Consciousness in Artificial Intelligence: Insights from the Science of Consciousness

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.

Automated clustering of COVID-19 anti-vaccine discourse on Twitter

Attitudes about vaccination have become more polarized; it is common to see vaccine disinformation and fringe conspiracy theories online. An observational study of Twitter vaccine discourse is found in Ojea Quintana et al. (2021): the authors analyzed approximately six months' of Twitter discourse -- 1.3 million original tweets and 18 million retweets between December 2019 and June 2020, ranging from before to after the establishment of Covid-19 as a pandemic. This work expands upon Ojea Quintana et al. (2021) with two main contributions from data science. First, based on the authors' initial network clustering and qualitative analysis techniques, we are able to clearly demarcate and visualize the language patterns used in discourse by Antivaxxers (anti-vaccination campaigners and vaccine deniers) versus other clusters (collectively, Others). Second, using the characteristics of Antivaxxers' tweets, we develop text classifiers to determine the likelihood a given user is employing anti-vaccination language, ultimately contributing to an early-warning mechanism to improve the health of our epistemic environment and bolster (and not hinder) public health initiatives.