arXiv subjects

Laura Koesten

Publications and source records attributed to Laura Koesten.

25 records · Page 2Linked to original sources

arXiv2023

Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making

We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences. We shortly introduce the importance of explaining ADM systems, give a brief overview of approaches drawing from other disciplines to improve explanations, and present the results of our qualitative task-based study incorporating the "six facets of understanding" framework. We close with questions guiding the discussion of how future studies can leverage an interdisciplinary approach.

cs.HC↗

arXiv2023

On the Impact of Explanations on Understanding of Algorithmic Decision-Making

Ethical principles for algorithms are gaining importance as more and more stakeholders are affected by "high-risk" algorithmic decision-making (ADM) systems. Understanding how these systems work enables stakeholders to make informed decisions and to assess the systems' adherence to ethical values. Explanations are a promising way to create understanding, but current explainable artificial intelligence (XAI) research does not always consider existent theories on how understanding is formed and evaluated. In this work, we aim to contribute to a better understanding of understanding by conducting a qualitative task-based study with 30 participants, including users and affected stakeholders. We use three explanation modalities (textual, dialogue, and interactive) to explain a "high-risk" ADM system to participants and analyse their responses both inductively and deductively, using the "six facets of understanding" framework by Wiggins & McTighe. Our findings indicate that the "six facets" framework is a promising approach to analyse participants' thought processes in understanding, providing categories for both rational and emotional understanding. We further introduce the "dialogue" modality as a valid explanation approach to increase participant engagement and interaction with the "explainer", allowing for more insight into their understanding in the process. Our analysis further suggests that individuality in understanding affects participants' perceptions of algorithmic fairness, demonstrating the interdependence between understanding and ADM assessment that previous studies have outlined. We posit that drawing from theories on learning and understanding like the "six facets" and leveraging explanation modalities can guide XAI research to better suit explanations to learning processes of individuals and consequently enable their assessment of ethical values of ADM systems.

cs.HC↗

arXiv2023

"The main message is that sustainability would help" -- Reflections on takeaway messages of climate change data visualizations

How do different audiences make sense of climate change data visualizations and what do they take away as a main message? To investigate this question, we are building on the results of a previous study, focusing on expert opinions regarding public climate change communication and the role of data visualizations. Hereby, we conducted semi-structured interviews with 17 experts in the fields of climate change, science communication, or data visualization. We also interviewed six lay persons with no professional background in either of these areas. With this analysis, we aim to shed light on how lay audiences arrive at an understanding of climate change data visualizations and what they take away as a main message. For two exemplary data visualizations, we compare their takeaway messages with messages formulated by experts. Through a thematic analysis, we observe differences regarding the included contents, the length and abstraction of messages, and the sensemaking process between and among the participant groups.

cs.HC↗

arXiv2020

Talking datasets: Understanding data sensemaking behaviours

The sharing and reuse of data are seen as critical to solving the most complex problems of today. Despite this potential, relatively little is known about a key step in data reuse: people's behaviours involved in data-centric sensemaking. We aim to address this gap by presenting a mixed-methods study combining in-depth interviews, a think-aloud task and a screen recording analysis with 31 researchers as they summarised and interacted with both familiar and unfamiliar data. We use our findings to identify and detail common activity patterns and necessary data attributes across three clusters of sensemaking activities: inspecting data, engaging with content, and placing data within broader contexts. We conclude by proposing design recommendations for tools and documentation practices which can be used to facilitate sensemaking and subsequent data reuse.

cs.HC↗

arXiv2019

Dataset search: a survey

Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.

cs.DB↗

arXiv2018

Everything you always wanted to know about a dataset: studies in data summarisation

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 a summarisation study, with a lab and a crowdsourcing component with overall 80 data-literate participants, which produced summaries for 25 datasets. In each study we carried out a qualitative analysis to identify key themes and commonly mentioned dataset attributes, which people consider when searching and making sense of data. The results helped us design a template to create more meaningful textual representations of data, alongside guidelines for improving data-search experience overall.

cs.IR↗

arXiv2018

DATA:SEARCH'18 -- Searching Data on the Web

This half day workshop explores challenges in data search, with a particular focus on data on the web. We want to stimulate an interdisciplinary discussion around how to improve the description, discovery, ranking and presentation of structured and semi-structured data, across data formats and domain applications. We welcome contributions describing algorithms and systems, as well as frameworks and studies in human data interaction. The workshop aims to bring together communities interested in making the web of data more discoverable, easier to search and more user friendly.

cs.OH↗