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Angela I. Fritz, "AI and Digital Leadership: Transforming Libraries, Archives, and Museums for the Future" (Bloomsbury, 2026)
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Welcome to the New Books Network welcome to the New Books Network. I'm your host, Michael Lamagna. In recent years, artificial intelligence has come to dominate discussions in library and information science as well as in society at large, with particular attention to how it is being implement and what the broader implications may be. For professionals working in galleries, libraries, archives and museums, it is essential to develop a critical framework that addresses ethical AI use, digital stewardship, workforce development, and institutional advocacy. Today, I'm joined by the author of AI and Digital Transforming Libraries, Archives, and Museums for the Future, published by Bloomsbury in 2026. Angela Fritz is an assistant professor at the School of Library and Information Science at the University of Iowa. Welcome to the podcast, Angela hi Michael.
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Thanks so much for having me.
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C
So before we dive into your book AI in the Digital and Digital Leadership. Could you share a little bit about yourself, your background and your career path?
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B
Absolutely. Michael, as you mentioned, I'm currently Assistant professor at the School of Library and Information Science at the University of Iowa, and I teach classes on archives, digital Preservation and AI in support of solisa's Special Collections and Archives Certificate. In terms of my own educational background, I have a BA from the University of Iowa in History and Political Science. I have an MLS from the University of Wisconsin Madison with an Archival Administration Specialist. At the time that I went to the University of Madison, they had amazing certificate program with the Wisconsin Historical Society. And so I was able to go through that program and get that amazing training. I then went on for an MA in History at the University of Wisconsin, Milwaukee and then would later return to receive my PhD. I was offered a Crown Fellowship by Loyola University in Chicago. So I went back to get my PhD in their dual program in American History and Public History. My career path really encompasses working in a range of different glam institutions. And that career path spans over 25 years. And so during that time I held leadership positions at the Wisconsin Historical Society, the University of Notre Dame, and my first position at a grad school was with the Office of Presidential Libraries and Museums at the National Archives. The commonalities of all of these positions have really been related to building digital stewardship programs. Not only building digital stewardship programs, but understanding how to sustain them and how to grow them over time. And so my current research at the University of Iowa really is informed by that professional experience. And I have a deep interest in glam digital convergence and integrated collections management, which was really the subject of my first book, Sustainable Enterprise Strategies for Optimizing Digital Stewardship. I know it's a mouthful, but it really focuses on integrative collections management across libraries, archives and museums. My current research really explores ethical AI with a specific focus on emerging frameworks that balance and ethics of care with AI enhanced stewardship practices across the glam sector.
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C
Oh, that's excellent. So we can definitely see what sparked your interest in this topic and ultimately led to this book. So, as we begin our conversation, how do you define digital leadership within the context of galleries, libraries, archives and museums? And what core competencies distinguishes effective digital leadership in these settings?
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B
This is an excellent question. It's really the focus of my book. I feel like I could fill the entire hour by talking about this, this one question, but I'll try to contain my answer. Do you think it's important to go to start a little bit with historical context to Explain what digital leadership is, the emergence of digital leadership, and how this initial inception of digital leadership in the 1990s is really different than what I'm talking about in terms of digital leadership in a glam context in the age of a. But to go kind of back in history, really, the framework for digital leadership really emerged in the 1990s. The concept of digital leadership emerged with the increased pace of technological change. And so the early framework for digital leadership really emerged within it, healthcare and a variety of different businesses. Digital leadership in this context really addressed the need for flexible and a distributed staff with greater expectations for timely decision making relating to the adoption and implementation of emerging technologies. So early digital leadership frameworks in these contexts really emphasized foresight, planning, agility, adaptability as the technological landscape changed. So when we think of the 1990s conception of digital leadership, it really focused on really three areas. The first area, modernization, how to modernize organizations. And digital leaders were seen as individuals who could bring in these key innovations to modernize organizations by adopting emerging technologies technologies. The modernization though, was not enough. It was really to modernize organizations for digital transformation. And so digital leaders kind of second component of digital leadership, the idea, the framework for digital leadership was really about the ability to guide organizations towards digital transformation by building digital capacity primarily through areas of organizational and staff development. The third area was really focused on digital leaders who could foster all of the basic characteristics that are required to transform a digitally literate workforce. During this time, we start to hear about digital literacy and digital skill building and the importance of how that is really connected to organizational development and leadership capacities in this area. In general, digital leadership is