att lösa olika uppgifter är dolda. Utifrån detta så har förklarbar artificiell intelligens (XAI) blivit ett område inom forskning som fått allt mer uppmärksamhet de senaste åren. Detta för att kunna förklara maskininlärningssystem för den mänskliga användaren. I denna examensrapport föreslår vi att använda modelloberoende XAI tekniker kombinerat klustringstekniker på enkla Atarispel, vi föreslår även ett sätt att automatisera hur man kan utvärdera hur väl en förklaring förklarar beteendet hos agenterna. Detta i ett försök att upptäcka till vilken grad modelloberoende XAI tekniker kan användas för att förklara beteenden hos förstärkningsinlärningsagenter. De testade metoderna var RISE, t-SNE och Deletion. Metoderna utvärderades på flera olika agenter, tränade att spelaAtari-breakout. Resultatet visar att de kan användas för att förklara beteendet hos agenterna på en lokal nivå (en individuell bild ur ett spel), globalt beteende (över den totala spelsekvensen) samt även att metoderna kan hitta olika strategier användna av de olika agenterna där mängden träning de fått skiljer sig.
Karlsson, Marcus. "Developing services based on Artificial Intelligence." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-73090.
Full textDenna uppsats utforskar utvecklingsprocessen av tjänster baserade på artificiell intelligens (AI) i en industriell miljö. Tekniken har fått ett förnyat intresse vilket har lett till att allt fler ledande teknik företag och start-up:s har integrerat AI i deras marknads erbjudande. Teknikens generella applikations möjlighet för att kunna förbättra produkter och tjänster tillsammans med dess automatiserings möjlighet för ökad operationell effektivitet gör den till en värdefull tillgång för företag. Dock så är implementations graden fortfarande låg för majoriteten av industrins aktörer. Forskningen inom AI området har varit mycket teknik dominerat med lite bidrag från andra forskningsdiscipliner. Därför syftar denna uppsats att identifiera utvecklingsutmaningar med AI tjänster och genom att hämta delar från tjänsteutveckling- och värde teori generera ett processramverk som premierar implementation. Uppsatsen har två huvudsakliga forskningsbidrag. Först genom att jämföra skillnader mellan teoretiska och praktiska utvecklingsutmaningar, sedan bidra genom att kombinera AI med tjänsteutveckling- och värdeteori. Den empiriska forskningen utfördes genom en fallstudie baserad på ett systematic combining tillvägagångsätt. På så sätt rör sig forskning iterativt mellan teori och empiri för att forma och stödja uppsatsen genom arbetet. Datat var insamlad genom semi strukturerade intervjuer med två separata, medvetet valda intervjugrupper där ena utgjorde en AI expert grupp och andra en intern grupp för fallstudien. Detta stöttades av deltagande observationer inom fallstudiens miljö. Dataanalysen utfördes med metoden flexible pattern matching. Resultatet var uppdelat i två olika sektioner, den första med praktiska utmaningar och den andra med utvecklingsaspekter av AI tjänsteutveckling. Dessa kombinerades med de utvalda teorierna för att skapa ett processramverk. Uppsatsen visar ett under studerat område angående affär och organisation i relation till AI tjänsteutveckling. Ett flertal av sådana utmaningar identifierades med begränsat stöd i existerande forskningslitteratur. För en mer utbredd adoption av AI tekniken behövs mer forskning för att förstå hur AI ska integreras med organisationer. Vidare, hållbarhet och etiska aspekter var inte en primär aspekt i resultatet, endast bemött i en av intervjuerna trots samlingen av artiklar och identifierade risker i litteraturen. Till sist, det tvärvetenskapliga angreppsättet var givande för AI området för att bättre integrera tekniken till en industriell miljö. Det utvecklade processramverket kunde bygga på existerande tjänsteutvecklings modeller för att hantera de identifierade utmaningarna.
Rouget, Thierry. "Learning explainable concepts in the presence of a qualitative model." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/9762.
