White Paper

In the early days of search engines, info pros had to play the role of interpreter and guide, helping their users navigate the wilds of the World Wide Web. They designed custom search engines to enhance and focus patrons’ queries, built research guides for frequently requested topics, and developed search tools to access internal resources.

Today, info pros continue to participate in designing new knowledge discovery tools, built around the library ethos of finding the best answer, not just the most popular web site. Advertising-supported search engines not only have a financial incentive to track and monetize users’ activities, but they also tend to de-contextualize information, surfacing a single page from deep within a web site isolated from clues with which to judge its veracity or reliability. Info pros can become involved in designing the next AI-based knowledge discovery tools and, just as they built customized search engines and created LibGuides, info pros can embed their focus on enabling the best information into these new tools.

While AI is often seen in high-end settings such as algorithmic stock trading or computer-aided interpretation of medical images, information centers of any size can find a role to play within their organizations. Public libraries can expand makerspaces to include a virtual reality lab or to offer workshops on machine learning tools. Information centers within organizations can work with their IT departments to identify and curate open-source datasets and text and data mining tools that would be of use to specific user groups.

The University of Rhode Island recently established a new AI Lab within the university library; the lab offers students, faculty, and researchers the opportunity to learn about data mining, robotics, machine learning and related technologies, as well the ethical implications of these technologies. Similar models, scaled to the appropriate size based on the organization, could be implemented in a wide range of library settings. 

Recommendation algorithms can be built into online catalogs or integrated library systems to enhance discovery of library resources. Even small information centers and solo librarians can bring AI concepts and applications to their users. They can start by creating an internal blog or web page with links to AI-related articles and videos relevant to their clients’ interests; there are almost 300 TED Talks alone on artificial intelligence, as well as podcasts, online courses, and other resources for the non-expert. They can host brown bag lunches and virtual meetings on topics that encourage further exploration—“AI For Everyone” or “Machine Learning 101”. They can use a free or low-cost version of IBM Watson Assistant to create a library chatbot as a demonstration of a relatively simple implementation of AI.

Evaluating information sources is a key skill of info pros. Just as they select resources for the library collection and subscribe to the digital content that offers the best ROI for their users, info pros can also work with project groups to select the most appropriate datasets for a machine learning or big data initiative. Their familiarity with information sources and techniques for evaluating the quality of a resource means they can ensure that the data being used accurately reflects the real world it is intended to represent. Info pros look at how the dataset was created and the data collected, where an implicit bias may have crept in, and whether more current datasets are available.

Info pros can see the strategic potential in bringing AI technology to a digital collection. Historical content—census data, historical manuscripts, news articles, and public records, for example—can be analyzed in new ways with text and data mining tools to reveal unrecognized cultural, social or historical trends. Info pros can identify opportunities to enhance existing collections with discovery tools that can add geolocation, identify named entities, and establish dates to identify temporal correlations.

Info pros can also lead the discussion within their organizations regarding user privacy and security issues when implementing an AI project. Now that recordings from household smart speakers such as Amazon Echo are being sought by investigators to help solve murder cases, concern about access to and use of personal data is not merely theoretical. The European Union’s General Data Protection Regulation (GDPR) and privacy regulations being planned and enacted in other regions have raised the awareness of personal data—what is being collected and what is done with the information. Info pros have a unique perspective on the information flow within organizations, and can advocate for the protection of identifiable information within datasets being used in AI applications.