really about proactive implementation of emerging technologies rather than reactive. The irony in this, and I talk about this a lot in my book, is that during in the 1990s, GLAMs, libraries, archives and museums are really disconnected from this digital leadership framework. And I talk a lot about how since the 1990s, libraries, archives and museums, in terms of technological change, has really outpaced their own organizational development, how they organize their work, how they implement emerging technologies. This doesn't mean that libraries, archives and museums were not implementing emerging technologies. They were, they were doing this in the 1990s, but they were doing it in a way where they really weren't considering the implications for the staff and the sustainable business planning relating to sort of a digital society and digital transformation. I talk a lot about early digital libraries in the 1990s as being a really good example of this, right? The idea that the first digital libraries that emerged in the Cultural heritage sector were really fractured. There was a lack of clear vision. They weren't really interoperable or scalable to a larger infrastructure. They were missing some important functionality, stewardship functionality in their systems, particularly digital preservation. I also talk about how during COVID 19, this really served as a watershed moment for our field to re envision and rethink about leadership in a post Digital Society. With COVID 19 for both administrators and practitioners, it really required us to rethink about our competencies like responsibilities for leadership during that time. We're just starting to beginning to see post Covid surveys that indicate that practitioners were able to accomplish extraordinary work. But they also experienced highs and lows when it came to leadership and the value of their specific work. And so Covid really necessitated the field to re evaluate leadership frameworks with a specific focus again on digital transformation. So hearkening back to those early characteristics of digital leaders in the 1990s, modernization, digital transformation and fostering a digital literate staff. So in that context, digital leadership for glams when we talk about specific competencies, I've picked like three to talk about today, but the first is really, it's a bit different from the 1990s. The first competency for digital leadership framework in glams is really the ability to normalize the digital. When we say this, we think this sounds crazy. In 2026, of course the digital is normalized, right? We live in a digital society. But it's not always the case with libraries, archives and museums, particularly when we think about our organizational structure, how we organize our work, how do gleams really, how do digital leaders do this? Normalize the digital in 2026, really focusing on organizational development, which I think we'll probably talk about later on in, in the session. Also understanding that libraries, archives and museums are dynamic networked environments, right? And this is not to say that our physical environments are not important. Our physical environments will always be important, you know, in terms of. For individuals to come and engage with distinctive collections. But really, digital leaders understand that, that it's important to frame organizations around distributed network environments because. Because we're providing, digitizing more of our collections, providing them and providing them access to global audiences. And so digital leaders really understand that and recognize the interdependent nature between people, technology and distinctive collections in libraries, archives and museums. Moreover, they understand the responsibility of digital stewardship really involves a network of people who are not only collaborative, but they're also. They have interrelated and interdependent roles. The final component, really the characteristic of a Digital leader in glam is really the ability to navigate successive waves of technological change. So it used to be in the 1990s when you took a leadership development class, it was all about change management and you learned about how to manage change in 2026. We know that that's just not. It's not sustainable to think that you can actually manage change. So digital leadership is structured really about being change ready and really being able to lead institutions through continuous change by empowering them in many ways. And really briefly, I wanted to just discuss. It's not just the technical kind of components of digital leadership, but also digital leadership is very much a people first centered framework. It's really about people first skills and soft skills to be able to build relationships and make specific connections. And we really learned this again from COVID 19, that leadership is just not about innovative ideas and increased productivity and introducing technology in that context, but it's really often measured by what happens behind the scenes, how digital leaders interact, think, and extend care in challenging times that are marked by digital disruption. And this is going to be important as ever in the age of AI. How do they do this? They do this by prioritizing human centered stewardship, which is their big focus on my book, but also understanding the fragility of trust and really working to safeguard trust and understanding that leaders in the future for any library, archive and museum will have to maintain trust on multiple fronts. And so the challenge is that with the accelerated development of AI, a host of complexities come into question about how glams will maintain specific trust, because there is a trust gap not only with practitioners and really how they will use and adopt and implement AI in their work, but also with the public. So digital leadership is really about navigating sort of that trust gap or trying to navigate ways to bridge that trust gap. So with that said, I do have a definition of digital leadership in glams, and that definition is the ability to embrace the possibilities of AI while advocating for the human care required for the stewardship of cultural heritage collections. Digital leaders in glams frame the adoption of AI in ways that augment or enhance creativity, care, and expertise that only practitioners can bring to their work. They do this with a sense of intentionality. They lead with ethical AI.
