Emerging Perspectives in Human-Centered Machine Learning

Ramos, Gonzalo; Suh, Jina; Ghorashi, Soroush; Meek, Christoper; Banks, Richard; Amershi, Saleema; Fiebrink, Rebecca; Smith-Renner, Alison and Bansal, Gagan. 2019. 'Emerging Perspectives in Human-Centered Machine Learning'. In: CHI’19 Extended Abstracts on Human Factors in Computing Systems. Glasgow, United Kingdom 4 - 9 May 2019. [Conference or Workshop Item]
Copy

Current Machine Learning (ML) models can make predictions that are as good as or better than those made by people. The rapid adoption of this technology puts it at the forefront of systems that impact the lives of many, yet the consequences of this adoption are not fully understood. Therefore, work at the intersection of people's needs and ML systems is more relevant than ever. This area of work, dubbed Human-Centered Machine Learning (HCML), re-thinks ML research and systems in terms of human goals. HCML gathers an interdisciplinary group of HCI and ML practitioners, each bringing their unique, yet related perspectives. This one-day workshop is a successor of Gillies et al. (2016) and focuses on recent advancements and emerging areas in HCML. We aim to discuss different perspectives on these areas and articulate a coordinated research agenda for the XXI century.


picture_as_pdf
CHI_2019_HCML_Perspectives_Workshop.pdf
subject
Accepted Version
Available under Creative Commons: Attribution-NonCommercial 3.0

View Download

Atom BibTeX OpenURL ContextObject in Span OpenURL ContextObject Dublin Core Dublin Core MPEG-21 DIDL Data Cite XML EndNote HTML Citation METS MODS RIOXX2 XML Reference Manager Refer ASCII Citation
Export

Downloads