Team Awarded Grant to Help Digital Humanities Scholars Navigate Legal Issues of Text Data Mining – UC Berkeley Library Update

“We are thrilled to share that the National Endowment for the Humanities (NEH) has awarded a $165,000 grant to a UC Berkeley-led team of legal experts, librarians, and scholars who will help humanities researchers and staff navigate complex legal questions in cutting-edge digital research….

Until now, humanities researchers conducting text data mining have had to navigate a thicket of legal issues without much guidance or assistance. For instance, imagine the researchers needed to scrape content about Egyptian artifacts from online sites or databases, or download videos about Egyptian tomb excavations, in order to conduct their automated analysis. And then imagine the researchers also want to share these content-rich data sets with others to encourage research reproducibility or enable other researchers to query the data sets with new questions. This kind of work can raise issues of copyright, contract, and privacy law, not to mention ethics if there are issues of, say, indigenous knowledge or cultural heritage materials plausibly at risk. Indeed, in a recent study of humanities scholars’ text analysis needs, participants noted that access to and use of copyright-protected texts was a “frequent obstacle” in their ability to select appropriate texts for text data mining. 

Potential legal hurdles do not just deter text data mining research; they also bias it toward particular topics and sources of data. In response to confusion over copyright, website terms of use, and other perceived legal roadblocks, some digital humanities researchers have gravitated to low-friction research questions and texts to avoid decision-making about rights-protected data. They use texts that have entered into the public domain or use materials that have been flexibly licensed through initiatives such as Creative Commons or Open Data Commons. When researchers limit their research to such sources, it is inevitably skewed, leaving important questions unanswered, and rendering resulting findings less broadly applicable. A growing body of research also demonstrates how race, gender, and other biases found in openly available texts have contributed to and exacerbated bias in developing artificial intelligence tools. …