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C
Oh, I love that. I really appreciate you bringing that historical information into this discussion and offering that solid definition to kind of frame what we're going to discuss during our time together. And like I said, I think that historical information is so important. And so as we think about glam institutions, how Has AI been implemented historically? And what lessons can be drawn from these early deployments regarding both opportunities, but also limitations?
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B
This is another excellent question, and I think it's something that is oftentimes missing from the conversation. This is also something that I'm reminded with my teaching in terms of when my students come into class in the beginning of the semester and we talk about AI, they always want to frame the question about if we should use AI, if we should use it. No, we should. No. Yes, we should. And I feel that my responsibility as instructor, as a professor, is to really reframe that conversation and get them to see that we actually have been using AI for decades. So the question is not if we should use AI, it's really about how to implement AI and to really underscore the important role that they're going to have in their future careers in navigating that question. So AI in the historical context of GLAM is really, really important. Of course, GLAMs have been experimenting with early iterations of AI for decades. The earliest implementations were really traditional or applied AI in the context of libraries, archives and museums. And this was largely based around projects relating to data analysis or predictive decision making. Most often these predictive AI models were integrated into existing public services, right? Early chatbots emerged at universities, or there are iterations of predictive AI or traditional AI being used in creating really interesting interpretive experiences at cultural heritage institutions. And of course, we've used AI applications, machine learning applications, in born digital curation workflows for decades. And this is an area where we find particularly good use of machine learning applications to help us process and manage large data sets, particularly using applications that help us identify PII in large data sets, using specific transcription tools for oral histories, certain tools that help us with photo labeling or photo identification. For decades we've used optical character recognition that has enhanced full text searching for our digitized collections. And so the scope and scale of these implementations in the earlier context in our history were really small and really framed by very specific institutional goals or projects. The early implementations of AI and GLAM took place under supervised learning of curators, special collection librarians, and museum curators and archivists who really supplied the high quality training data based on their own subject expertise, their professional knowledge base, and their own collection metadata. And so predictive analytics and early traditional AI tools have been around for decades. Libraries, archives and museums have a rich history of exploring AI and ML tools. What is different though, in our current context is that the current context is really marked by accelerated commercial Development, which has surface tensions and anxieties and resistance to AI adoption among practitioners across glams. And so very different from those early iterations where practitioners were involved in the implementation and the assessment and even the development of the AI tools. Oftentimes it seems that practitioners are left out of the loop with these development, with the development of AI tools and the implementation. And so digital leaders really need to think about how to change that kind of paradigm, really to think about how they can again practice ethical AI by aligning it with responsible stewardship practices by those that are closest to the work. So when we think about your question about opportunities and limitations, I think those are really important questions to keep in mind because we have always had those same opportunities and limitations with our AI deployment, right. For decades. They're not really any. The, the actual limitations are not any different, the scope and scale different, but the opportunities, I think it's really important to acknowledge those opportunities, particularly as it relates to how these tools have enhanced search and discoverability of our distinctive collections, how these tools have ushered in the collections as data movement. And with that new digital humanities methodologies that have really changed our approach to understanding distinctive collections, print based collections, how they can be used, the creation of new knowledge, but also efficiencies and workflows. Practitioners have directly benefited from AI and ML applications in the past, particularly as it relates to born digital curation. And it's something that is like for me personally, I feel there is the greatest potential and opportunity in the development and implication and implementation of AI tools with sort of curator or really enhancing the tools that we already use. But the limitations I think also are really important. And I think digital leaders really understand these limitations that are able to articulate these limitations in a way that aren't necessarily barriers, but leave an opportunity open for possibility. Right? Understanding where there are areas of improvement and so output quality, accuracy of tools have already been always been a limitation. The legal, ethical and privacy barriers and considerations have been a limitation. I think there are new considerations though, particularly that haven't really been addressed in a lot of the discussions currently. And that is the resource and skill constraints in really implementing AI at scale. And so digital leaders, I think in a glimpse setting, really understand these resources and skill constraints and can demonstrate or advocate for the substantive investment that needs to occur with both human and technological resources as we think about implementation of these new technologies into our ecosystems.
