The Hidden Costs of Reliance on Proprietary AI for Libraries

  The Hidden Costs of Reliance on Proprietary AI for Libraries

Libraries worldwide have quickly identified the promise of artificial intelligence in improving user services and streamlining back-end operations. Automated cataloging tools, recommendation engines, and data-driven insights into how patrons engage with collections offer a chance at more profound, meaningful interactions with resources. However, while the possibilities are undeniably energizing, there are mounting concerns about institutions becoming passive recipients of technology produced and controlled by entities whose motivations and business models differ significantly from those of academic or public libraries. In particular, the role of large technology firms stands out as a crucial element in shaping not only the capabilities of AI but also the ideological and ethical frameworks within which these tools operate.

When a library becomes dependent on a proprietary tool from a major technology company, it not only subjects its entire process to the conditions set by that corporate entity but also risks a significant loss of agency. This loss of agency extends beyond practical risks to a more profound sense of diminished control. Historically, libraries have prided themselves on shaping their own services and governance policies, reflecting the needs of their specific communities and the broader profession's commitment to intellectual freedom and open access. However, when the tools come pre-packaged with restrictions and functionalities determined by external priorities, libraries may find that the core values they endeavor to uphold are at risk and urgently in need of protection.

One of the reasons technology corporations have become so influential in this sphere is their capacity to invest at a scale few academic or public institutions could hope to match. Tech giants have assembled enormous datasets culled from billions of users worldwide, which they can then feed into advanced machine-learning systems. Their research and development budgets dwarf those available to most libraries. As a result, the most cutting-edge AI typically arises from these environments, reflecting the computational muscle and expertise that are more readily found there. Consequently, libraries that want to remain at the technological forefront may find that developing in-house solutions requires a level of capital and staffing that is simply unrealistic. The fallback option—purchasing or licensing existing AI products—seems to offer a quicker, more efficient route to modernization.

However, this reliance on licensed solutions can bring unintended costs to a library's sense of identity and independence. The mission and character of a library can be intimately tied to its approach to collecting, curating, and granting access to information. Whether an institution emphasizes open-source software to align with a commitment to transparency or invests in user privacy mechanisms to maintain a trusted public space, these choices form a backdrop against which all technological decisions are evaluated. A library's use of externally developed AI is not merely a technical matter. However, a policy decision impacts how it handles sensitive user data, prioritizes specific collections over others, and interacts with broader educational objectives. If the software in question was primarily designed to serve the goals of commercial clients—often data monetization or advertising—its embedded assumptions and constraints might conflict with scholarly and community-oriented objectives.

Over time, one of the most troubling outcomes of this dynamic could be a homogenization of library experiences. Libraries relying on the same vendor ecosystem may offer nearly identical interfaces, recommendation algorithms, and data collection policies. This homogenization could overshadow the local flavor of how a particular institution organizes its collections, its special attention to specific research areas, or its unique stance on user privacy. This shift can challenge the identity of libraries that, for centuries, have thrived on local curation strategies designed to reflect the particular needs of their communities. If crucial decision-making capacity moves outside the institution's purview, a library's distinctive personality and mission might not just be at risk but face a significant loss.

Furthermore, there is a persistent worry about licensing agreements that typically accompany AI solutions from major vendors. These legal contracts can be intricate and restrictive, spelling out how data is processed, stored, and shared. In many instances, the vendor retains extensive control over the rules governing the flow of information. Though ostensibly the owner or steward of the user data, the library might not be free to retain complete sets of usage logs for long-term archival, research, or auditing purposes. The company providing the AI may demand that all usage data be erased after a specific timeframe or might explicitly forbid any retention that would allow a future re-analysis of how patrons interact with the system. This limitation severely hampers academic libraries interested in studying longitudinal data for improvements in service or for scholarly inquiries into user behavior. Such restrictions hinder the library's ability to improve its services and raise serious ethical concerns about user privacy and trust.

Conflicts can arise where data must be stored for legitimate institutional reasons—such as compliance with grant requirements, the need to evaluate the effectiveness of newly introduced services, or a desire to develop in-house analytics for planning. The library's interest in retaining user data for legitimate educational or operational goals might clash with the vendor's licensing terms or privacy frameworks. If a library cannot negotiate amendments to the contract, it may find itself forced to either forgo meaningful research opportunities or risk breaching the terms of its agreement. In this sense, licensing constraints could extend beyond the purely legal realm and strike at the heart of how libraries carry out their mission, from fostering scholarship to maintaining a transparent record of institutional history. These conflicts highlight the practical challenges of AI adoption and the potential for libraries to lose control over their operations and decision-making processes.

