Modern technology can nowadays be found everywhere. Computers take their knowledge from past data and make predictions about the future with the help of algorithms. So far so good, isn’t it? Diversity of data and its evaluation play an important role for success. Therefor 6 women at A1 have decided to share their enthusiasm for this topic. More about that in this A1 story!

A life without Artificial Intelligence (AI) is impossible to imagine. Already in our daily lives we encounter robotic vacuum cleaners and our phones sort pictures by people’s faces. In our free time we’re not just running against time, we’re also tracking our route, we compete against like-minded people, and some hope to find these through various dating platforms. And in our career? There for example, our Chatbot Kara assists our customers with answers or we proactively offer the right products. The myth »Advanced Analytics and Artificial Intelligence does not concern me« has long become obsolete.

More diversity!

A »Women’s only« edition about »Data analysis and AI« started this July with similarly striking examples, in which Michaela Barta-Müller and Simone Scholz tried to soothe the dread of 20 interested colleagues about the seemingly complex topic (successfully!) and illustrate possibly unexpected connection points with artificial intelligence in our career. An important part of the workshop was the importance of AI when it comes to correcting blind spots or highlighting the disadvantage of underrepresented groups. At A1 Austria, data scientist Donatella is responsible for the diversity perspective and sees to it, that the »ingredients« we feed our database are balanced. Our AAA Role Models are furthermore a diverse team, due to their experiences, education and special interests.

Algorithms are like a recipe

For a striking comparison, Michaela will swap the computer with a cooking-apron and explain understandably:

»Imagine a recipe – the ingredients are data/influencing factors, the amount defines the mixing relation and amount of influence and you cook based on a formula. A recipe like this can be easy if you cook scrambled eggs for example (easy analysis and models like linear regression) or complicated like a haute cuisine meal (from complex models to neuronal networks). If you use high quality ingredients, then you can see their impact in the result – food like evaluation – depending on the skills of the cook or datascientist. Oh and sometimes easy models are just as good as complicated ones – just like with food, after all many people prefer schnitzel to a meal from the fusion kitchen.«

Those, who know nothing, have to believe everything

The list of buzzwords concerning this topic is long and strikes some before they even get close to the center. Especially with sensible topics that are publicly effective, like movement data, there is a necessity of elucidation on how important it is to ask, to inform yourself and rather to stay away from clichés, than to perpetuate them. The participants of the workshop shared their experiences regarding prejudices:


»Sadly, it is still conveyed to girls that they’re not competent enough for STEM fields.

…However, my young daughter (primary school age) is still very curious and thinks it’s great that I bought her the BBC Micro Bit V2 so she can learn the basics of Coding.« – Waltraud

»My daughter (5 years old) loves dinosaurs, the colors blue and green, cars and all these other stereotypically »boy« things…

…she’s already mad about the packaging of her toys only displaying boys and never girls.« – Sandra

Reversed experiences also show that the road to an open mindset is still long:

»My son is 3 and loves dolls, stuffed animals, the color pink and has long hair.

…Not only that every stranger addresses him as a girl, others will also suggest that he has to play with cars and tractors, be interested in technology and become an engineer – dolls are for girls. Will that ever change?« – Marlies

Our A1 role models are on a mission to not scare others with »Advanced Analytics & Artificial Intelligence« but to clarify, to build curiosity and enthusiasm for the topic. More women in the technical job field, like data analysis, will not only foster a gender equal balance in data weighting, but also in our company and provide a positive contribution to our ESG goals.