Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ellen Charlesworth
dblp:353/2154
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-0808-204XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 77% Design research and methods · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction
algorithmic transparency |
0.8 | 1 | 2024 | Mind The Gap: Designers and Standards on Algorithmic System Transparency for Users · CHI 2024 |
Design research and methods
responsible design |
0.2 | 1 | 2024 | Mind The Gap: Designers and Standards on Algorithmic System Transparency for Users · CHI 2024 |
Methods — techniques the papers use, named apart from their topics
mixed-method study · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Mind The Gap: Designers and Standards on Algorithmic System Transparency for UsersabstractMany call for algorithmic systems to be more transparent, yet it is often unclear for designers how to do so in practice. Standards are emerging that aim to support designers in building transparent systems, e.g by setting testable transparency levels, but their efficacy in this regard is not yet understood. In this paper, we use the ‘Standard for Transparency of Autonomous Systems’ (IEEE 7001) to explore designers’ understanding of algorithmic system transparency, and the degree to which their perspectives align with the standard’s recommendations. Our mixed-method study reveals participants consider transparency important, difficult to implement, and welcome support. However, despite IEEE 7001’s potential, many did not find its recommendations particularly appropriate. Given the importance and increased attention on transparency, and because standards like this purport to guide system design, our findings reveal the need for ‘bridging the gap’, through (i) raising designers’ awareness about the importance of algorithmic system transparency, alongside (ii) better engagement between stakeholders (i.e. standards bodies, designers, users). We further identify opportunities towards developing transparency best practices, as means to help drive more responsible systems going forward. Bianca G. S. Schor, Chris Norval, Ellen Charlesworth, Jatinder Singh |
CHI | 3 |