Much of the tension stems from the reality that major technology firms usually focus on global markets and universal product offerings. These platforms and AI services are designed to meet the most common commercial needs, such as personalized content delivery or large-scale data analytics for consumer behavior. On the other hand, libraries are mission-oriented institutions committed to providing equitable access to information, protecting intellectual freedom, and serving as guardians of scholarly records and public knowledge. The alignment between corporate objectives and library values may be partial at best and outright incompatible at worst. Situations can arise where corporate policies incentivize data collection and monetization in ways that run counter to librarians' ethical standards. If the technology is structured to gather detailed user profiles, libraries might find themselves unwittingly contributing to surveillance or data commodification. This potential for unwitting contribution to practices that counter library values underscores the ethical implications of AI adoption and the need for libraries to carefully consider the tools they use.

Academic libraries, in particular, face a precarious balancing act. They operate at the intersection of educational mandates, faculty research needs, and student resource access. They must respect various local, national, and international regulations regarding data protection, intellectual property rights, and academic freedom. When a major technology vendor imposes stringent rules about storing or repurposing data, the library must struggle between fulfilling institutional obligations and adhering to external constraints. For instance, an institution might need to keep detailed usage statistics to justify future budget proposals or measure the impact of specific collections on student success. If the vendor forbids the library from retaining these logs or repurposing them for institutional research, the library could be disadvantaged in internal policymaking and broader strategic planning.

Another dimension to this issue is the legal uncertainty surrounding AI applications. Data gathered from user interactions in library systems often touches on sensitive areas related to academic research, personal reading preferences, and even private communications. Many jurisdictions have strict rules about such data—from privacy acts to regulations governing the confidentiality of patron records. In the context of a proprietary AI system, the library might not be fully aware of how to process and store data, raising the prospect of unintentional non-compliance. Even if the library diligently negotiates specific terms with the vendor, the ever-changing regulatory landscape could mean new guidelines or laws contradicting the existing agreement. The cost and complexity of renegotiating or switching vendors might then become prohibitive.

The resulting ethical and legal landscapes can be remarkably delicate. Librarians have long been recognized as professionals safeguarding user privacy and upholding transparency in managing information. The perception that a library is simply turning over user data to a corporate entity—especially one with opaque data-sharing practices—could damage trust in the institution. It might also spur faculty, students, and the broader public to question whether the library fulfills its ethical obligations. Under these circumstances, librarians could find themselves uncomfortable attempting to explain or justify the intricacies of vendor agreements and AI functionalities that they have limited power to modify or understand. Transparency, a cornerstone of library operations, can become elusive when proprietary algorithms and confidentiality clauses stand between librarians and the complete picture of data handling practices.

To negotiate this terrain, libraries might be compelled to commit significant resources to legal consultations, contract negotiations, and ongoing compliance oversight. These tasks can strain budgets under pressure from shrinking public funding or rising subscription costs for electronic resources. At the same time, library staff with expertise in AI, data analysis, and contract law are often in short supply. Institutions could find that to maintain any sense of control or autonomy, they need to invest in specialized skill sets that enable them to interpret and, when possible, adopt these technological tools to align with institutional values. Such investments may be entirely justifiable but still heavily burden operational costs.

Some libraries might explore hybrid strategies that combine off-the-shelf AI solutions with open-source software components. In this scenario, the library licenses specific modules from a tech firm while retaining some core infrastructure in-house or relying on open-source frameworks that grant more freedom. This approach can help a library mitigate the risks of total dependence on any single vendor and enable deeper customization. However, such strategies demand coordination and technical expertise not all institutions can muster. There is also the risk that if the proprietary modules are central to the system's functionality, much of the library's day-to-day operations remain locked into vendor-defined processes and constraints.

These challenges highlight a future that could unfold in multiple ways. On the one hand, the widespread adoption of advanced AI within libraries might deliver powerful benefits to users: dynamic discovery tools, more profound insights into research patterns, and improved operational efficiency. If libraries can skillfully negotiate contracts and collaborate with vendors to embed library values in designing and deploying these tools, this future could align more with the institution's interests. On the other hand, if libraries take a more passive stance—simply importing vendor solutions without thoroughly assessing the long-term implications—they could gradually relinquish key aspects of their professional and ethical identities. Such a scenario would see libraries offering uniform services shaped by external commercial interests, with little local input into functionality or data practices.

