Legal Obligation and Ethical Best Practice: Towards Meaningful Verbal Consent for Voice Assistants
January 19, 2023 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
William Seymour, Mark Cote, Jose Such
arXiv ID
2301.08091
Category
cs.HC: Human-Computer Interaction
Citations
22
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
Abstract
To improve user experience, Alexa now allows users to consent to data sharing via voice rather than directing them to the companion smartphone app. While verbal consent mechanisms for voice assistants (VAs) can increase usability, they can also undermine principles core to informed consent. We conducted a Delphi study with experts from academia, industry, and the public sector on requirements for verbal consent in VAs. Candidate requirements were drawn from the literature, regulations, and research ethics guidelines that participants rated based on their relevance to the consent process, actionability by platforms, and usability by end-users, discussing their reasoning as the study progressed. We highlight key areas of (dis)agreement between experts, deriving recommendations for regulators, skill developers, and VA platforms towards crafting meaningful verbal consent mechanisms. Key themes include approaching permissions according to the user's ability to opt-out, minimising consent decisions, and ensuring platforms follow established consent principles.
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