Towards Dynamic Transparency: The AppTrans (Transparency for Android Applications) Project

Austin, Lisa M.; Lie, David; Sun, Peter Yi Ping; Spillette, Robin; D'Angelo, Mariana; Wong, Michelle Y.

doi:10.2139/ssrn.3203601

Cited by 3 publications

(2 citation statements)

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“…Just as with text and images, data about data collection could become actionable knowledge in the hands of consumers, who could either take an individual initiative or, thanks to AI, even collective action. Some research (Austin et al, 2018) suggests that the source code of apps installed on a smartphone could be reverse-engineered, which would enable uncovering with whom certain data is shared. Arguably, this scenario is the most challenging one, not only for the AI technologies involved, but also form a system's viewpoint, because data collected from a user device could be shared via other nodes of the network.…”

Section: Code Analysismentioning

confidence: 99%

“…Regarding the analysis of software compliance, the Mobile App Privacy System (MAPS) tool (Story et al, 2019) and the AppTrans system (Austin et al, 2018) make use of machine learning techniques to check whether the implemented data practices comply with the declared policies. The key idea behind these approaches is to automatically analyze the data flow of the binary code of an app, comparing the information regarding the data actually shared with the information extracted from the privacy policies using natural language processing.…”

Section: Current State Of the Artmentioning

confidence: 99%

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The Force Awakens: Artificial Intelligence for Consumer Law

Lippi

Contissa²,

Jabłonowska³

et al. 2020

jair

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Recent years have been tainted by market practices that continuously expose us, as consumers, to new risks and threats. We have become accustomed, and sometimes even resigned, to businesses monitoring our activities, examining our data, and even meddling with our choices. Artificial Intelligence (AI) is often depicted as a weapon in the hands of businesses and blamed for allowing this to happen. In this paper, we envision a paradigm shift, where AI technologies are brought to the side of consumers and their organizations, with the aim of building an efficient and effective counter-power. AI-powered tools can support a massive-scale automated analysis of textual and audiovisual data, as well as code, for the benefit of consumers and their organizations. This in turn can lead to a better oversight of business activities, help consumers exercise their rights, and enable the civil society to mitigate information overload. We discuss the societal, political, and technological challenges that stand before that vision.

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Section: Code Analysismentioning

confidence: 99%

Section: Current State Of the Artmentioning

confidence: 99%