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Modern data is messy and high-dimensional, and it is often not clear a priori what are the right questions to ask. Instead, the analyst typically needs to use the data to search for interesting analyses to perform and hypotheses to test.…
Human-Computer Interaction (HCI) and Computer Supported Collaborative Work (CSCW) have a longstanding tradition of interrogating the values that underlie systems in order to create novel and accessible experiences. In this work, we use a…
Conventional automated decision-support systems often prioritize predictive accuracy, overlooking the complexities of real-world settings where stakeholders' preferences may diverge or conflict. This can lead to outcomes that disadvantage…
Emerging discussions on the responsible government use of algorithmic technologies propose transparency and public participation as key mechanisms for preserving accountability and trust. But in practice, the adoption and use of any…
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.…
Visualization design is often described as the process of solving a well-defined problem by navigating a design space. While existing visualization design models have provided valuable structure and guidance, they tend to foreground…
Both conventional wisdom and empirical evidence suggests that arranging a prior commitment or agreement before an interaction enhances the chance of reaching mutual cooperation. Yet it is not clear what mechanisms can promote the…
The development and deployment of matching procedures that incentivize truthful preference reporting is considered one of the major successes of market design research. In this study, we test the degree to which these procedures succeed in…
The outcome of the explorative data analysis (EDA) phase is vital for successful data analysis. EDA is more effective when the user interacts with the system used to carry out the exploration. In the recently proposed paradigm of iterative…
In the digital health domain, ethical data collection practices are crucial for ensuring the availability of quality datasets that drive medical advancement. Data donation, allowing patients to share their medical data for secondary…
Whether investigating research questions or designing systems, many researchers and designers need to engage users with their personal data. However, it is difficult to successfully design user-facing tools for interacting with personal…
Financial exclusion remains a major barrier to digital public service delivery in resource-constrained and archipelagic nations. Traditional policy evaluations rely on retrospective data, limiting the ex-ante intelligence needed for agile…
Data visualization is becoming an increasingly popular field of design practice. Although many studies have highlighted the knowledge required for effective data visualization design, their focus has largely been on formal knowledge and…
Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory…
Search engines play a crucial role in shaping public discourse by influencing how information is accessed and framed. While prior research has extensively examined various dimensions of search bias -- such as content prioritization,…
Human social dilemmas are often shaped by actions involving uncertain goals and returns that may only be achieved in the future. Climate action, voluntary vaccination and other prospective choices stand as paramount examples of this…
As humans perceive and actively engage with the world, we adjust our decisions in response to shifting group dynamics and are influenced by social interactions. This study aims to identify which aspects of interaction affect…
In recent years, the games industry has made a major move towards data-driven development, using data analytics and player modeling to inform design decisions. Data-driven techniques are beneficial as they allow for the study of player…
Many Artificial Intelligence systems depend on the agent's updating its beliefs about the world on the basis of experience. Experiments constitute one type of experience, so scientific methodology offers a natural environment for examining…
Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately. Representation Bias in data can happen due to various reasons ranging from…