Simplications: Why and how we should rethink data of/by/for the people in smart homes and its privacy implications
December 09, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Albrecht Kurze, Alexa Becker
arXiv ID
2412.06960
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
More and more smart devices enter our homes. Often these devices come with a variety of sensors, mostly simple sensors, e.g., for light, temperature, humidity or motion. And they all collect data. While it is data of the home environment it is also data of domestic life in the home. Thus it is data of the people and by the people in the home capturing their presence, arrival and departure, typical domestic activities, bad habits, health status etc. Based on previous as well as ongoing research we know that people are actually able to make sense of simple sensor data and that they will make use of it for their own purposes. Simple sensors, when critically reflected, are often only "simple" in a technical sense. The unreflected design and use of these sensors can easily lead to unintended implications, i.e. for privacy. However, it may not even need a Big Brother or data experts or AI to make the data of these sensors sensitive, e.g., if used for lateral surveillance within families. Often unintended but wicked implications emerge despite good intentions, such as improving efficiency or energy saving through collecting sensor data. Thus sensor data from the home is actually data of/by/for the people in the home. First, we explain how this might have relevance across scales of community of people - not only for the domain of the home but also in broader meaning. Second, we relate our previous as well as ongoing research in the domain of smart homes to this topic.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted