Interpassivity instead of Interactivity? The Uses and Gratifications of Automated Features

Cheng Chen, Sangwook Lee, & S. Shyam Sundar (2022)

Paper presented at the 72nd annual conference of International Communication Association

The rising popularity of automated features, such as autocorrect, autofill, and autoplay, reflects an interesting paradox in digital media use: While people appreciate the interactivity afforded by these media to users, they also seem to derive satisfaction from passively observing interactive actions performed by the system on their behalf. We aim to understand this paradox by drawing on the concept of interpassivity and exploring the primary gratifications people seek in automated features.

Mindlessness in the Mobile Era? A Comparison of Information Processing on Mobile Phones and Personal Computers

Mengqi Liao, Jinping Wang, Cheng Chen, & S. Shyam Sundar (2022)

Paper presented at the 72nd annual conference of International Communication Association

Mobile phones are the most dominant information technology of our times, but do they affect the way we process information? To investigate this question, we conducted two online field experiments by randomly assigning participants to use their mobile phones or computers to process information.

Combating algorithmic bias: Should AI media show and tell to gain user trust?

Cheng Chen & S. Shyam Sundar (2021)

Poster presented at the 2021 ICDS Symposium on Fairness of Machine Learning

To combat perceptions of racial and other biases, it is important for AI systems to be transparent about their algorithms so that users can assess for themselves if there are any potential sources of bias. Given that the nature of training data may be the root cause of algorithmic bias, would it help to show users a snapshot of the labeled data used for algorithmic training? Or, should the interface focus on the features underlying classification?