About this Research Topic
In recent years, the integration of artificial intelligence (AI), primarily through machine learning (ML) techniques, into virtually every domain of knowledge and practice has surged dramatically. This uptick has propelled pertinent questions about ML models that transcend traditional quantitative assessments. Attributes such as fairness, transparency, accountability, trustworthiness, and explainability have become critical in applications where the impact of these models is profound. With today's ML models hosting potentially hundreds of billions of parameters, their complexity and the breadth of their application have grown considerably. These developments underscore the urgent need for sophisticated tools to analyze and understand ML models. Visualization, effective in handling large data, offers a pathway for stakeholders to discern and engage with these critical aspects of AI systems, paving the way for enhanced model comprehension and diagnostic capability.
This Research Topic aims to shed light on the intersection of visualization techniques and machine learning improvement. It seeks to gather cutting-edge research and viewpoints from both academic and industrial spheres on how visualization can contextualize and enhance ML applications. Contributions may focus on leveraging visualization for a clearer understanding of ML operations and for augmenting characteristics such as fairness and accountability in AI applications. Through enhanced visual approaches to ML, this research endeavor strives to elevate the trust and efficiency of AI technologies across various sectors.
To advance our understanding in this emerging field, we invite submissions that delve into various themes such as:
- Conceptual frameworks and taxonomies for trust and transparency in ML via visualization.
- Innovative visual and interactive tools to interpret and elucidate ML models.
- Strategies for ML model refinement and deployment supported by visualization techniques.
- Domain-specific applications of visual ML tools addressing unique challenges.
- Interactive mechanisms that guide and document ML processes emphasizing core values like trustworthiness and transparency.
Contributors are encouraged to explore these and other related topics to contribute to the evolving landscape of ML visualization.
Potential authors may contact the Topic Editors with any questions they have relating to this Research Topic, at carla@inf.ufrgs.br .
This Research Topic aims to shed light on the intersection of visualization techniques and machine learning improvement. It seeks to gather cutting-edge research and viewpoints from both academic and industrial spheres on how visualization can contextualize and enhance ML applications. Contributions may focus on leveraging visualization for a clearer understanding of ML operations and for augmenting characteristics such as fairness and accountability in AI applications. Through enhanced visual approaches to ML, this research endeavor strives to elevate the trust and efficiency of AI technologies across various sectors.
To advance our understanding in this emerging field, we invite submissions that delve into various themes such as:
- Conceptual frameworks and taxonomies for trust and transparency in ML via visualization.
- Innovative visual and interactive tools to interpret and elucidate ML models.
- Strategies for ML model refinement and deployment supported by visualization techniques.
- Domain-specific applications of visual ML tools addressing unique challenges.
- Interactive mechanisms that guide and document ML processes emphasizing core values like trustworthiness and transparency.
Contributors are encouraged to explore these and other related topics to contribute to the evolving landscape of ML visualization.
Potential authors may contact the Topic Editors with any questions they have relating to this Research Topic, at carla@inf.ufrgs.br .
Keywords: visualization, artificial intelligence, machine learning, explainability, fairness
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