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C
I'm so glad that you reframed those conversations with your students to really have them think about scope and scale. I think that's such an excellent point that we've been using this technology and that we just need to kind of reframe the way we think, think about it. So what an excellent point. Now, as we think about the current landscape which you've mentioned, how should digital leaders approach information governance, particularly in relation to AI ethics, accountability and responsible data stewardship?
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B
This is another excellent question, particularly as it relates to the heightened visibility of information governance. I think one of the great things that has come out of the conversations about AI and implementation, how institutions are going to implement it, how they're going to adopt it, what that path might look like. The focus on information governance has been really interesting because I think a lot of individuals benefit from information governance at their institutions, but they might not always know what it is or that it exists. So it's a really great opportunity to kind of shine some light on the importance of information governance. Of course, digital leadership and information governance are extricably linked to, for those of individuals that might not have an information governance committee or board where they work just to quickly define what information governance is, it really helps to ensure that individuals within organizations understand their roles and responsibilities relating to the data that they create, that they use, that they collect and they preserve. And so how do institutions do this? They establish formal information governance boards or committees or advisory boards that help to implement a series of interrelated policies and procedures. They make procurement decisions on tools that can help build out infrastructures, and they can help to solidify support and kind of unifying decision making around the deployment and the sustainability of their information technology. Information governance can play, it should play a big role in assessing and implementation and implementing AI for GLAMs. And some of the ways that information governance can do this is by really embedding AI ethics as a core governance principle. And one of the things that I'm slightly concerned about is with the emergence of AI, individual institutions will think that they have to create separate boards or separate policies, rather aligning sort of AI implementation and adoption with what they have currently in place, particularly with information governance is really the prime area institution, entity to really be able to manage some of those conversations and create those policies. So it's really an opportunity, information governance is an opportunity to really embed AI ethics into what our core principles and values are. It also can help us prioritize accountability through clear policy and standards and oversight at an institutional level. At a very high level, it can help us strengthen responsible data stewardship practices that are already in place. So how does AI align with our current understanding of Responsible data stewardship. Of course, managing legal, privacy and rights based considerations proactively is a benefit to aligning AI implementation with information governance. And then really good information governance entities, whether it be a board or a committee, they're also able to provide some type of support for the institutional capacity through training as well, so providing some very high level training opportunities. I do think it's really important to note that information governance in itself is not enough though for ethical AI for responsible stewardship. Aligning AI with responsible stewardship we also need informal paths to develop inclusive guidelines and practices. Because oftentimes information governance is top down in an institution, oftentimes information governance is situated in it, in centralized it, so it might not be as inclusive and provide that sort of provide avenues for feedback. And so in addition to information governance, the importance of things like value statements or a statement of practice, or thinking about ways to practice transparency in the work of GLEAM stewardship all help to make AI explainable and understandable and more and inclusive really across any sort of institution. And I see this actually happening in my own conversations with communities with GLEAM institutions who are developing more informal communities of practice to think about AI and AI adoption in their own institution. Some more formalized advisory groups like within their specific division that really focuses on the stewardship role that they have and how they might integrate AI assisted workflows in their own kind of work responsibilities in their own specific context.
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C
Yeah, I really like that you made the point to say there's no need to create an additional governance board, that this can be fit within the existing structures. Now, as we think many institutions have attempted to integrate emerging technologies by simply redistributing responsibilities within the existing organizational structure. And why do such approaches often fall short? And what alternative organizational models might better support sustained digital transformations?
