Leandra Bräuninger (she/her or they/them) Freelance Researcher for a project by UCL, Genomics England and the Alan
What does your job actually entail?
I am working on a literature review into Statistical Methods on Fairness in Genomics and Healthcare. What that means is that we want to find out what research has been published on Stats/Machine Learning/AI tools that aim at detecting and correcting inequities in Genomics and Health applications. For that I (try to) systematically scour the internet for papers published on the topic, read them and if they are relevant add them to our collection. The main difficulty is limiting ourselves, as there are so many different ways to approach this topic.
We are just starting out with this project, so currently I am spending most of my time organising a workshop series with expert panel discussions which will inform our literature review.
When not at work you can be found...
Tattooing! What started out as a hobby has now become a full blown second job. It allows me to express myself creatively, which is a great balance for all the sciencing I otherwise do.
Why did you join the DSxHE community?
My career path has always been incredibly interdisciplinary but a theme that started to emerge was using data and technology to help make our systems work for everyone. I am particularly interested in Biology and Health and have found that inequities are even more pronounced in this area, which is why I wanted to focus on this field. I have spent the last couple of years trying to explain to people why inequity in health AI matters, but when I found DSxHE there finally was an entire room full of people who not only understood that fact but shared an urgency about changing it for the better.
What should people reach out to you for help on/with?
I have a lot of experience in working on interdisciplinary projects and the specific challenges that come with it. Sometimes a bit of “translation” is needed between fields in order to work together – I can be your translator for that.
Also, having worked in multiple digital health start-ups, while by no means an expert, I am more than happy to share my experience and insights into any applied and entrepreneurial projects.
What would like help with from members of the DSxHE community?
Mainly, I would like to find out about relevant people, projects and events in the field of machine learning for health equity. With the Statistical Methods Theme of DSxHE, we are also creating a landscape of people working in this field on which we would love your input! Come join our Slack via the DSxHE website if you’re interested!
I’m also keen to make sure my statistical work remains relevant to real-world applications, so if you are working on a clinical project (e.g. on disease diagnostics) and would like to collaborate to make it equitable, don’t hesitate to reach out.
What’s your interest in data science and/or health equity?
We live in an era of unprecedented amounts of data – let’s make sure we utilise it in a way that helps all of us.
What’s a topic in data science/health equity that you know/care a lot about - why is it important/interesting, tell us about it!
In each of the steps involved in creating a Machine Learning model for a health-related question, social bias and inequity can creep inside. Most of the time that is not due to malevolent intent and a false belief that mathematical tools are value-neutral. But the human decision maker’s biases – and especially the unconscious ones – still consistently seep into models. Hence, it’s all the more important to create rigorous models where equity is prioritised.
What is your last Google search?
“Elvis teeth” … ?!
What was your last impulse buy?
A second hand book on cooking theory
What is not a big deal to most people but is torture to you?
Socks. I hate them.
Right now, what is the most pivotal relationship in your life?
My group of best friends. Even though we are in spread across multiple countries, the love, support and celebration of each other we share allows me to do work I love and grow in all kinds of ways.
What is humanity’s most redeeming quality?