Full textAmarasinghe, Kasun. "Explainable Neural Networks based Anomaly Detection for Cyber-Physical Systems." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6091.
Full textKim, Jee Won. "How speciesism affects artificial intelligence (AI) adoption intent." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/228673/1/Jee%20Won_Kim_Thesis.pdf.
Full textBooks on the topic "Artificial Intelligence, Explainable AI"
Krötzsch, Markus, and Daria Stepanova, eds. Reasoning Web. Explainable Artificial Intelligence. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31423-1.
Full textLahby, Mohamed, Utku Kose, and Akash Kumar Bhoi. Explainable Artificial Intelligence for Smart Cities. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003172772.
Full textAhmed, Mohiuddin, Sheikh Rabiul Islam, Adnan Anwar, Nour Moustafa, and Al-Sakib Khan Pathan, eds. Explainable Artificial Intelligence for Cyber Security. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96630-0.
Full textGaur, Loveleen, and Biswa Mohan Sahoo. Explainable Artificial Intelligence for Intelligent Transportation Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09644-0.
Full textKamath, Uday, and John Liu. Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83356-5.
Full textArtificial intelligence. 3rd ed. Reading, Mass: Addison-Wesley, 1992.
Find full textWinston, Patrick Henry. Artificial intelligence. 3rd ed. Reading, MA: Addison-Wesley, 1993.
Find full textSurviving AI. [Place of publication not identified]: Three Cs, 2015.
Find full textArtificial intelligence. 3rd ed. Reading, Mass: Addison-Wesley Pub. Co., 1992.
Find full textAI jiten: Encyclopedia of artificial intelligence. 2nd ed. Tōkyō: Kyōritsu Shuppan, 2003.
Find full textBook chapters on the topic "Artificial Intelligence, Explainable AI"
Holzinger, Andreas, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, and Wojciech Samek. "xxAI - Beyond Explainable Artificial Intelligence." In xxAI - Beyond Explainable AI, 3–10. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_1.
Full textSamek, Wojciech, and Klaus-Robert Müller. "Towards Explainable Artificial Intelligence." In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 5–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28954-6_1.
Full textFan, Xiuyi, and Siyuan Liu. "Explainable AI for Classification Using Probabilistic Logic Inference." In Artificial Intelligence, 16–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93049-3_2.
Full textGuidotti, Riccardo, Anna Monreale, Dino Pedreschi, and Fosca Giannotti. "Principles of Explainable Artificial Intelligence." In Explainable AI Within the Digital Transformation and Cyber Physical Systems, 9–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76409-8_2.
Full textGaur, Loveleen, and Biswa Mohan Sahoo. "Explainable AI in ITS: Ethical Concerns." In Explainable Artificial Intelligence for Intelligent Transportation Systems, 79–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09644-0_5.
Full textChennam, Krishna Keerthi, Swapna Mudrakola, V. Uma Maheswari, Rajanikanth Aluvalu, and K. Gangadhara Rao. "Black Box Models for eXplainable Artificial Intelligence." In Explainable AI: Foundations, Methodologies and Applications, 1–24. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12807-3_1.
Full textCruz, Francisco, Richard Dazeley, and Peter Vamplew. "Memory-Based Explainable Reinforcement Learning." In AI 2019: Advances in Artificial Intelligence, 66–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35288-2_6.
Full textGaur, Loveleen, and Biswa Mohan Sahoo. "Introduction to Explainable AI and Intelligent Transportation." In Explainable Artificial Intelligence for Intelligent Transportation Systems, 1–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09644-0_1.
Full textSayed-Mouchaweh, Moamar. "Prologue: Introduction to Explainable Artificial Intelligence." In Explainable AI Within the Digital Transformation and Cyber Physical Systems, 1–8. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76409-8_1.
Full textMamalakis, Antonios, Imme Ebert-Uphoff, and Elizabeth A. Barnes. "Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science." In xxAI - Beyond Explainable AI, 315–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_16.
