ATG Interviews Marjorie M K Hlava President, Access Innovations By Tom Gilson (Associate Editor, Against the Grain) <gilsont@cofc.edu> and Katina Strauch (Editor, Against the Grain) <kstrauch@comcast.net> ATG: Margie, for those readers who are unfamiliar with Access Innovations, can you tell us what you and your team do as it relates to the information industry? MH: We change search to found. What does that mean? Studies have shown that knowledge workers spend a third of their time looking for information. That information could be in house only or from external sources such as libraries or discovery platforms. And half of that time they spend looking, they do not find what they need. The value of a collection is worthless to patrons, if they cannot find what they need. We solve that challenge by adding conceptually appropriate words from controlled vocabularies. The control is needed so that words with many synonyms or that are ambiguous can be clearly delineated to the user. ATG: What inspired you to start Access Innovations? Was there something about the market for knowledge management and information services in 1978 that made you think, now is the time? Did you ever think that over 40 years later, you would still be on the cutting edge of technological innovation? MH: Databases were just beginning to be built. The combination of newly available budding technology and the clear need for broader distribution seemed like an excellent thing to become involved in. We were searching a few databases but many people were asking how to build ones for their own collections and put them up online for broader access. Some 600 databases later and thousands of engagements have proven that to be a good model. ATG: The Data Harmony (DH) software is at the core of Access Innovation and its services. Your website describes Data Harmony as “a suite of software modules designed to maximize precise, efficient information management and retrieval.” What does that mean exactly? What do these software modules do, and how do they interact? MH: Big question! Solving knowledge workers’ frustrations with finding what they need requires enriching the article of record with additional metadata beyond what is typically found in, for example, book or article metadata from publishers. DH analyzes what the content is about using “explainable AI.” It starts with DH analyzing an existing corpus in order to develop a thesaurus that conceptually describes the content. DH provides for human review. Next, each digital object in the corpus is automatically labeled (tagged) with the most conceptually appropriate thesaurus terms using DH’s M.A.I. module. Another DH module identifies entities (people, places, and things) and other metadata elements. Together, this value-add metadata improves the search and discovery experience. Search results
Against the Grain / February 2022
are more precise meaning there is fewer items retrieved that are not of interest to the searcher. ATG: You also describe Data Harmony as an “artificial intelligence suite that leverages explainable AI for efficient, innovative and precise semantic discovery.” That sounds complicated to the uninitiated. Can you clarify how it works and why it’s important? And more broadly speaking, what is your view of the overall potential of AI as it relates to information management and services? MH: “Explainable AI” isn’t to difficult to get your head around, but getting there is not easy. All AI/ML/ DL platforms split out results — good, bad, indifferent. Explainable AI is the ability to explain how the AI platform generated its output. This can be critical when the AI output is used to make decisions. As mentioned, DH provides a human friendly window into its workings. These GUIs also provide the ability to intervene and correct. Such interventions do not require IT experience. AI is already having an impact on information management and it will only grow in importance. AI based content services such as DH provides “future proofs” of your information assets. It will improve the flow of information from producers to users. It will facilitate “information finding you.” When it is only a Black box, that is you do not know how the presented answer was derived, it is not safe to implement that information. Again, this is already happening and will only get more pervasive and, hopefully, better. ATG: Can you tell us how you developed Data Harmony and how you keep it updated to meet the everchanging needs of the information industry? MH: With the increase in database creation work, we needed something that would aid the editor and make them faster. Our intention has never been to replace the editor or the librarian but rather to aid their work and ensure that their time is spent doing the intellectual things humans do best and automate everything that is repetitive or clerical. Over time we found more things that could be automated. We built it first for our own use in house but over time customers wanted to use it as well. We did not start out to be a software firm. Our most critical users are still our inhouse editorial team and the software grows by responding to user needs rather than trendy industry unicorns. ATG: Margie - you have said that there are way too many “black boxes “ that don’t explain what they do, are not very accurate, and are too expensive. Can you elaborate? MH: This gets back to explainable AI. If you don’t know how it is happening, then you cannot explain the results. Let me answer in context; libraries have huge collections that are
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