How individuals can shape AI through data - An AI literacy and morality perspective

Today’s performance of artificial intelligence (AI) heavily depends on its training data, for which the donation of data by users is an important criterion. However, it is still difficult for users to anticipate how the quantity and quality of training data may affect them. Thus, users face challenges choosing between giving data to companies or keeping it confidential. That is, foregoing their privacy rights in favor of the greater good , i.e., better AI systems not only for themselves but for everyone. In this paper, we provide a conceptual understanding paired with empirical evidence on the impact of donating data of different quality on the AI system\u27s performance. We focus on two common data: medical data and data from entertainment. Furthermore, we discuss ethical concerns within this context. This work is not normative; rather, it empowers people to reflect on their moral beliefs and understand their impact on AI

Geo-Targeting, Privacy, and the Rise of Consumer Location Trajectories

Consumer location tracking is becoming omnipresent on mobile devices, producing vast volumes of behavior-rich location trajectory data. However, this comes at a cost – potential invasion of consumer privacy. Existing approaches to privacy preservation are largely unsuited for these new data, not personalized, and difficult to interpret the trade-off between consumer privacy and data utility. We propose a personalized and interpretable framework to enable location data collectors to optimize the privacy-utility trade-off. Validating on a sample of nearly one million location trajectories from over 40,000 individuals, we find that high privacy risks indeed prevail in the absence of data obfuscation. Outperforming multiple baselines, the proposed framework significantly reduces the privacy risk with minimal decrease in an advertiser’s utility. As novel and powerful consumer location trajectory data become increasingly leveraged, we demonstrate its value and propose an interpretable framework to mitigate its risk while maximizing its value