Laura Koesten
Biographic Data
| ID | 7716597 |
|---|---|
| NAME | Laura Koesten |
| GIVEN NAMES | Laura |
| FAMILY NAME | Koesten |
| SIGNATURE | KOESTEN L |
| AFFILIATIONS | University of Vienna |
| ORCID | 0000-0003-4110-1759 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Information that matters
Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system’s decisions, such as domain experts and decision subjects. To address this, we p…
Human-Data Interaction
Shifting focus from static data use to dynamic interaction enables more diverse ways of exploring and conceptualizing how people work with data. Contexts are constantly evolving, and data evolves with them. Capturing the dynamic nature of data processes requires rethinking how to document them
Talking datasets – Understanding data sensemaking behaviours
UX of data
This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. --- Juho Kim and Henriette Cramer, Editors
Everything you always wanted to know about a dataset
Summarising data as text helps people make sense of it. It also improves data discovery, as search algorithms can match this text against keyword queries. In this paper, we explore the characteristics of text summaries of data in order to understand how meaningful summaries look like. We present two complementary studies: a data-search diary study with 69 students, which offers insight into the information needs of people searching for data; and …
Smoking gun
No abstract available
No prominent works on this page.
Everything you always wanted to know about a dataset
Summarising data as text helps people make sense of it. It also improves data discovery, as search algorithms can match this text against keyword queries. In this paper, we explore the characteristics of text summaries of data in order to understand how meaningful summaries look like. We present two complementary studies: a data-search diary study with 69 students, which offers insight into the information needs of people searching for data; and …
Smoking gun
No abstract available
Talking datasets – Understanding data sensemaking behaviours
UX of data
This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. --- Juho Kim and Henriette Cramer, Editors
Information that matters
Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system’s decisions, such as domain experts and decision subjects. To address this, we p…
Human-Data Interaction
Shifting focus from static data use to dynamic interaction enables more diverse ways of exploring and conceptualizing how people work with data. Contexts are constantly evolving, and data evolves with them. Capturing the dynamic nature of data processes requires rethinking how to document them
Computer Science (5 works) · Data science (4 works) · World Wide Web (3 works) · Data Quality and Management (2 works) · Data Visualization and Analytics (2 works) · Ethics and Social Impacts of AI (2 works) · Human–computer interaction (2 works) · Artificial Intelligence in Healthcare and Education (1 work) · Big Data and Business Intelligence (1 work) · Crowdsourcing (1 work)