Over 2014, the Cornell University Legal Information Institute and the Australian National University worked with users of the Cornell LII site in a citizen science project to collect over 43,000 crowdsourced assessments of the readability of legal and other sentences. Readers (“citizen scientists”) on legislative pages of the LII site were asked to rate passages from the United States Code and the Code of Federal Regulations and other texts for readability and other characteristics. They were also asked to provide information about themselves as part of the audience that uses legislation online. The overall aim of the project was to develop empirical insights into characteristics of law that may make it easy or hard to read for the audience that use it. Also, the project aimed to assess machine learning for automatically predicting readability of legal sentences at sentence level. A major focus of this paper is to report results and insights from demographic data collected during the study. Understanding the audience which reads the law is directly relevant to readability - as the relevant question is readable by whom? Who are the citizens for whom “citizen access” might be enhanced? The paper also describes methods used to rank sentences by readability, using the data provided by citizen scientists. Finally, the paper reports initial tests on the viability of machine learning as a means of predicting readability in advance. The exploratory machine learning results reported here will be extended in further work reported in a future paper. The research provides insight into who uses legal rules and how they do so. We draw conclusions as to the current readability of law, as well as the spread of readability among legal rules. The research creates a dataset of legal rules labelled for readability by human judges. As far as we are aware, this research project is the largest ever study of readability of regulatory language and the first research which has applied crowdsourcing to such an investigation.