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Shiva Mayahi

dblp:334/4115 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
0000-0003-3690-7424ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Usability and user experience research · 77% Human-AI interaction · 23%
Network and information security
1 paper
Privacy and data protection · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Privacy and data protection
privacy compliance
1.012026
Tinker, Tailor, Trust: How Developers Create Privacy Policies With and Without AI · CHI 2026

Methods — techniques the papers use, named apart from their topics

observation study · 2.0interview study · 2.0
YearPublicationVenuePosition
2026 Tinker, Tailor, Trust: How Developers Create Privacy Policies With and Without AI
abstract
For mobile developers to comply with privacy regulations, they must create privacy policies that accurately describe their apps’ data practices. This requires a complete understanding of their apps’ behaviors, including those of embedded third-party SDKs. Despite the complexity of this process, little is known about how privacy policies are created and validated. To investigate, we interviewed 20 developers from around the world about their processes, also observing them use a large language model (LLM) to prepare privacy policies for their apps. We found that developers struggle with collecting information about third-party SDKs, even when they use LLMs, and feel uncertain about the legal validity of LLM outputs. Many developers do not seek legal assistance and believe that, as long as app stores accept their privacy policies, they are protected. Our findings suggest that reliance on LLMs and developers’ desire to externalize validation may result in increasingly unreliable privacy policies.
Shiva Mayahi, Noura Alomar, Nathan Malkin
CHI1