ProbeTools: Unconventional cameras and audio devices for user research

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TaskCam: Designing and Testing an Open Tool for Cultural Probes Studies

TaskCams are simple digital cameras intended to serve as a tool for Cultural Probe studies and made available by the Interaction Research Studio via open-source distribution. In conjunction with an associated website, instructions and videos, they represent a novel strategy for disseminating and facilitating a research methodology. At the same time, they provide a myriad of options for customisation and modification, allowing researchers to adopt and adapt them to their needs. In the first part of this paper, the design team describes the rationale and design of the TaskCams and the tactics developed to make them publicly available. In the second part, the story is taken up by designers from the Everyday Design Studio, who assembled their own TaskCams and customised them extensively for a Cultural Probe study they ran for an ongoing project. Rather than discussing the results of their study, we focus on how their experiences reveal some of the issues both in producing and using open-source products such as these. These suggest the potential of TaskCams to support design-led user studies more generally

TaskCam

TaskCams are simple digital cameras designed for studies of users and their contexts. Researchers and practitioners can build their own TaskCams using instructions and videos from www.probetools.net, off-the-shelf parts, and a custom Arduino shield made available from the site. There is a myriad of options for customisation and modification, allowing researchers to adopt and adapt them to their needs. We view the open-source distribution of TaskCams as a novel approach to disseminating a research methodology

Data Probes: Reflecting on Connected Devices with Technology-Mediated Probes

We introduce Data Probes, technology-mediated probes designed to reveal some of the inner workings of connected devices, including common embedded sensors and the data they collect. By making these common features both accessible and unfamiliar, the probes supported research participants in looking at these technologies from a different perspective and reflecting on capabilities and behaviours that may be obscured by the design of commercial products. During a study where participants lived and travelled with the probes for a month, we were able to gain generative design insights into people’s attitudes towards and relationships with connected devices, suggesting new opportunities for designs that take alternative approaches to currently entrenched visions of the Internet of Things. We present this exploratory study as an illustration of how a technology-mediated probe might prompt reflection on their technologies and open up new design spaces

Crafting a Place for Attending to the Things of Design at CHI

Over the past two years, we have organized workshops at the CHI conference that have focused on the “Things of Design Research. The goal of these workshops is simple: to explore and develop a venue at CHI for research through design (RtD) practitioners to materially share their work with each other. RtD often centers on the making of things— artifacts, systems, services, or other knowledge in the interaction-design and human-computer interaction (CHI) research communities. Yet, over the years, we have felt that the things of design research have remained conspicuously overlooked, under-engaged with, and, for the most part, absent from the CHI conference. If RtD is to continue to develop as a research practice in the HCI community—and we want to build a community of designers doing research with and through designed objects—we need more things at CHI

Seeing Through Things:Exploring the Design Space of Privacy-Aware Data-Enabled Objects

Increasing amounts of sensor-augmented research objects have been used in design research. We call these objects Data-Enabled Objects, which can be integrated into daily activities capturing data about people's detailed whereabouts, behaviours, and routines. These objects provide data perspectives on everyday life for contextual design research. However, data-enabled objects are still computational devices with limited privacy awareness and nuanced data sharing. To better design data-enabled objects, we explore privacy design spaces by inviting 18 teams of undergraduate design students to re-design the same type of sensor-enabled home research camera. We developed the Connected Peekaboo Toolkit (CPT) to support the design teams in designing, building, and directly deploying their prototypes in real home studies. We conducted Thematic Analysis to analyze their outcomes which led us to interpret that privacy is not just an obstacle but can be a driver by unfolding an exploration of possible design spaces for data-enabled objects.</p

Towards a probe design framework

© 2019 Association for Computing Machinery. Since their introduction, probes have been widely used in HCI. Despite this, there have not been much reflections and discussions about the design thinking behind their creation and use. There is also a lack of actionable guidance on designing and using probes. This lack may have contributed to some concerns that the method has been misinterpreted and misunderstood. We reviewed HCI literature surrounding probes and found one of the few papers that offers a nascent framework for probe design and use. We used it to guide the design of a collection of probes and reflected on the framework's usefulness. We extend this framework by offering a more useful way of visualizing and working with probe design properties. We also provide further clarity and advice on how others may think and approach the design and use of probes more effectively, especially those turning to probes for the first time

Memory probes:Exploring retrospective user experience through traces of use on cherished objects

Our daily interactions with objects can not only leave traces of use on the objects but also leave memories in our minds. These human traces on objects are potential cues that can trigger our autobiographical memories and connect us to social networks. The first aim of this paper is to demonstrate what might be a suitable method of inquiry into the way materials can enrich dialogues about remembered experiences derived from human traces left on cherished possessions. The second aim is to investigate how the accumulation of human traces on objects influences people’s remembering and usage. The design of our research artifacts, Memory Probes, was situated in relation to three spectra of paired values: (1) the familiarity and strangeness of tool use, (2) the definiteness and ambiguity of data capture, and (3) the objective and subjective reality of interpretation. Our field study revealed a transactive nature between traces of interaction with possessions and memories in the owners’ minds. It also informed us of how gradual and curiosity-driven understanding could become a methodological nuance when we are empathetically engaged in a collaborative way of knowing with other participants. To conclude, several implications for designing products that can participate in our everyday reminiscing and meaning-making are proposed

A Survey on Explainability of Graph Neural Networks

Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, including natural language processing, drug discovery, and recommendation systems. However, combining feature information and combinatorial graph structures has led to complex non-linear GNN models. Consequently, this has increased the challenges of understanding the workings of GNNs and the underlying reasons behind their predictions. To address this, numerous explainability methods have been proposed to shed light on the inner mechanism of the GNNs. Explainable GNNs improve their security and enhance trust in their recommendations. This survey aims to provide a comprehensive overview of the existing explainability techniques for GNNs. We create a novel taxonomy and hierarchy to categorize these methods based on their objective and methodology. We also discuss the strengths, limitations, and application scenarios of each category. Furthermore, we highlight the key evaluation metrics and datasets commonly used to assess the explainability of GNNs. This survey aims to assist researchers and practitioners in understanding the existing landscape of explainability methods, identifying gaps, and fostering further advancements in interpretable graph-based machine learning.Comment: submitted to Bulletin of the IEEE Computer Society Technical Committee on Data Engineerin