Automatic Modeling of Logic Device Performance Based on Machine Learning and Explainable AI

2020 ◽  
Author(s):  
Seungju Kim ◽  
Kwangseok Lee ◽  
Hyeon-Kyun Noh ◽  
Youngkyu Shin ◽  
Kyu-Baik Chang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Device Performance ◽  
Logic Device ◽  
Automatic Modeling ◽  
Explainable Ai

Understanding Machine Learning for Diversified Portfolio Construction by Explainable AI

2020 ◽  
Author(s):  
Markus Jaeger ◽  
Stephan Krügel ◽  
Dimitri Marinelli ◽  
Jochen Papenbrock ◽  
Peter Schwendner
Keyword(s):  
Machine Learning ◽  
Portfolio Construction ◽  
Explainable Ai ◽  
Diversified Portfolio

Explainable AI Models of Stock Crashes: A Machine-Learning Explanation of the Covid March 2020 Equity Meltdown

2021 ◽  
Author(s):  
Jean-Jacques Ohana ◽  
Steve Ohana ◽  
Eric Benhamou ◽  
David Saltiel ◽  
Beatrice Guez
Keyword(s):  
Machine Learning ◽  
Explainable Ai

scholarly journals PyDL8.5: a Library for Learning Optimal Decision Trees

2020 ◽  
Author(s):  
Gaël Aglin ◽  
Siegfried Nijssen ◽  
Pierre Schaus
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Efficient Algorithm ◽  
Optimal Decision ◽  
Learning Tasks ◽  
Explainable Ai ◽  
Classification Tasks ◽  
Interpretable Models ◽  
Limited Depth

Decision Trees (DTs) are widely used Machine Learning (ML) models with a broad range of applications. The interest in these models has increased even further in the context of Explainable AI (XAI), as decision trees of limited depth are very interpretable models. However, traditional algorithms for learning DTs are heuristic in nature; they may produce trees that are of suboptimal quality under depth constraints. We introduce PyDL8.5, a Python library to infer depth-constrained Optimal Decision Trees (ODTs). PyDL8.5 provides an interface for DL8.5, an efficient algorithm for inferring depth-constrained ODTs. The library provides an easy-to-use scikit-learn compatible interface. It cannot only be used for classification tasks, but also for regression, clustering, and other tasks. We introduce an interface that allows users to easily implement these other learning tasks. We provide a number of examples of how to use this library.

Key factors governing the device performance of CIGS solar cells: Insights from machine learning

Solar Energy ◽  
2021 ◽  
Vol 228 ◽  
pp. 45-52
Author(s):  
Chengwan Zhu ◽  
Wu Liu ◽  
Yaoyao Li ◽  
Xiaomin Huo ◽  
Haotian Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solar Cells ◽  
Device Performance ◽  
Key Factors ◽  
Cigs Solar Cells

Towards Human-Centered AI: Psychological concepts as foundation for empirical XAI research

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Katharina Weitz
Keyword(s):  
Machine Learning ◽  
Mental Models ◽  
Empirical Studies ◽  
End Users ◽  
User Needs ◽  
Specific Task ◽  
Explainable Ai ◽  
First Results ◽  
Shed Light ◽  
Psychological Concepts

Abstract Human-Centered AI is a widely requested goal for AI applications. To reach this is explainable AI promises to help humans to understand the inner workings and decisions of AI systems. While different XAI techniques have been developed to shed light on AI systems, it is still unclear how end-users with no experience in machine learning perceive these. Psychological concepts like trust, mental models, and self-efficacy can serve as instruments to evaluate XAI approaches in empirical studies with end-users. First results in applications for education, healthcare, and industry suggest that one XAI does not fit all. Instead, the design of XAI has to consider user needs, personal background, and the specific task of the AI system.

GPT-3 AI language tool calls for cautious optimism

2020 ◽  
Keyword(s):  
Machine Learning ◽  
Public Funding ◽  
Computational Effort ◽  
Learning Models ◽  
Content Type ◽  
Business Applications ◽  
Machine Learning Model ◽  
Explainable Ai ◽  
Advance Research ◽  
Machine Learning Models

Significance It required arguably the single largest computational effort for a machine learning model to date, and is it capable of producing text at times indistinguishable from the work of a human author. This has generated considerable excitement about potentially transformative business applications -- and concerns about the system's weaknesses and possible misuse. Impacts Stereotypes and biases in machine learning models will become increasingly problematic as they are adopted by businesses and governments. The use of flawed AI tools that result in embarrassing failures risk cuts to public funding for AI research. Academia and industry face pressure to advance research into explainable AI, but progress is slow.

scholarly journals An Initial Study of Machine Learning Underspecification Using Feature Attribution Explainable AI Algorithms: A COVID-19 Virus Transmission Case Study

2021 ◽  
pp. 323-335
Author(s):  
James Hinns ◽  
Xiuyi Fan ◽  
Siyuan Liu ◽  
Veera Raghava Reddy Kovvuri ◽  
Mehmet Orcun Yalcin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virus Transmission ◽  
Initial Study ◽  
Transmission Case ◽  
Explainable Ai

scholarly journals Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends

2021 ◽  
Vol 4 ◽  
Author(s):  
Lindsay Wells ◽  
Tomasz Bednarz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Reinforcement Learning ◽  
User Studies ◽  
Human In The Loop ◽  
Search Results ◽  
Explainable Artificial Intelligence ◽  
Explainable Ai ◽  
Immersive Visualization

Research into Explainable Artificial Intelligence (XAI) has been increasing in recent years as a response to the need for increased transparency and trust in AI. This is particularly important as AI is used in sensitive domains with societal, ethical, and safety implications. Work in XAI has primarily focused on Machine Learning (ML) for classification, decision, or action, with detailed systematic reviews already undertaken. This review looks to explore current approaches and limitations for XAI in the area of Reinforcement Learning (RL). From 520 search results, 25 studies (including 5 snowball sampled) are reviewed, highlighting visualization, query-based explanations, policy summarization, human-in-the-loop collaboration, and verification as trends in this area. Limitations in the studies are presented, particularly a lack of user studies, and the prevalence of toy-examples and difficulties providing understandable explanations. Areas for future study are identified, including immersive visualization, and symbolic representation.

scholarly journals Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare

Patterns ◽  
2021 ◽  
pp. 100395
Author(s):  
Somya D. Mohanty ◽  
Deborah Lekan ◽  
Thomas P. McCoy ◽  
Marjorie Jenkins ◽  
Prashanti Manda
Keyword(s):  
Machine Learning ◽  
Explainable Ai ◽  
Readmission Risk
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