Results 41 to 50 of about 81,501 (265)
Visualizations for an Explainable Planning Agent
, 2018 In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making.
Bellamy, Rachel K. E. +6 more
core +1 more source
Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI
, 2021 Many ML models are opaque to humans, producing decisions too complex for humans to easily understand. In response, explainable artificial intelligence (XAI) tools that analyze the inner workings of a model have been created. Despite these tools' strength in translating model behavior, critiques have raised concerns about the impact of XAI tools as a ...
Alikhademi, Kiana +3 more
openaire +2 more sources
Medically-oriented design for explainable AI for stress prediction from physiological measurements
BMC Medical Informatics and Decision Making, 2022 Background In the last decade, a lot of attention has been given to develop artificial intelligence (AI) solutions for mental health using machine learning.
Dalia Jaber +3 more
doaj +1 more source
Applied Mechanics, 2023 Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured parts ...
Akshansh Mishra +3 more
doaj +1 more source
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, 2020 Department of Computer Science and EngineeringAs deep learning has grown fast, so did the desire to interpret deep learning black boxes. As a result, many analysis tools have emerged to interpret it.
Lee, Ginkyeng
core
Aggressive prostate cancer is associated with pericyte dysfunction
Molecular Oncology, EarlyView.Tumor‐produced TGF‐β drives pericyte dysfunction in prostate cancer. This dysfunction is characterized by downregulation of some canonical pericyte markers (i.e., DES, CSPG4, and ACTA2) while maintaining the expression of others (i.e., PDGFRB, NOTCH3, and RGS5).
Anabel Martinez‐Romero +11 more
wiley +1 more source
What Does Explainable AI Really Mean? A New Conceptualization of Perspectives [PDF]
, 2017 We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and ...
Besold, T. R., Doran, D., Schulz, S.C.
core +2 more sources
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval
, 2019 While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks.
Burachas, Giedrius +4 more
core +2 more sources
Molecular Oncology, EarlyView.Development of therapies targeting cancer‐associated fibroblasts (CAFs) necessitates preclinical model systems that faithfully represent CAF–tumor biology. We established an in vitro coculture system of patient‐derived pancreatic CAFs and tumor cell lines and demonstrated its recapitulation of primary CAF–tumor biology with single‐cell transcriptomics ...
Elysia Saputra +10 more
wiley +1 more source
Explaining Machines: Social Management of Incomprehensible Algorithms. Introduction
Sociologica, 2023 This short introduction presents the symposium ‘Explaining Machines’. It locates the debate about Explainable AI in the history of the reflection about AI and outlines the issues discussed in the contributions.
Elena Esposito
doaj +1 more source