Foregrounding Bystanders as Stakeholders in Smart Home Product Design
As computing advances, we are faced with tough decisions like how to balance individual privacy with the potential for innovation. People are often uncomfortable with how data is collected and used, yet we continue to see new data-driven technologies deployed. The oft-touted approach of transparency and control has not been an effective solution to individual privacy. People are ill-equipped to decipher how systems work, so cannot effectively use tools intended to put them in control. And as technology expands beyond devices for individuals, privacy expands beyond individual choice. The privacy choices of individuals – constrained by the choices of the people who manufacture their devices – often affect the privacy of those around them (bystanders). This issue is becoming rapidly more urgent with the expansion of the Internet of Things (IoT), from smart-home devices to public surveillance.
In this collaborative project with University of Washington, ICSI researchers are focusing on how the growth of the IoT is affecting the privacy of bystanders – not just the privacy of those who deliberately deploy the devices – and how those effects can be mitigated in product development. Based on findings from case studies, they will design and evaluate the prototypes of system controls for protecting privacy and balancing the interests of bystanders, secondary, and primary users. They will also study IoT product teams to understand their perspectives on privacy, including their beliefs about users’ and bystanders’ privacy expectations, and how they currently make decisions about data use, both for the direct customer and for people who aren’t the direct customer (if they consider the latter at all). Based on their findings, they will will develop and test experimental interventions for product teams to increase empathy to bystanders’ concerns, inform about mitigation strategies, provide practical rubrics and tools, and motivate to implement them.
This work is funded by the National Science Foundation grant #2114229.