The goal of this document is to describe the current landscape of publishers moving into core activities of universities. Actively formulating and implementing solutions to these problems is complex but critically important, and something SPARC intends to work closely with the community on. However, we think it is worthwhile to conclude this document with a recap of some of the options for action currently available to the academic community.
In general, we think that solutions must be tailored to individual colleges or universities, although some of them would likely require a significant number of institutions to work together. For any institutional context, the key drivers will be a mix of cultural elements (how centralized or decentralized are decisions? What is the appetite for establishing deep ties to commercial vendors?) and financial considerations (what are the spending priorities? What magnitude of savings does leadership hope to get from the deployment of data analytics? Would targeting significant revenues from IP represent an acceptable decision? What kind of companies are off-limits to partnerships because of their activities?) We aim to offer a menu of choices and illustrations of best practices, rather than prescribing a single course of action. That said, we think that there are two different sets of solutions.
We think of the first set as risk mitigation solutions. These are actions aimed at protecting colleges and universities from the unintended consequences of deploying a rising number of data analytic tools and collecting larger and more intrusive amounts and categories of data.
These actions could include establishing detailed data policies and mechanisms for ensuring compliance. These policies could demand that institutions maintain the ownership of data, that terms and conditions of contracts with commercial vendors are not covered by non-disclosure agreements (i.e. open procurement), that algorithms used by vendors are made fully transparent, that results from their usage are portable (i.e. that institutions maintain the right to historic output series, in order to facilitate switching to other vendors), that data is not re-sold to third parties (or at least is not resold without the explicit consent of the institution), and that data sets that are deemed particularly sensitive are not turned over to government agencies without first resorting to the appropriate court, etc.
We also think that risk mitigation requires appropriate organizational decisions. Some institutions may opt for coordination group or task forces, while others may decide that presidents or provosts may want to identify individuals tasked with both issuing data policies, monitoring execution, and helping individual offices negotiate with vendors and adjudicate possible conflicts of interest across different parts of the institution. Over time, we think that the latter solution will prevail in most complex institutions, but each one will have to determine its own transition pace.
A second set of solutions will require individual institutions to decide among the many trade-offs. For example, using data analytics to drive the admissions process is less expensive than hiring a large staff, and institutions may choose different levels of human intervention to balance their budgets and oversight priorities. Similarly, research institutions must balance an aggressive policy of commercial exploitation of intellectual property to supplement funding with faculty demands for independence and funding of basic research and disciplines with limited commercial upside. Some institutions will support community solutions to some of these issues, while others will be skeptical. Our goal is to offer a broad array of detailed solutions and to help, when needed, individual institutions think through the issues.
In conclusion, we believe there is still time for the academic community to act, and now is the time to do it. By taking stock of the situation, asking the right questions, and choosing the right course of action, the academic community can prevent itself from winding up in a position where it is obliged to follow a path out of its control and harmful to its future.