“One thing scientists do is to find order among a large number of facts,” according to Alan Stern, “and one way to do that across fields as diverse as biology, geology, physics and astronomy is through classification.”
We think Stern’s rationale applies to PIT as well.
Through our first round of interviews with Harvard experts, consultation of PIT literature, and initial mapping of PIT initiatives on the diagram from Entry II, we’ve identified a definition for PIT. Our definition builds on the those offered by Sasha Costanza-Chock and Michael Brennan: PIT is the synthesis of non-technological fields with digital technological expertise channeled toward solving issues of public concern.
The nature of this synthesis varies based on the axes of our PIT diagram: the level of tech and non-tech centricity and the orientation of the technology to the relevant governance issue.
Our definition prioritizes the interaction of varied perspectives and skills. Technological expertise alone — such as the advanced study or application of data science — strays from the interdisciplinary nature of PIT. It’s true that digital technology initiatives alone can lend important insights to issues of public concern. For example, a data scientist can cull data from a 311 portal and identify some patterns that could prove useful to public servants.
What distinguishes PIT, though, is the combination of this technological expertise with the additional perspective of someone well-versed in non-technological skills such as ethics and governance. When the data scientist’s work receives feedback and guidance from an ethicist their joint output becomes more nuanced, more applicable to future use as well as to use in the public sector, and more reflective of the context in which the data was gathered. In the same way, an ethicist’s review of a city’s 311 data collection practices falls outside of PIT because it lacks the technological rigor that a data scientist, software engineer, or practitioner of a more technological trade could add.
There’s no perfect formula for identifying something as PIT. That’s why we’re starting with our best approximations of where current research, coursework, and center programming at Harvard land on a map of the PIT universe at the University. Our team member, Lavanya, a freshman at the College, thoroughly analyzed the websites of centers, degree programs, and initiatives to enumerate any and all PIT-related work. She sorted courses, programming, and research along the aforementioned axes:
- Level of Tech/Non-Tech Interaction — is the research, center, or course oriented solely toward one end of the technological spectrum or does it facilitate interaction between technological and non-technological expertise?
- Orientation of PIT — to what extent is a non-technological perspective informing the use of a technological solution to a governance problem and vice versa? In other words, how does the effort fall on a spectrum of technology for governance on one end and governance for technology on the other?
We’ve additional done our best to consider the applicability of PIT initiatives housed in specific Harvard schools to the rest of the University. For example, the joint work of the Philosophy and Computer Sciences departments to create more co-taught courses qualifies as being applicable to several schools across the University. Comparatively, the Connected Teaching in the Digital Age course (T525) offered by the Harvard Graduate School of Education likely applies just to that school. Including some consideration for applicability in our mapping allows us to identify areas for potential collaboration — i.e. ff there’s a multi-school effort underway that’s presently “below the PIT line” then perhaps a reciprocal above the line partner exists.
More specific criteria for each of the axes and variables will amplify our ability to spot potential partnerships. These criterion are works in progress. Here are some of the questions we’re asking (feel free to send us your thoughts):
- What qualifies as expertise in digital technology? We think it makes sense to include computer and data scientists in this camp. We struggle to place someone like a product manager that has familiarity with highly technological products and tools but may not be formally trained in the use of those tools.
- How do you assess the orientation of a PIT effort? Some cases — such as the introduction of ethicists into computer science courses — seem fairly straightforward. In this case, you’re taking a non-technological field — ethics — that has substantial overlap with governance issues and applying it to tech; hence, governance for tech. Other cases will not be as clear. How do you think we should weigh placement on the PIT axis?
- What does relevant to another school or center mean? If, for example, one student cross registers at a PIT-related course does that mean it qualifies as multi-school or does a multi-school characterization necessitate a more substantive overlap?
Please continue to share your thoughts on our work. Tell us about PIT-related work going on at your institution. Let us know what other questions we should explore. Point us to some suggested resources! (@hks_digital #PublicInterestTech)