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B
This is really at the heart of my book in terms of really thinking about the connection between digital leadership in glams and really thinking about sort of new organizational or alternative organizational models for GLAMs. And again, history is a really important, important component of the, of the importance of understanding the power of organizational development. How we organize our work directly affects how we integrate technology into our work. Right? And we always, we haven't always made that connection. And this is because most GLAMs have operated under organizational structures that have really been in place since the mid 20th century that are really hierarchical based. They're really structured around managing print and physical materials. And over time institutions have, instead of really rethinking that organizational structure itself, have just kind of made integrated or introduced Digital services within that hierarchical structure historically gleam institutions really have also organized their work around a curatorial based structure that really situates and fortifies institutional knowledge of their collections, be it analog or digital, within one division or one department, and most often with one person. So when that one person, when that curator leaves, there can be a continuity of services crisis. Right? That person knew everything. And now institutions really can't navigate basic, even research support for those collections. And the irony in all of this is that there's a large consensus across our field that we're managing more digital collections and providing more services, digital services, than ever before. And so to really accommodate that, glams have really segregated their digital services that have been separate from their analog or their print based or their core, you know, their traditional stewardship services. And as a result, that organizational structure in itself has created barriers for us in terms of cross training, in terms of us really understanding the centrality of digital services, of emerging technologies in our work. And so I really, part of my book is really helping us or putting forward an idea about an organizational structure that might help us think differently about how we organize our work. And that is really this idea of course, services of having specific core stewardship services that include acquisitions processing, metadata management and preservation. A unified concept of preservation that are all centralized within an organization, that are all working very integratively and not bound by necessarily department or unit lines, but have the freedom to be able to make decisions and to think more integratively about their work. What does this have to do with AI in terms of this organizational structure? So I suggest that by centralizing all of these functional components of our collection stewardship work, GLAM practitioners can better work together to assess and implement AI more effectively in the context of our work. And so we often think about humans in the loop as a, as a way to think about, to prioritize human oversight for AI implementation and adoption. And sort of restructuring our work around core services allows us to facilitate those conversations and really to empower practitioners to make those decisions. And those are the individuals that should be making the decisions because they are closest to the work. I will say that I think that without this reorganization, I think the age of AI is going to be very different for glam institutions. I think that it's paramount that we really think differently about our challenges in the context of how we just actually engage in our work. If we're not creating organizational structures that help us build out AI literacy in ways that empower practitioners, it's going to be harder for us to see the impact and the usefulness of AI and machine learning tools. In addition, GLAMs run the risk of losing agency and really being able to assess and make decisions and advocate for their work, including how to articulate the limitations of AI as well as leverage AI to optimize their work. If we don't think about how we organize our work to.
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C
Yeah, that's such a great point. So you've kind of mentioned this, but so in your book you introduce the concept of a core stewardship services model. So what is this model and how does this framework reposition institutional capacities and workflows to more effectively support AI integration into that glam context?
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B
So the, the idea of core services is really a, a different way to think about how we integrate with iterative workflows sometimes. I've worked in institutions where creating a workflow to digitize a PDF can be like a year long project. And so this reorganization of core services is this idea of bringing individuals together, practitioners that engage in the work so they have more, they're empowered, they have more, more agency and authority in being able to think about how they will integrate these emerging technologies in their work and really do this in a very unified in a sense. Another example of like how this has really why this is so important. If we just look at digital preservation, for example, when we think about in the past decade how digital preservation has been such a conundrum for glam institutions, like they really don't know situationally, like organizationally where digital preservation should be situated, like where should it live in an organization. And so when I've worked in institutions, digital preservation has been located in like a distinctive collections division. It has been located in like a digital services unit. Sometimes it's in analog preservation, sometimes it's like a strategic position in the C suite, sometimes it's located outside of the library in an IT department. And so this idea, not to say that digital stewardship should be in one place, but this idea of just this consternation of something that's so central to digital curation, not having a sense of like how we should leverage this position or this function to help us think in much more of a digital forward way. You know, I think that's a really, really good example. So core suit is core stewardship services. Bring all of these stewardship services together. And so I'm, for example, I'm a huge proponent of integrating, unifying preservation. Right. Physical preservation and digital preservation are really have a shared philosophy in many ways. So they should really, really be unified in core services. To be able to, to support one another in that larger philosophy. So why is this again important for AI development implementation, assessment? It facilitates human in the loop human oversight for all AI assessment and deployment. It helps us think and make the case for alignment with AI deployment and responsible stewardship practices and standards that are already in place. It helps us continue to facilitate iterative workflows by really integrating AI into our workflows in a context of AI assisted workflows. Right. And so that hopefully will help mitigate some of the anxiety if individuals are more involved in making and empowered to make those decisions. Also, looking to the future, this idea of core services really support a more unified approach to the acquisition of AI generated content, which is going to be a very large issue challenge for libraries, archives and museums. With an AI value statement in place, practitioners will be positioned to appraise these collections against existing policies and guidelines and best practices, but also implement new tools in terms of on the horizon AI detection tools, how we're going to actually start navigating and, and making decisions about if we're going to acquire AI generated content. And so having those core services will help us have more constructive conversations, but also will help us align those decisions with responsible stewardship practices. I think it's really important to reiterate that organizational development is so critical to addressing a whole host of questions relating to AI deployment deployment in terms of asking questions like what are the desired outcomes, like why are we implementing this specific application, what interests are prioritized in this AI deployment and what risks are acceptable? And instead of having a top down approach to these questions, core services really centralize those conversations with practitioners.
