DATA USE

When determining how questionnaire data can be used internally and shared externally, refer back to the initial decisions and policy documents related to the organization’s intentions for the data and the parameters outlined in the questionnaire (see Getting Started and Creating and Implementing a Questionnaire). The demographic data that is documented and stored should support the planned end uses. For example, using quantified data for internal analytics requires controlled terminology. Contextualizing the demographic data on an individual level requires narrative (free text) options. Your data-capture tools and process should allow both approaches. (See Questionnaire Structure.)

When deciding where and with whom to share the data, keep in mind the legal and ethical implications of assigning identity tags to named individuals, e.g., in an object ID on a label or in a visible search tag online. The data you gather can be intentionally misused or cause unintentional harm to the identified artist. Consider limiting quantified reporting to the aggregate, with a preference for a contextual approach when sharing data about any individual publicly. Assume that any data gathered is potentially public, as there is no way to ensure the security of the data over time and across users once it is documented, regardless of your good intentions. If you think any of the data you gather should NOT be made public, then it is best not to collect it. Consider both the intended and unintended consequences

NOTE

Organizations may want to use the consequence decision tree found in the Playbook for Ethical Technology Governance to reflect upon consequences.

Resources

Data Use

Sandberg, Jane, ed. Ethical Questions in Name Authority Control. Sacramento, CA: Library Juice Press, 2019.

Dunagan, Jake, and Ilana Lipsett.

“A Playbook for Ethical Technology Governance: Helping governments anticipate and prepare for unintended consequences of new technology.” IFTF Foresight Essentials, Foresight Matters, July 21, 2021.

D’Ignazio, C., and L. F. Klein. Data Feminism. Cambridge, MA: MIT Press, 2020.

“Insight, Impact, and Equity: Collecting Demographic Data.” of making artist demographic data accessible when sharing information both internally and externally.

PEAK Grantmaking.

Increasingly, arts organizations are being asked to report quantified data on the demographics of their artists by third parties, e.g., funders wishing to ensure their contributions support diverse communities, researchers examining collecting and exhibition practices, or potential employees or board members. These often take the form of broad percentages of given demographic groups, which loses detail (flattens) and can embed assumptions in your data set (e.g., by referencing “Black” or “White” artists without defining what those terms mean). Internal uses could also result in the flattening and decontextualization of data. When deciding what data to share and with whom, be aware of the requestors’ intentions and that any nuance in your data may be lost as these parties use the data for various purposes.

Using the planning and policy documents created earlier in the project as a starting point (see Planning and

Policy Documents), develop more detailed policies, plans, and procedures around data use prior to distributing the questionnaire.

Create and approve within your organization a publishing process for demographic information, including provisions for takedown.

When sharing data online, consider how you will both present and make artist demographic data discoverable as well as the implications of your choices. Information could do harm, such as outing an individual, exposing refugee status, or unintentionally misrepresenting changing identity information.

Have an organizational policy about how much demographic information is publicly shared about any individual or group of individuals. Consider all demographic information recorded by the organization equally whether it describes artists, donors, employees, board members, etc.

Note

Organizations may want to consider their intentions for artist demographic data collection and use the lens of the seven principles of data feminism as proposed by Catherine D’Ignazio and Lauren F. Klein in their book Data Feminism. The principles are: Examine Power; Challenge Power; Elevate Emotion & Embodiment; Rethink Binaries & Hierarchies; Embrace Pluralism; Consider Context; and Make Labor Visible. In so doing, organizations may become aware of and determine how they might address power imbalance and its reflections in data science.

Resources

Data Use Continued

Montenegro, María. “Subverting the Universality of Metadata Standards.” Journal of Documentation; Bradford 75, No. 4 (2019): 731–749. DOI:10.1108/ JD-08-2018-0124.

“More Than Numbers: A Guide Toward Diversity, Equity, and Inclusion (DEI) in Data Collection.” Charles and Lynn Schusterman Family Philanthropies.

Rahnama, Hossein, and Alex “Sandy” Pentland. “New Rules of Data Privacy.” Harvard Business Review, February 25, 2022.