Full textConference papers on the topic "Artificial Intelligence, Explainable AI"
Ignatiev, Alexey. "Towards Trustable Explainable AI." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/726.
Full textByrne, Ruth M. J. "Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/876.
Full textKou, Ziyi, Lanyu Shang, Yang Zhang, Zhenrui Yue, Huimin Zeng, and Dong Wang. "Crowd, Expert & AI: A Human-AI Interactive Approach Towards Natural Language Explanation Based COVID-19 Misinformation Detection." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/706.
Full textBelle, Vaishak. "Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/733.
Full textClinciu, Miruna-Adriana, and Helen Hastie. "A Survey of Explainable AI Terminology." In Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-8403.
Full textSampat, Shailaja. "Technical, Hard and Explainable Question Answering (THE-QA)." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/916.
Full textKapcia, Marcin, Hassan Eshkiki, Jamie Duell, Xiuyi Fan, Shangming Zhou, and Benjamin Mora. "ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics." In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai52525.2021.00134.
Full textYu, Han, Yang Liu, Xiguang Wei, Chuyu Zheng, Tianjian Chen, Qiang Yang, and Xiong Peng. "Fair and Explainable Dynamic Engagement of Crowd Workers." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/961.
Full textReiter, Ehud. "Natural Language Generation Challenges for Explainable AI." In Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-8402.
Full textZheng, Yongqing, Han Yu, Kun Zhang, Yuliang Shi, Cyril Leung, and Chunyan Miao. "Intelligent Decision Support for Improving Power Management." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/965.
Full textReports on the topic "Artificial Intelligence, Explainable AI"
Core, Mark G., H. C. Lane, Michael van Lent, Dave Gomboc, Steve Solomon, and Milton Rosenberg. Building Explainable Artificial Intelligence Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada459166.
Full textPhillips, P. Jonathon, Carina A. Hahn, Peter C. Fontana, Amy N. Yates, Kristen Greene, David A. Broniatowski, and Mark A. Przybocki. Four Principles of Explainable Artificial Intelligence. National Institute of Standards and Technology, September 2021. http://dx.doi.org/10.6028/nist.ir.8312.
Full textDiaz-Herrera, Jorge L. Artificial Intelligence (AI) and Ada: Integrating AI with Mainstream Software Engineering. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada286093.
Full textArnold, Zachary, and Ngor Luong. China’s Artificial Intelligence Industry Alliance. Center for Security and Emerging Technology, May 2021. http://dx.doi.org/10.51593/20200094.
Full textGillespie, Nicole, Caitlin Curtis, Rossana Bianchi, Ali Akbari, and Rita Fentener van Vlissingen. Achieving Trustworthy AI: A Model for Trustworthy Artificial Intelligence. Australia: The University of Queensland and KPMG, November 2020. http://dx.doi.org/10.14264/ca0819d.
Full textWaugh, Gordon W., and Deirdre J. Knapp. Development of an Army Civilian Artificial Intelligence (AI) Specialty. Fort Belvoir, VA: Defense Technical Information Center, November 1997. http://dx.doi.org/10.21236/ada343149.
Full textWiley, Cathy J. User's Guide to the (ncar ai) Artificial Intelligence Technical Library. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada237270.
Full textMurdick, Dewey, and Patrick Thomas. Patents and Artificial Intelligence: A Primer. Center for Security and Emerging Technology, September 2020. http://dx.doi.org/10.51593/20200038.
Full textHannas, William, Huey-Meei Chang, Daniel Chou, and Brian Fleeger. China's Advanced AI Research: Monitoring China's Paths to "General" Artificial Intelligence. Center for Security and Emerging Technology, July 2022. http://dx.doi.org/10.51593/20210064.
Full textBaker, James E. Ethics and Artificial Intelligence: A Policymaker's Introduction. Center for Security and Emerging Technology, April 2021. http://dx.doi.org/10.51593/20190022.
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