Forgetting Practices in the Data Sciences
This essay paper is joint work with Dr. Angelika Strohmayer.
I am recording this talk from the traditional, contemporary, and unceded lands of the Wampanoag
and Massachusett peoples.
I honor their ancestors and elders – past, present, and future.
We will mention a few topics from the feminist technoscience analyses in our paper:
• How forgetting can be both beneficial and harmful,
• A taxonomy of data silences based in HCI, data science, and Indigenous studies,
• What and how we forget when we perform the data work of data science,
and • Why forgetting matters.
Forgetting has often been described as an aid to remembrance –
an aid to a memory for what matters.
If there is Remembrance, then there is also Forgettance, which is the study of Forgetting.
Many scholars have stated, without irony, that forgetting is an important part of memory
work.
We forget distractions so that we can focus on what matters.
But forgetting can be harmful.
Robert Proctor describes Agnotology – the science of ignorance – as:
“doubt or uncertainty that is made, maintained, and manipulated by means of certain arts and
sciences.”
Forgetting through the use of those arts and sciences – that’s what we do.
And part of what we do is to create data silences.
Mimi Onuqha defined data silences as: ”the blank spots that exist in spaces that
are otherwise data-saturated...
“ These blank spots can reveal our hidden social
biases and indifferences.
Dr. Strohmeyer and I constructed an initial taxonomy of data silences.
These silences are described in detail in our paper.
In this brief talk, we build on one type of data silence called Colonial Unknowing.
Colonial unknowing is the attempt to hide Indigenous peoples and their knowledges,
as being impossible or inconceivable.
Through Colonial Unknowing, oppressors assert their reality over the world knowledges of
the peoples they have conquered, and whom they attempt to replace and erase.
We begin with Colonial Unknowing to clarify the concept of Data Unknowing.
In our analysis, we built a forgettance stack of data science
– in this slide, of machine learning.
When we do data work, we change the data to make the data fit-for-purpose.
We transform variables, we discard cases that we don’t want to analyze,
we compute new variables, and we create an entirely new type of data
called ground truth.
At each step, we forget the preceding steps.
We keep no systematic records of what we did to the data,
or how we did it, or why we did it.
We forget, and we forget, and we forget.
By the time we train a model, we assume that our data are perfect.
We have forgotten how we made those data fit-for-purpose.
For a little while, we remember that data work is human work,
and that humans inevitably bring their own unexamined social assumptions and biases to
that data work.
But then we forget that the data and software were created and modified by fallible humans,
And we are left with the bright shiny data science pipeline.
We have systematically forgotten all of the human actions
that could bring doubt on our pipeline, and on its outcomes.
Like the data silence principle of Colonial Unknowing,
we perform a kind of Data Unknowing.
We unknow – we erase – knowledge of the many things that could be wrong with
our data, our pipeline,
and how that pipeline may be used to harm people, including people we love.
With the best of intentions, we unwittingly build bias into our software,
and we forget how that bias got there.
and we forget that that bias is in there, in the data that we have designed and made.
In our paper, we call for a kind of Sociotechnical Reform in our Data Work.
We need work practices that help us remember what we actually did in our data work.
We need technologies that help us remember.
To improve the quality and the ethics of our analyses,
we need to calibrate our Remembrance and our Forgettance.
Because, If forgettance is an aid to a memory for what
matters, then
People and their actions matter, and we need practices and technologies to
help us remember.
And so we leave you with these three questions: • What data do you systematically forget
in your data practices?
• Who is harmed by your forgettances?
• How can we work together to reduce those harms?
Thank you.