Sökning: "explainable machine learning"

Visar resultat 1 - 5 av 13 avhandlingar innehållade orden explainable machine learning.

1. 1. Visual Analytics for Explainable and Trustworthy Machine Learning

   Författare :Angelos Chatzimparmpas; Andreas Kerren; Rafael M. Martins; Ilir Jusufi; Alex Endert; Linnéuniversitetet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; visualization; interaction; visual analytics; explainable machine learning; XAI; trustworthy machine learning; ensemble learning; dimensionality reduction; supervised learning; unsupervised learning; ML; AI; tabular data; visualisering; interaktion; visuell analys; förklarlig maskininlärning; XAI; pålitlig maskininlärning; ensembleinlärning; dimensionesreducering; övervakad inlärning; oövervakad inlärning; ML; AI; tabelldata; Computer Science; Datavetenskap; Informations- och programvisualisering; Information and software visualization;

   Sammanfattning : The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. LÄS MER

3. 2. Human In Command Machine Learning

   Författare :Lars Holmberg; Paul Davidsson; Per Linde; Malmö universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Human-centered AI ML; Explainable AI; Machine Learning; Human In the Loop ML; Interaktionsdesign; Interaktionsdesign;

   Sammanfattning : Machine Learning (ML) and Artificial Intelligence (AI) impact many aspects of human life, from recommending a significant other to assist the search for extraterrestrial life. The area develops rapidly and exiting unexplored design spaces are constantly laid bare. LÄS MER

4. 3. Machine Learning Survival Models : Performance and Explainability

   Författare :Abdallah Alabdallah; Mattias Ohlsson; Thorsteinn Rögnvaldsson; Sepideh Pashami; Erik Frisk; Högskolan i Halmstad; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Survival Analysis; Explainable Artificial Intelligence; Survival Patterns; Counterfactual Explanations; Evaluation Metrics; Concordance Index;

   Sammanfattning : Survival analysis is an essential statistics and machine learning field in various critical applications like medical research and predictive maintenance. In these domains understanding models' predictions is paramount. LÄS MER

5. 4. Learning from Complex Medical Data Sources

   Författare :Jonathan Rebane; Panagiotis Papapetrou; Isak Samsten; Myra Spiliopoulou; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Data Science; Healthcare; Complex Data; Explainable AI; Deep Learning; data- och systemvetenskap; Computer and Systems Sciences;

   Sammanfattning : Large, varied, and time-evolving data sources can be observed across many domains and present a unique challenge for classification problems, in which traditional machine learning approaches must be adapted to accommodate for the complex nature of such data. Across most domains, there is also a need for machine learning models that are both well-performing and interpretable, to help provide explanations of a model's decisions that stakeholders can trust and take appropriate actions with. LÄS MER

7. 5. Context-based explanations for machine learning predictions

   Författare :Sule Anjomshoae; Lili Jiang; Vicenç Torra; Maria Riveiro; Umeå universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Explainable AI; explainability; interpretability; black-box models; deep learning; neural networks; contextual importance; Computer Science; datalogi;

   Sammanfattning : In recent years, growing concern regarding trust in algorithmic decision-making has drawn attention to more transparent and interpretable models. Laws and regulations are moving towards requiring this functionality from information systems to prevent unintended side effects. LÄS MER