Even the path of skillful negotiation has its obstacles. The reality is that many libraries, significantly smaller or less funded ones, lack leverage when dealing with corporate giants. Negotiating custom terms can be challenging, if not impossible when the other side is a global enterprise that has standard licensing terms it offers at scale. These companies might see limited benefit in tailoring their agreements to each small libralibrary'ss, especially if the returns from those relationships are negligible compared to their more lucrative business clients. Libraries that attempt to push back too firmly on data-sharing provisions or demand the right to audit AI algorithms may face inflated costs or be turned away in favor of other clients willing to accept the standard terms. The unfortunate result is that well-resourced institutions might secure more favorable agreements, while smaller libraries become locked into less ideal contracts or are priced out of advanced AI altogether.

In contexts where libraries operate as part of a larger consortium or network, collective bargaining can offer an avenue for more equitable outcomes. By pooling resources and presenting a unified front, libraries can negotiate improved licensing terms. This collective approach can help mitigate the power imbalance and ensure that smaller or underfunded institutions also gain access to essential AI tools without sacrificing their ethical or operational priorities. At the same time, organizing such consortiums around AI procurement adds complexity since members must reach a consensus on how data is managed, how costs are distributed, and how compliance will be monitored.

Strict licensing limitations on data use or storage remain a pressing issue that cuts to the heart of the library's role as a steward of knowledge. Libraries curate external content and generate records of how that content is accessed and utilized. This valuable dataset can fuel institutional research, historical archives, and improvements in service design. If an AI vendor disallows certain forms of data retention, the library might lose an essential lens into its operations. Tracking usage over the long term can reveal changing interests within a scholarly community, the success of new initiatives, or areas where access is lacking. Without the capacity to preserve such logs, libraries risk basing future decisions on incomplete or anecdotal evidence.

A further complication emerges with issues of ownership and intellectual property. AI systems often rely on continuous streams of user input to refine their models. Suppose the vendor claims ownership over any improvements to the AI from training on library data. In that case, the institution's contributions to refining the technology become a commodity controlled by the corporation. In principle, the vendor might provide updates or improved algorithms to all its clients. This could mean that the original library that helped refine the system sees only marginal benefits compared to the value the vendor gains. The question of who truly “owns" the" data and the appropriate boundaries of use can be notoriously fuzzy in such relationships. Libraries that sign agreements granting the vendor broad usage rights over anonymized or aggregated user data may later discover that the corporate entity has monetized the aggregated information well beyond the scope that the library initially envisioned.

All these concerns can converge in a way that forces academic libraries, in particular, into cautious decision-making. On the one hand, institutions want to provide students and faculty with state-of-the-art tools that support cutting-edge research and efficient access to scholarly materials. On the other hand, they must preserve their autonomy, guard user privacy, respect diverse ethical codes, and maintain a sense of local identity that fosters trust. Is it possible to integrate advanced AI systems created by large, profit-driven entities without incurring some loss of independent governance?

In many respects, libraries stand at a crossroads. Carefully chosen AI solutions could amplify their knowledge and services, but deploying such technology presents non-trivial challenges related to values and principles. For an institution to remain faithful to its unique character and mission, it must engage in robust and proactive discussions with vendors, clarifying and insisting upon certain operational boundaries. Libraries might explore or even develop open-source or community-driven AI frameworks that do not carry the same potential for heavy-handed contractual limitations. These alternative pathways, while potentially more resource-intensive and complex, could help maintain a healthier balance between innovation and independence.

Ultimately, the threat of becoming passive consumers of proprietary AI tools is not purely a technological issue; it reflects broader questions about how power and influence flow within the information ecosystem. Libraries have long asserted their standing as public goods, aligned with educational, cultural, and democratic values. Tech giants, in contrast, are typically beholden to shareholders, growth targets, and profit margins. Balancing these disparate aims requires vigilant oversight, deliberate negotiation, and willingness to consider less conventional or more labor-intensive solutions. It may also require that libraries band together—through professional associations, consortia, or cross-institutional initiatives—to speak with a collective voice capable of compelling more transparent and flexible licensing terms from AI vendors.

As the field continues to evolve, the crux is how libraries can harness AI without betraying the core ideals that have defined them for centuries. If they can achieve that equilibrium, artificial intelligence might serve as a powerful instrument for knowledge discovery, operational efficiency, and community engagement while retaining the library capacity for its own course. If they fail, a new chapter might emerge in which libraries find their autonomy overshadowed by external imperatives, losing something vital. The choices made now, and the negotiations undertaken with AI providers will shape the character and mission of libraries in ways that could endure for decades to come.