Journal of Data Science

The exploration of whether artificial intelligence (AI) can evolve to possess consciousness is an intensely debated and researched topic within the fields of philosophy, neuroscience, and artificial intelligence. Understanding this complex phenomenon hinges on integrating two complementary perspectives of consciousness: the objective and the subjective. Objective perspectives involve quantifiable measures and observable phenomena, offering a more scientific and empirical approach. This includes the use of neuroimaging technologies such as electrocorticography (ECoG), EEG, and fMRI to study brain activities and patterns. These methods allow for the mapping and understanding of neural representations related to language, visual, acoustic, emotional, and semantic information. However, the objective approach may miss the nuances of personal experience and introspection. On the other hand, subjective perspectives focus on personal experiences, thoughts, and feelings. This introspective view provides insights into the individual nature of consciousness, which cannot be directly measured or observed by others. Yet, the subjective approach is often criticized for its lack of empirical evidence and its reliance on personal interpretation, which may not be universally applicable or reliable. Integrating these two perspectives is essential for a comprehensive understanding of consciousness. By combining objective measures with subjective reports, we can develop a more holistic understanding of the mind.

Law and legal studies has been an exciting new field for data science applications whereas the technological advancement also has profound implications for legal practice. For example, the legal industry has accumulated a rich body of high quality texts, images and other digitised formats, which are ready to be further processed and analysed by data scientists. On the other hand, the increasing popularity of data science has been a genuine challenge to legal practitioners, regulators and even general public and has motivated a long-lasting debate in the academia focusing on issues such as privacy protection and algorithmic discrimination. This paper collects 1236 journal articles involving both law and data science from the platform Web of Science to understand the patterns and trends of this interdisciplinary research field in terms of English journal publications. We find a clear trend of increasing publication volume over time and a strong presence of high-impact law and political science journals. We then use the Latent Dirichlet Allocation (LDA) as a topic modelling method to classify the abstracts into four topics based on the coherence measure. The four topics identified confirm that both challenges and opportunities have been investigated in this interdisciplinary field and help offer directions for future research.

Predictive automation is a pervasive and archetypical example of the digital economy. Studying how Americans evaluate predictive automation is important because it affects corporate and state governance. However, we have relevant questions unanswered. We lack comparisons across use cases using a nationally representative sample. We also have yet to determine what are the key predictors of evaluations of predictive automation. This article uses the American Trends Panel’s 2018 wave ($n=4,594$) to study whether American adults think predictive automation is fair across four use cases: helping credit decisions, assisting parole decisions, filtering job applicants based on interview videos, and assessing job candidates based on resumes. Results from lasso regressions trained with 112 predictors reveal that people’s evaluations of predictive automation align with their views about social media, technology, and politics.