Future of discovery: artificial scholars and automated collections
Artificial intelligence is increasing in sophistication, ubiquity, and disruptive effect across entire sectors of society and the economy. Students and academics are gaining the ability to discover knowledge in radically different ways, changing expectations of scholarly communication and library services. At the same time, the collection of data and the absence of algorithmic transparency in the operations of machine learning require coordinated responses to risks of surveillance and bias, and control over information accessibility and reliability.
In academic research, we can observe big data trends across disciplines and innovation in the application of artificial intelligence—generally by publishers and venture capital funded start-ups—to scholarly communication. These provide new opportunities for knowledge discovery and creation but raise questions of impartiality in representations of knowledge and enclosure of the information ecosystem by established and emerging entities in the scholarly publishing landscape.
This presentation will provide an overview of big data trends in academic research across disciplines, and machine learning applications to information discovery and knowledge creation–including the effects of socially-influenced discovery environments and the ethics of algorithmic mediation of knowledge infrastructures. Opportunities will be suggested for library service development, including collection development, information literacy, and novel partnerships.