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Intersectionality in ML

Updated: Nov 3, 2023

An experience report of our workshop on Exploring Ethical AI Guidelines and Establishing Individual Inclusive Practices together with QualityMinds

As AI continues to shape our world, there are several reasons why companies must focus on developing their own standards right now.


Beyond the immediate need to safeguard reputation and engender trust, companies must be attuned to the evolving regulatory environment and the imperative to mitigate biases and discrimination.


However, the significance goes deeper, extending to the profound impact a company can make on shaping a positive and equitable future for all!


This is why we want to share some of the key insights we gained from our Workshop on Exploring Ethical AI Guidelines and Establishing Individual Inclusive Practices with QualityMinds.




Namrata Gurung, PhD is Senior Data Scientist (ML/MLP) at QualityMinds. She holds a PhD in physics. Since two years, Namrata works as an ambassador for Women in Data Science (WiDS) in Zurich. She clearly bridges the gap between Tech and Social Impact.


We learnt about the importance of our everyday decisions and contributions when it comes to ethical AI which in fact lies at the intersectionality of departments and various stages of a product.

Namrata, what do you think about this workshop?


QualityMinds GmbH (QM) works at the technological frontier of building AI applications, software development, AI and software testing, and creating learning modules for the most advanced topics for a variety of clients.


Thus, it was important to become aware of ethical aspects of the rapidly evolving technologies that would be used on a regular basis, as our work involves AI at different levels.


And who was involved?


The workshop involved participants from several backgrounds within QM, including R&D Team, and Quality Learning team, consisting of a mix of ML engineers, data scientists, dev ops specialists, product owners, software developers, UX/UI designers, testing community, and learning coaches.


What was your concrete output, Namrata?


Our conclusion and take away from the 2-part workshop, was to start working on a QM AI Ethics toolkit, that would align with QM values, ethical AI guidelines, and what it would mean for different stages of an AI product/ consultation.


For instance, it could mean hosting ethical AI awareness workshops, discussion on the implementation of ethical and inclusivity aspects at the very beginning before building a product, including ethical grounds during AI-testing, and more.


So what is your overall conclusion?


Through engaging discussions with our different backgrounds, we were able to disentangle the ethical, fairness, and bias aspects of AI, at different levels.


Going beyond awareness, we also investigated aspects of what we could implement as a company, at different stages and environments, to make sure that we implement ethical AI, whether it be building, testing, or imparting awareness of ethical AI applications.


Thank you for this interview, Nam!



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