Search Results - Algorithm transparency vs. explainability

On conflicts between ethical and logical principles in artificial intelligence by D’Acquisto, Giuseppe

“…Artificial intelligence is nowadays a reality. Setting rules on the potential outcomes of intelligent machines, so that no surprise can be expected by humans…”
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The Effects of Explainability and User Control on Algorithmic Transparency: The Moderating Role of Algorithmic Literacy by Moon, Jang Ho, Kim, Seheon, Jung, Youngju, Bang, Jaeyeon, Sung, Yongjun

“… In response, platforms have introduced transparency features such as algorithm-based recommendation explanations and user control features…”
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It is Not "Accuracy vs. Explainability"-We Need Both for Trustworthy AI Systems by Petkovic, Dragutin

“… However, AI systems may produce errors, can exhibit bias, may be sensitive to noise in the data, and often lack technical and judicial transparency resulting in reduction in trust and challenges to their adoption…”
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Detection of COVID-19 in X-ray Images Using Densely Connected Squeeze Convolutional Neural Network (DCSCNN): Focusing on Interpretability and Explainability of the Black Box Model by Ali, Sikandar, Hussain, Ali, Bhattacharjee, Subrata, Athar, Ali, Abdullah, Kim, Hee-Cheol

“…The novel coronavirus (COVID-19), which emerged as a pandemic, has engulfed so many lives and affected millions of people across the world since December 2019…”
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Reflections and attentiveness on eXplainable Artificial Intelligence (XAI). The journey ahead from criticisms to human–AI collaboration by Herrera, Francisco

“…). As AI systems grow in size and complexity, ensuring interpretability and transparency becomes essential, especially in high-stakes applications…”
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From Black Box to Glass Box: A Practical Review of Explainable Artificial Intelligence (XAI) by Liu, Xiaoming, Huang, Danni, Yao, Jingyu, Dong, Jing, Song, Litong, Wang, Hui, Yao, Chao, Chu, Weishen

“…”, limiting trust and accountability. However, most existing reviews treat explainability either as a technical problem or a philosophical issue, without connecting interpretability techniques to their real-world implications…”
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A cloud-based architecture for explainable Big Data analytics using self-structuring Artificial Intelligence by Mills, Nishan, Issadeen, Zafar, Matharaarachchi, Amali, Bandaragoda, Tharindu, De Silva, Daswin, Jennings, Andrew, Manic, Milos

“… It is increasingly challenging for conventional AI algorithms to process and transform this data, analyse and visualise a broad spectrum of insights, and then formulate the explainability…”
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Managing Paradoxical Tensions in the Implementation of Explainable AI for Product Innovation by Amarilli, Fabrizio, Uboldi, Sara, Saraceni, Francesca, Tencati, Lorenzo

“… We identify four persistent tensions: automation vs. human judgment, transparency vs. complexity, speed vs…”
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Artificial intelligence in diabetes care: from predictive analytics to generative AI and implementation challenges by Deng, Mengqi, Yang, Ruiye, Zheng, Xiaoran, Deng, Yaoqi, Jiang, Junyi

“… From a technology point of view, explainability solutions and culturally-aware design align transparency with cultural sensibility…”
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Systematic Literature Review On Explainable AI In Finance: Methods, Applications, And Research Gaps by Bhardwaj, Alok

“…” algorithms still hinder financial organizations. SHAP, LIME, Integrated Gradients, and Layer-wise Relevance Propagation increase model transparency, accountability, and confidence…”
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Consumer Responses to Algorithmic Decisions by Demirdag, Bilge Ipek

“… Nevertheless, people are often algorithm averse, that is, they are less willing to rely on algorithms than humans in tasks such as forecasting…”
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Understanding the Role of Interactivity and Explanation in Adaptive Experiences by Guo, Lijie

“… Whether the currently ongoing research on adaptive experiences has focused on personalization algorithms, explainability, user engagement, or privacy and security, there is growing interest…”
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Machine Learning for Patent Intelligence Opportunities and Challenges by Denter, Nils

“…The analysis of large data volumes for decision-making has evolved from a sideline to a key driver of economic success in the business world of today. As being…”
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