Lessons for “big data”

What are the potentials? What are the big problems?

“We deﬁne Big Data as a cultural, technological, and scholarly phenomenon that rests on the interplay of:

Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets.
Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims.
Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.” (boyd & Crawford, 2012)

“The next time you hear someone talking about algorithms, replace the term with ‘God’ and ask yourself if the meaning changes. Our supposedly algorithmic culture is not a material phenomenon so much as a devotional one….It gives us an excuse not to intervene in the social shifts wrought by big corporations like Google or Facebook or their kindred, to see their outcomes as beyond our inﬂuence [and] it makes us forget that particular computational systems are abstractions, caricatures of the world, one perspective among many. The ﬁrst error turns computers into gods, the second treats their outputs as scripture.” (Bogost, 2015)

“We believe ‘big data’ research can be similarly improved by working with, rather than denying the importance of, ‘small data’ (Kitchin and Lauriault, 2014; Thatcher and Burns, 2013) and other existing approaches to research….Furthermore, doing critical work with ‘big data’ involves understanding not only data’s formal characteristics, but also the social context of the research amidst shifting technologies and broad social processes. Done right, ‘big’ and small data utilized in concert opens new possibilities: topics, methods, concepts, and meanings for what can be understood and done through research.” (Dalton & Thatcher, 2014)

Ten simple rules for responsible big data research

Acknowledge that data are people and can do harm
Recognize that privacy is more than a binary value
Guard against the reidentification of your data
Practice ethical data sharing
Consider the strengths and limitations of your data; big does not automatically mean better
Debate the tough, ethical choices
Develop a code of conduct for your organization, research community, or industry
Design your data and systems for auditability
Engage with the broader consequences of data and analysis practices
Know when to break these rules

Zook, Matthew et al. “Ten simple rules for responsible big data research.” PLoS computational biology vol. 13,3 e1005399. 30 Mar, 2017. doi:10.1371/journal.pcbi.1005399

Checklists, principles, examples

Data Ethics Decision Aid: DEDA
Data Harm Record: DHR
Data Science Ethics Checklist & Examples of Data Harms :Deon
“Feminist Data Visualization” (D’Ignazio & Klein, 2018): FDV