So far, we’ve interviewed 22 people and transcribed and analyzed the data from 19 of them. Because the libraries (machine learning models) perform best on English material, we translated the Dutch interviews into English. The transcripts were then converted into the format required by a machine learning open-source text generator that we discovered. This text-generator uses Keras, a Python-based deep learning API built on top of TensorFlow’s machine learning framework. We also anonymised the data by assigning a unique code to each statement and categorizing the sentences by question. This makes it easier to see how the sentence (response) came to be in the first place.
We ran a light pre-analysis before executing the code, looking at the quotes and categorizing them into insights, and looking for a relationship between the data. The father as a role model and influencer in career choices, overall bias and prejudice on family planning, prejudice concerning underestimating, participants’ opinion on a quota, working in a diverse team, and message to young women were among the themes that aligned. Overall, it appears that a young woman’s job path is heavily influenced by her father. The father ‘opened their eyes,’ introduced technology into their lives, or encouraged them to seek a profession in technology because they believe it is a good fit for their skills. We also see that the majority of women encounter various forms of bias, whether it is related to family planning, remarks about whether or not they plan to have children, or how their work would be affected. Another trend we noticed was underestimating. The majority were either underestimated and had to prove themselves capable first or if they were capable at all. Working in a diverse environment, according to the majority, is the ‘best’ since it changes the ‘dynamic,’ ‘atmosphere,’ and ‘culture,’ as well as adding a ‘creative approach.’ When we asked the women what they thought of the quota, the general reaction was critical, mostly because it makes you a “diversity hire,” that people should be “hired based on their skills,” and that it is an “artificial thing” to impose. Companies, on the other hand, require that push to ‘break some things’ and ‘reset’. ‘People tend to hire people who look like them,’ said a few, and the quota changes this. Finally, the majority of women urge other women to pursue careers in tech and to “just do it!”, the industry is diverse and offers many opportunities. We’ve also heard that women prefer working with men and that doing so is ‘fun.’
We next ran the algorithm, training the model on our data and asking it the same questions we asked the participants. The model’s generated text appeared to us to be quite familiar at first glance. We chose a few quotes from the generated text. We didn’t change any words or the structure of a sentence in any way. Only punctuation marks were added to a sentence. Eventually, we will compile all of the quotes into a single story and present it in a virtual reality (web) setting.