{"id":38966,"date":"2026-08-21T15:27:30","date_gmt":"2026-08-21T15:27:30","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/understanding-how-humans-inject-knowledge-into-machine-learning-workflows-through-visual-analytics\/"},"modified":"2026-09-17T11:16:48","modified_gmt":"2026-09-17T11:16:48","slug":"understanding-how-humans-inject-knowledge-into-machine-learning-workflows-through-visual-analytics","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/understanding-how-humans-inject-knowledge-into-machine-learning-workflows-through-visual-analytics\/","title":{"rendered":"Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics"},"content":{"rendered":"<p>Visual analytics ({VA}) plays an increasingly important role in supporting machine learning ({ML}) workflows. In the field of visualization, such approaches and techniques are referred to as {VIS}4ML. While {ML} models are mostly learned automatically, the corresponding {ML} workflows receive a variety of human inputs, such as data labelling, feature engineering, model architecture designing, hyper-parameter tuning, and so on. In this work, we surveyed over 200 {VIS}4ML papers to gain an understanding of how humans inject their knowledge into {ML} workflows through interactive visualization. We collected a corpus of {VIS}4ML papers from the {IEEE} {VIS} conferences in the past decade. We developed a coding scheme to facilitate the literature research from four perspectives: characteristics of {ML}, visualization, interaction, and actions. The analysis of the coded dataset allows us to observe different pathways that transfer human knowledge to {ML} workflows via interactive visualization. Building on the analysis, we explain the phenomena of {VIS}4ML using the conceptual model that views {VA} as model building and the information-theoretic cost-benefit analysis that reasons {VA} as for optimizing {ML} workflows. This work provides unequivocal evidence showing the merits of using {VA} in {ML} workflows. The full list of surveyed papers, along with all analysis results and figures, is available at https:\/\/vis4ml4hd.github.io\/ml-knowledge-inject-va\/.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Visual analytics ({VA}) plays an increasingly important role in supporting machine learning ({ML}) workflows. In the field of visualization, such approaches and techniques are referred to as {VIS}4ML. While {ML} models are mostly learned automatically, the corresponding {ML} workflows receive a variety of human inputs, such as data labelling, feature engineering, model architecture designing, hyper-parameter tuning, and so on. In this work, we surveyed over 200 {VIS}4ML papers to gain [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-38966","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/38966","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/14"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/38966\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=38966"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=38966"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}