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Creative practitioners (like designers, software developers, and architects) have started to employ Generative AI models (GenAI) to produce text, images, and assets comparable to those made by people. While HCI research explores specific GenAI models and ...

Work-nonwork balance is an important aspect of workplace well-being with associations to improved physical and mental health, job performance, and quality of life. However, realizing work-nonwork balance goals is challenging due to competing demands and ...

Goal setting and realization are important but challenging. These challenges can be mitigated through effective application of behavior change realization techniques such as implementation intention and mental contrasting (IIMC). IIMC relies on ...

Explainability is an essential pillar of Responsible AI that calls for equitable and ethical Human-AI interaction. Explanations are essential to hold AI systems and their producers accountable, and can serve as a means to ensure humans’ right to ...

Reflection on one’s personal data can be an effective tool for supporting wellbeing. However, current wellbeing reflection support tools tend to offer a one-size-fits-all approach, ignoring the diversity of people’s wellbeing goals and their agency in ...

Online research is a frequent and important activity people perform on the Internet, yet current support for this task is basic, fragmented and not well integrated into web browser experiences. Guided by sensemaking theory, we present ForSense, a browser ...

Workplace stress-reduction interventions have produced mixed results due to engagement and adherence barriers. Leveraging technology to integrate such interventions into the workday may address these barriers and help mitigate the mental, physical, and ...

How might computing support us in becoming our better, more emotionally resilient selves? We explore this in an interview with the team from Microsoft Research's Human Understanding and Empathy group.

Designing natural language interfaces for querying databases remains an important goal pursued by researchers in natural language processing, databases, and HCI. These systems receive natural language as input, translate it into a formal database query, ...

Online research is a frequent and important activity people perform on the Internet, yet current support for this task is basic, fragmented and not well integrated into web browser experiences. Guided by sensemaking theory, we present ForSense, a ...

Real-world recommender systems often allow users to adjust the presented content through a variety of preference elicitation techniques such as “liking” or interest profiles. These elicitation techniques trade-off time and effort to users with the ...

Machine teaching (MT) is an emerging field that studies non-machine learning (ML) experts incrementally building semantic ML models in efficient ways. While MT focuses on the types of knowledge a human teacher provides a machine learner, not much is ...

Object detection is a key application of machine learning. Currently, these detector models rely on deep networks that offer model builders limited agency over model construction, refinement and maintenance. Human-centered approaches to address these ...

Year-round ultraviolet exposure silently causes skin damage that goes unnoticed until sunburn. Current personal wearables for monitoring UV exposure have not seen significant uptake, which may be attributed to their one-size-fits-all aesthetic or ...

Machine Teaching (MT) is an emerging practice where people, without Machine Learning (ML) expertise, provide rich information beyond labels in order to create ML models. MT promises to lower the barrier of entry to creating ML models by requiring ...

When building a classifier in interactive machine learning (iML), human knowledge about the target class can be a powerful reference to make the classifier robust to unseen items. The main challenge lies in finding unlabeled items that can either help ...

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 ...

Machine learning (ML) promises data-driven insights and solutions for people from all walks of life, but the skill of crafting these solutions is possessed by only a few. Emerging research addresses this issue by creating ML tools that are easy and ...

When building a classifier in interactive machine learning, human knowledge about the target class can be a powerful reference to make the classifier robust to unseen items. The main challenge lies in finding unlabeled items that can either help discover ...