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C
So you've mentioned this human in the loop or this human first kind of approach, this people first human centered framework. How do we get digital leaders to kind of implement this framework in how they work within their organization?
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B
A really important component to my book and to this concept of digital leadership in glam is this idea that organizational development and staff training are mutually reinforcing. Right? You can't really have one without the other. And so it's not just only how we structure our work, but it's also how we empower work to, or how we empower people to engage in that work. And I talk, I really contextualize this in my book and talk a little bit about our current, current state and how we really need to think differently about, about particularly staff development and staff support. And I begin during the Great Recession of 2007 and really point to a growing number of surveys that really point to the steady decline of staff morale and job satisfaction among practitioners in the glam sector. And then talk about additional research that suggests that this low morale among glam practitioners has gotten worse, really since the COVID 19 pandemic. There are many surveys that are conducted both during the pandemic and after the pandemic that reveal that there is a direct correlation between institutional leadership, institutional support, professional development, and job satisfaction. And this is also compounded by the fact that traditionally libraries, archives and museums have relied on grants and soft funding and limited term contracts to create a workforce that is really relegated to to precarious and contingent cycles of project based work. So my book really looks at how digital leaders can really create more equitable workplaces by adopting a people first framework for organizational development that prioritizes personal growth of everybody in the organization, not just a few, but everyone across the organization. And this is going to be really critical if we are to develop AI literate staff and faculty within the claim institutions. And so to do this people first framework really for organizational development really prioritizes a continuous learning culture by making a continuous learning culture really a core skill. So individuals are actually evaluated or not? I wouldn't say evaluated with their mutual with their evaluations, there's a mutual agreement on the continuous learning that individuals want to engage in and then they're supported in those specific learning goals that they have to be able to do that. We need to really rethink our approach to professional development and really focus on equitable access to training and educational resources. So instead of professional training benefiting a few members and pockets of an organization through professional development funds, thinking about how we can leverage continuing education and democratize it through enhanced access to digital learning platforms and institutional wide training opportunities rather than individual training opportunities within institutions. That of course can be balanced with focused skill development. And underscoring the importance of ethical implications of AI and job displacements that many people are worried about by really focusing on how we can advocate and empower practitioners in their own development and focusing their specific training on what is important to them and what would be helpful for them and ways that they can really become critical consumers of AI in their specific context. I do think this is kind of an interesting area where in the future AI agents could possibly be a catalyst for helping practitioners with equitable training programs and particularly as it relates to focused learning. I think that's an area of development that's really, really interesting. But if AI agents are used, I really am a firm believer and my book presents this approach as a people first approach that balances professionals and collaboration between practitioners through centering knowledge exchange right through and across glam institutions, but also with the communities that we serve and then investing in communities of practice, not only in kind of developing new approaches and new ways of engaging with our work, but also investing in open source development of AI tools by specific practitioners. All of this to say is that people, a people first framework for organizational development and professional training is really critical to promote an environment where individuals can have a critical understanding of AI's evolving capabilities and limits. One of the things that I underscore in my class is that AI is just not AI applications and ML applications are not just a one time something that you learn, but they're completely evolving tools and applications and future generations of land practitioners must continually interface with it to be able to understand how to best use it in the context of their own work settings.
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C
I really like this idea of an equitable staff development that's human centered and that we're not just focusing skill development on a few, but the many to really train up everybody to be information literate. And that's so important. Now, given the accelerating pace of technological change, especially in this AI era, how can glam institutions strategically adapt while remaining aligned with their public mission? And what concrete steps should leaders take to ensure ongoing relevancy and resilience?
