#DataYou: Ixone Sáenz Paraíso

Ixone Sáenz Paraíso is an IT support technician and data engineering officer. She is also deaf and has worked on the Data Education in Schools British Sign Language glossary project to better communicate data science terms within the deaf community, this is her story.

Data just meant ‘information’ to me when I was at school in Bilbao, in the Basque region.

I’m from a deaf family and my parents didn’t go into higher education, or have much knowledge of IT. The school curriculum during my time in education only covered the basics, such as Microsoft Office and word processing.

My understanding of what data was then, compared to how I understand and interact with it now is completely different.

My first degree was in Library and Information Science, but it was only when I started learning about databases that I started to realise the true breadth of the word ‘data’. The more I learned, the more I went down the rabbit hole!

I was always very interested in data analysis – I like finding patterns, seeing how things are connected in order to reveal the bigger picture – so I decided to enrol on a Data Science MA at London South Bank University.

Pursuing the MA degree was challenging, I had a different experience to my classmates, and had to devise a number of strategies on the go, in order to overcome the challenges and obstacles I experienced as a deaf signer in a hearing environment.

Firstly, there’s not really the terminology in British Sign Language (BSL) to address detailed subject matter in topic areas such as computer  and data science. Interpreters could be reluctant to work with me due to this, or if they did, they wouldn’t have the necessary subject knowledge. When you get to MA level data science, it’s a complex subject and it’s a lot to expect people to understand.

Confusion also arose in cases where data science terminology might borrow an existing word in everyday parlance, that means something completely different in the context of data science. That can be very difficult.

‘Python’, for example, is a software platform, but the interpreters might use a different sign – like the sign for a snake! That was very confusing. You have to try to figure it out with the interpreter in the moment but by that time, they would be very behind with the lecture being delivered as they would not understand why we are talking about a specific breed of snake in the lecture, or they would doubt if they had understood the word correctly and would need to clarify

That’s one small example, but there are many more instances where there aren’t commonly used signs to represent data terminology. We had to be creative and use our own agreed signs. When agreeing some signs with my interpreters it was tricky, because BSL is not my first language, as I am Basque. I was not sure if some signs were already in use and I simply wasn’t aware of them, or if I was using the wrong signs.

Quite often, I would have to decode what the interpreter signed to me, and translate it into something that made sense. I often didn’t have  regular or consistent interpreters, which was challenging and frustrasting, as you can’t build up thee necessary rapport. Later on, in the second semester, a Communication Support Worker (CSW) started to work with me who was really interested in data, with a solid foundation of  knowledge on the subject. They were able to break down the information, although they weren’t amazing at signing. Then I had another good interpreter, who had no idea about IT and data science, but made a good team with the CSW as they could support each other to relay the right information in their joint interpretations If they couldn’t clarify something because they didn’t understand or I wanted to ask something complex, I’d write it down for a Basque classmate on my course and ask them to ask the question. That was a useful coping strategy. The correct interpretation of any subject is very important when it comes to education, as it helps to minimise the disparity between deaf learners who are receiving the information second hand, via an interpreter, and our hearing peers who are not.

Soon after I finished my MA, Covid hit and that became challenging, so I decided to have a break for a while. Later on, I eventually got involved in the glossary project thanks to a tweet that Audrey Cameron posted. While getting involved in the glossary project, I began working in a local authority in London, gathering information about Covid infection rates in the community.  I  was very keen to apply my data skills and this was an opportunity to do so.

Regarding the glossary project, I found it to be a very interesting and necessary endeavour – a BSL project which aimed to create useful data terms to help open up the field to people in the deaf community. I was very keen to be involved in the project, thinking back to university and how I had struggled with data science terminology and using BSL interpreters. As technology moves on, language has to follow and move on accordingly. BSL has to develop in that way too, and it’s so important to have language to support effective learning.

Shared language means people are able to engage with the subject and learn it. If you don’t have specific language to describe the subject matter, it’s very difficult to engage and becomes more of an exercise in trying to decode English. When there isn’t a sign, you might fingerspell it but then it’s not really visual anymore. You’re losing the meaning behind it. It’s not really sign language. Also, an English word might encompass several concepts, but these cannot be directly transliterated into BSL because it is a visual language. Sometimes we might have two different signs for one particular word. We wanted to emphasise that in the glossary and made sure we had agreed signs for each different meaning derived from the same word, rather than muddying the waters. The word ‘variables’, for example, is commonly used in data science, so I’d sign it in a particular way – but it means something different in computing. So the sign has to be different to capture the visual meaning in these different contexts.

The glossary opens up greater educational opportunities for younger deaf people. It expands their opportunities to get into data science and computing, because they are such specific fields and without the language to describe what we’re talking about, how do we expect young people to study specific subjects and absorb them? We hope the glossary will encourage more deaf people to be involved in these fields.

Also, interpreters coming into this complex field can be confident they have signs to describe what’s going on. In my work, I use interpreters funded by Access to Work and I can say ‘Have a look at this glossary to help you with the kind of terms we will be using’.

There’s a lot of onus placed on us as deaf people to educate interpreters – and tools like this glossary allow us to simply signpost it. They get access to terminology and help, especially those working in deaf schools, for example.

The glossary gives not only the sign for the word, but also a full definition in sign language. For interpreters, it means they can understand what it means and to relay the information better. For me, it really clicked having a definition in sign language – something visual.

The glossary will hopefully solve or simplify a lot of things for people and enrich the lexicon of BSL and make it a fuller, richer language.

The glossary at the moment is specifically linked to the curriculum. But there’s capacity to add new terms as they come along and become commonplace, so I hope the project is ongoing.

Hopefully, the BSL glossary will encourage more deaf people to look at careers in data; it’s such a broad career and there’s so much you can do. I don’t want people to have a view that their career choices should be limited by the fact that they’re deaf or because they are aware that there are not specifically trained interpreters in their chosen field.

I’ve also been involved in adding some BSL videos to the virtual Data Town and I was very happy to do so.

At present, I’m working as an IT support technician and data engineering officer for a video relay interpreting company. In future, I want to focus more on coding and would love to educate interpreters or become a teacher of deaf children with a focus on data and similar subject areas. I’m passionate about deaf children having strong female role models. I think it’s important for deaf girls and young women to have someone to look up to if they aspire to study computing and data science.

Data you Ixone (1)

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