Gender Data Course 101 (TechChange): some reflections

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(due to a synchronization issue with my account, the original post was published here:

https://medium.com/@frauastradata/gender-data-course-101-techchange-some-reflections-60820d99b968

A couple of months ago as part of my career transition into data analysis and effort to have a more holistic approach on the topic, I had the chance to attend the first data gender edition 101 course promoted by TechChange.

My motivation to participate in the course was primarily curiosity to collect new learning inputs and get the chance to exchange ideas with other humans passionate about data and gender. Once the course started, I got the chance to login into the course platform which a first glance was very organized and with plenty of interaction options. I enjoyed the structure and the possibility of attending (at my own pace) the orientation course whose goal was to present the different tools and functionalities to maximize the virtual experience.

The most enriching part was offered by the speaker’s session, experts from all over the world, having different backgrounds and dealing with data in different fields. Here the format was 30 to 40 minutes topic presentations from the speaker and a live chat in which participants added questions to the topic and the moderator collected them to create a “live” interaction and share feedback on the discussed topic for an overall time of one hour.

Jumping back to my memories, one of the reflections (based on an Excerpt from Lera Boroditsky, Cognitive Science Professor at the University of California, San Diego) that I appreciated the most was the “Gender & Language” on the first week of the course. In one study monolingual participants of Italian and German language as well bilingual participants of the same languages were asked to explain why an object was attributed to a grammatical gender instead of another. For the German language, the item had a masculine article, while the Italian had a feminine one. On the other hand, bilingual speakers took the time to realize that there was a gender disagreement between the two words gender.

Monolingual participants mostly answered that the gender assigned to the said item was a fact and (to me) it felt quite no arguable why it was like that. Conversely, bilingual speakers were keener to identify this difference among languages as an inherited language structure. This simple example underlined how exposed we are to bias when we do work with gender and data (and probably also in other fields in which we apply our knowledge). To me, this exercise about “awareness” was important, but also easy to remember and it should be applied whenever possible, to remind us of the world structure (economic, social, political, and many more) we live in.

As for the live event with speakers, the presentation that I cherished the most is the talk of Paz Peña (independent consultant & activist on human rights, intersectional and digital technologies).

She presented some questions around gender and data and the importance of an intersectional approach. All this by an underlying that the questions she introduced do either have an open answer or not answer at all.

- Feminism or Gender? Which of these two approaches should be considered when working with data? To Paz gender seems to have a more technical approach while feminism has a political and more critical thinking background. However, one does not exclude the other.

- Can datafication be applied to reality? My understanding of the question here is if realities can be measured and how the use of technologies is shaped by the economic system we live in to quantify or measure most of the aspects of our life

- Is data feminism possible? One theory proposes to collect more data and cluster them differently to avoid or reduce (gender) biased data. The other one considers emancipation as an alternative to be socially represented (by keeping in mind the economical capitalistic world we do live in)

- Our inclusion and diversity a way of overcoming biased decisions based on partial data? Historically the need of representing minorities or not standardized categories comes always from these affected categories, so how can we be reassured that most of us are representee when working with data

Remembering that there is no answer to these questions, but open-reflections it is also important to consider that it is fine to have a solution for everything. The difference here is to be aware of these limits.

Overall, the content of the Gender Data Course 101 covered different aspects of dealing with data and shared resources from ongoing projects, initiatives, and up-to-date information (which is not an easy task as the data analysis field grows constantly). I liked the accessibility and sharing of tools, sheets with additional information, and links to interesting projects. A background or some first experience in data might be helpful to go through the content and get move through the course without feeling overwhelmed.

I am thankful for the food for thought from the course and the speakers. This experience helped me understand better how “awareness” is an important aspect of growing as a professional. Mistakes and misinterpretations (or underrepresentation) will always happen, but knowing that and working on correcting them is a step towards a structural change.

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Astra Stories Data Storytelling I Data analyst in progress

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Astra Stories

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Astra Stories Data Storytelling I Data analyst in progress

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