As part of global climate action, digital technologies are seen as a key enabler of energy efficiency savings. A popular application domain for this work is smart homes. There is a risk, however, that these efficiency gains result inrebound effects, which reduce or even overcompensate the savings. Rebound effects are well-established in economics, but it is less clear whether they also inform smart energy research in other disciplines. In this paper, we ask: to what extent have rebound effects and their underlying mechanisms been considered in computing, HCI and smart home research? To answer this, we conducted a literature mapping drawing on four scientific databases and a SIGCHI corpus. Our results reveal limited consideration of rebound effects and significant opportunities for HCI to advance this topic. We conclude with a taxonomy of actions for HCI to address rebound effects and help determine the viability of energy efficiency projects.

Studies of dataset development in machine learning call for greater attention to the data practices that make model development possible and shape its outcomes. Many argue that the adoption of theory and practices from archives and data curation fields can support greater fairness, accountability, transparency, and more ethical machine learning. In response, this paper examines data practices in machine learning dataset development through the lens of data curation. We evaluate data practices in machine learning as data curation practices. To do so, we develop a framework for evaluating machine learning datasets using data curation concepts and principles through a rubric. Through a mixed-methods analysis of evaluation results for 25 ML datasets, we study the feasibility of data curation principles to be adopted for machine learning data work in practice and explore how data curation is currently performed. We find that researchers in machine learning, which often emphasizes model development, struggle to apply standard data curation principles. Our findings illustrate difficulties at the intersection of these fields, such as evaluating dimensions that have shared terms in both fields but non-shared meanings, a high degree of interpretative flexibility in adapting concepts without prescriptive restrictions, obstacles in limiting the depth of data curation expertise needed to apply the rubric, and challenges in scoping the extent of documentation dataset creators are responsible for. We propose ways to address these challenges and develop an overall framework for evaluation that outlines how data curation concepts and methods can inform machine learning data practices.

Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals’, other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.

Digital sufficiency, an emerging concept from the sustainable computing literature, can inform interface design to better manage and potentially reduce energy consumption in data centers, which is intensifying due to AI and data growth, despite energy efficiency efforts. Since 26% of data center energy consumption stems from cloud storage and servers, this research integrates digital sufficiency with existing HCI guidelines to enable cloud providers to respond to demands for sustainable infrastructure and facilitate user reflection. We conducted an online survey to understand users’ storage needs, awareness of climate impacts, and openness to sustainable storage. Our findings highlight the limited awareness among users of the carbon footprint associated with data centers and a strong demand for more sustainable storage options once they become aware. To empower users to reflect on climate impacts and align storage practices with their personal sustainability goals, we propose interface design recommendations that challenge the status quo.