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Research & Data Justice

What is data justice?

"Data justice is an approach that redresses ways of collecting and disseminating data that have invisibilized and harmed historically marginalized communities. For decades, if not centuries, data has been weaponized against BIPOC communities, in particular, to reinforce oppressive systems that result in divestment and often inappropriate and harmful policies" (Coalition of Communities of Color. Research Justice).

 

The myth of data neutrality

"Data are never objective and free from bias or ideological convictions, just like historical sources. Data convey conflicts, hegemonies, and colonialisms.

They reinforce cognitive biases and subaltern positions, and can sometimes intensify effects of silencing, both in regard to victims and processes of opposition or even defiance. Recognizing the epistemic baggage in data and data structures is thus a key part of carrying out digitally informed source criticism" (Haslinger, P. Data are Never Neutral. Copernico.eu).

 

Data "Neutrality"

 

 
Are data truly objective

 

 

They may seem so - but is this really the case?  Data do not spring out of the ether - to exist they must be generated - generally by:
 

  • human researchers
     
  • an observational instrument, such as a rain gauge, thermometer, sound measuring device etc
     
  • or an artificial intelligence tool

 

In the case of machines / AI tools these are programmed by a human research team and / or scrape up data derived from human activity.  Every decision that goes into the research design is a reflection of the time and place in which the researcher is working - as well as the researcher's particular interests, objectives, world-views etc., and cannot therefore be considered neutral.



 

Consider this example from Canada: 

 

"The 2021 Census of Population marked the first time that data on gender were collected, allowing for cisgender and transgender men (and) women, and non-binary people to be captured....

Previous versions of the sex at birth statistical standards did not include the distinction of "at birth." Until 2018, gender did not have its own statistical standard. Previous versions of the classification of gender did not include the distinction between men, women and non-binary people, or the distinction between cisgender, transgender and non-binary people until 2021" (Statistics Canada. Understanding sex at birth and gender of people in Canada.)

 

It's interesting to consider who gets to decide which types of demographic information are valuable, who (if anyone) benefits from data gaps like these, and what socio-political processes might lead to change. 

The bottom line: important historical information about Canadians who are transgender or non-binary does not exist because it simply wasn't collected - and the data that were collected before these amendments paint an imprecise picture of the population.

 

To learn about about the gender-identity options now available to respondents see Statistics Canada: Age, Sex at Birth and Gender Reference Guide: Census of Population 2021 [PDF].

 
 
Ask yourself

 

 

  • Who has the power and resources to collect data?
     
  • Which platforms are accessible for those who want to disseminate their data?
     
  • Who decides which participants / species / phenomena are included or excluded?
     
  • Why might certain geographic regions are be excluded from study? 
     
  • Who decides which questions will be asked
     
  • Who decides which responses are relevant?  How are "irrelevant" responses coded?
     
  • Who determines if the research project is ethical
     
  • Who determines the research goals
     
  • How transparent are these decisions to the consumers of the data you've produced?

 

Implications for Research and Society

Implications for Research

 
 
Every decision about research design has repercussions and can alter your data in many ways, including:

 
  • the volume of data that are produced
     
  • the barriers to accessing / re-using the data
     
  • the time frame covered
     
  • the demographic groups who are included, excluded, and/or aggregated together
     
  • the geographic locations which are included or excluded from the study
     
  • the themes/topics that are explored or ignored
     
  • the interpretations and conclusions that are published and disseminated around the world

 

 

Implications for Society

 

 

What happens when entire communities are under-represented or invisible due to poor, biased or dated research design? 

 
  • The people within these communities may be un-served, under-served or misunderstood.
     
  • Communities perceive that research only serves to benefit to the research team and its institution - not the communities or groups who were studied.
     
  • Historic harms may be perpetuated.
     
  • Community distrust of academia / government may be hardened.

Learn More

  • Data Harm Record: From the Data Justice Lab, this site provides "a running record of ‘data harms’, harms that have been caused by uses of algorithmic systems...."

     
  • Data Justice:  From Royal Roads University Library, "this guide introduces researchers to the concepts of data justice, its connections to data literacy and data equity, and provides guidance for incorporating data justice into their research projects."

     
  • Diversity, Equity & Inclusion in Research:  From University of Maryland Libraries, this is a "guide to help research embed equity, diversity and inclusion into the research process, from planning to citations.


  • Dr. Linnet Taylor on Data Justice.  [Youtube. 17:04].  From Diggit Magazine.  "Data is often called 'the new oil'; the world's most valuable resource. But....we don't want to wind up in a climate-change-situation with regard to our data. In this video Dr. Taylor talks about data justice, group privacy and data ethics."