- [43:22]
B
This again goes to the heart of really ethical AI and really leading with ethical AI. In my book, I make the case that digital leaders understand that if glams are to remain relevant and resilient, they really need to adapt as AI becomes more integrated into the world. Right on one sense, and we've talked a lot about that through the course of our conversation. But they also have to really keep in mind the importance of connecting to their communities in new ways by underscoring their specific shared responsibilities with their communities. And so again, I think ethical AI is leading with ethical AI is at the center of this in terms of aligning AI implementation and adoption with that public mission and then integrating community feedback in the process. And we're just beginning to see examples of individual institutions that might be implementing AI without kind of providing a community feedback loop. And it has been complicated, to say the least for these institutions, particularly as it relates to their public mission. And so ethical AIs I think, really, really important. Important. I have a very specific definition of ethical AI. I do a lot of training across the country talking with practitioners, and the question always comes up, what is ethical AI? Is there such a thing as ethical AI? And so I always like to begin by, you know, defining what that is. And so for my definition, I really, I believe that there is such a thing as ethical AI in glams. And I think that it is really about balancing value based collection care with responsibility responsible stewardship practices by prioritizing the expertise of GLAM practitioners and the feedback of their designated communities in AI adoption and implementation. So four important components of that definition, responsible stewardship practices, centering responsible stewardship practices, values based collection, collections, care exercise, or really prioritizing practitioners expertise and then community feedback. All of those are really important components to leading with ethical AI and are really important in terms of advocacy in an age of AI. So thinking about what our shared responsibilities are with our communities and how we can in our advocacy initiatives as it relates to AI implementation and adoption, how we can bring our communities into the conversation is a really important component of maintaining that public mission and those larger advocacy initiatives for relevancy and resilience. Ways that we can do that are really centralizing projects and opportunities to engage in shared digital collection, building with our communities, right? Capturing history in the moment. This is something that we can only do with our communities. AI can help us do those, machine learning can help us do this, but only we can really define and make appraisal decisions about what those digital collections will look like that best represent our communities. Establishing advocacy programs for collective impact by revisiting our shared responsibilities of preserving cultural heritage. This is a really important responsibility that we have that we often share with our communities. Again, AI cannot do this for us. It's something that we do through working with our specific communities. And then finally, I talk a little bit about reaffirming democratic values and civic engagement through data integrity and the importance of in the future of how we are working with our communities to reaffirm these democratic values and the civic engagement of our place really in society in terms of libraries, archives and museums and what their public missions stands for.
- [47:25]
C
So now let me ask you, now that the book is out, what are you working on next?
- [47:29]
B
Lot of exciting things I have. I am a guest editor for a special issue of Collections, a journal for museum and archives professionals, and that is going to be published in June. And so really excited about the special focus issue. The title of the focused issue is Ethical AI Challenges and Opportunities for Collection and Stewardship and Glams. And the call was really for case studies, original research, review of resources and scholarly reflections. So it's really a wide breadth of articles. We received a positive response actually from an array of individuals across glam and so the print issue is going to be published in June with the online first articles actually appearing. They're currently on Sage's online first platform. But the thing that I'm really excited about with this special issue is that the articles address the central challenges that we're facing in our fields. But the very interesting thing about this issue is that it also models approaches that help us consider ways to engage with AI that center ethical stewardship. So I'm a big believer in like, sharing our expertise, our specific findings, particularly when we use AI and specific contexts. And we as a field can benefit so much from sharing that experience. And so in many ways this special issue is kind of a. It's a reflection of that. I've written the introduction for that special issue, which is called Finding Common Ground in Ethical AI and three themes. I also have contributed an article relating to AI microskilling and it's called Learning in the AI Microskilling and Ethical Stewardship in Glam. In addition, I'm going to be presenting the Society of American Archivists Annual conference in New Orleans this year in July on trust indicators in AI assisted workflows. So really delving a little deeper into kind of human in the loop centered approaches to implementing AI. And also in the fall, going to be serving on SAA's new AI task force, which I'm really excited about. And, and they're coming together to develop a value statement for the field as well as some other areas that will focus on proficiencies or attributes or competencies relating to the actual archives field, both within professional and educational contexts. So I'm really excited about that. And then I have, of course, I'm doing additional research and writing on topics relating to ethical AI.
- [50:12]
C
Wow, so. So you're staying very busy now that the book is out. I'm actually really looking forward to that special issue and, and particularly your article on the micro skilling. I think that's, I think that's going to be great.
- [50:26]
B
Thank you, Michael.
- [50:28]
C
Well, these all sound like interesting projects, Angela. And I want to thank you for taking your, your taking your time to speak with me today. I really enjoyed our conversation.
- [50:37]
B
It was my pleasure, Michael. Thanks.
- [50:39]
C
Thank you so much. I'm your host, Michael Amagna. And thank you for listening to the New Books Network.
- [50:45]
B
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