Diversity in AI

About

The Diversity in AI Event is organized as a side event of Northern Lights Deep Learning Conference (NLDL). NLDL is a conference organized by SFI Visual Intelligence, hosted by UiT The Arctic University of Norway. The Diversity in AI event is organized by PhD fellows from the Visual Intelligence Graduate School in collaboration with NORA.ai (Norwegian Artificial Intelligence Research Consortium).

In Norway, public institutions, academia, and industry should actively strive to improve diversity and increase the visibility of underrepresented groups within the field of AI and tech. In previous iterations of our event, we have emphasized the importance of diversity by involving a wide range of voices and experiences from underrepresented groups within the AI and tech fields in Norway.

This year, we are shifting our focus to explore a critical issue under the theme "AI As a Mirror to Society." Our aim is to delve deeper into how AI can inadvertently reflect and perpetuate societal biases, and how misinformation or flawed data can lead to biased AI systems. We will discuss what effects biased AI has on all of us, and how it can amplify bias for underrepresented groups who have been subjected to biases historically, in particular. This exploration is crucial for developing fair and effective technologies that do not disproportionately disadvantage or favour specific groups.

During this Diversity in AI segment, we will bring together an interdisciplinary and diverse group of experts in AI, AI governance and policy, and law to address the disparities in AI in Norway and the world today, the dangers that come with it, and discuss initiatives to ensure inclusivity and diversity in AI for the future.

Alex Moltzau

European AI Office of the European Commission

The EU AI Act, Its Limitations and Possibilities for Diversity

Eva Breznik

KTH Royal Institute of Technology, Sweden

Keynote title coming soon

Artem Galushko

UiT The Arctic University of Norway

Legal discourse and Examples of Amplifying Biases against Minorities and Historically Marginalized Groups

Panel Discussion

Alex Moltzau

European AI Office of the European Commission

Eva Breznik

KTH Royal Institute of Technology, Sweden

Artem Galushko

UiT The Arctic University of Norway

Elisabeth Wetzer (Moderator)

UiT The Arctic University of Norway

Program

Date: January 6th, 2026

Time: 15:15 - 17:15 CET

Place: UiT, The Arctic University of Norway
Program:

15:15 - 16:15: Presentations
16:15 - 17:15: Panel discussion

Keynote abstracts

The EU AI Act, Its Limitations and Possibilities for Diversity

After setting up the European AI Office during the last few years, and working directly to shape regulation in Brussels, there are some stark contrasts that were observed. The geopolitics of how regulations are realised, the limitations of product safety legislation, and the possibilities therein to shape the future.

How can we shape a future where respect for diversity, and the conditions for recognising diversity, becomes a priority? Can the EU AI Act contribute to increased diversity, and helps us navigate the increasing saturation of AI products and services in a safe way for different groups of people? These are questions that will be discussed, as the EU AI Act moves from a European project to national and regional implementation. How can we bridge a path from product safety legislation towards meaningful social change? What are the limitations to the EU AI Act, and what possibilities are provided in the framework that can be considered for diversity?

Alex Moltzau

Eva Breznik

Keynote title coming soon

Keynote description to be announced

Legal discourse and Examples of Amplifying Biases against Minorities and Historically Marginalized Groups

My short presentation will address diversity in legal discourse. To discuss potential risks and challenges posed by AI for diversity, I will demonstrate how AI tools can be misused to discriminate against historically marginalised groups, including ethnic, religious, and other minorities.

A preliminary observation is that language, origin, previous police record, as well as political and social activities, are among the key factors that make minorities particularly vulnerable to discrimination.

The key question in this context is whether examples of biased AI systems can inform the future development of fair technologies that do not disadvantage or favour specific groups.

Artem Galushko

Google Sites

Report abuse