Session
Youth Coalition on Internet Governance
Umut Pajaro Velasquez, Malmö Universitet, Academia, WEOG Denise Leal Machado, Youth Coalition on Internet Governance, Civil Society, GRULAC
Umut Pajaro Velasquez, Malmö Universitet, Academia, WEOG
Denise Leal Machado
Umut Pajaro Velasquez
5. Gender Equality
10. Reduced Inequalities
16. Peace, Justice and Strong Institutions
Targets: The proposal connects to SDGs 5, 10, and 16 in the following ways: SDG 5: Gender Equality :Focus on gender lens: The proposal emphasizes exploring open data and collaborative construction with a gender lens. This directly addresses the need to consider gender biases when building AI models, which can perpetuate existing inequalities (SDG 5.1). SDG 10: Reduced Inequalities: Mitigating bias in LLMs: By using open data and including diverse perspectives, the proposal aims to create less biased deep language models (LLMs). This can help reduce inequalities in areas like job opportunities and access to information, which can be affected by biased AI (SDG 10.2, 10.3). SDG 16: Peace, Justice and Strong Institutions: Transparency and accessibility: The proposal highlights the importance of open data, making the construction process of LLMs more transparent and fostering trust (SDG 16.10). Collaborative construction: The collaborative approach promotes inclusive decision-making and strengthens institutions by involving diverse voices (SSDG 16.7).
Roundtable
We will explore the synergy between open data and collaborative construction with a gender lens, unraveling how this combination becomes a fundamental catalyst for the creation of more equitable and bias-free deep language models. We will take a close look at how the transparency and accessibility of open data, along with the active inclusion of gender perspectives in its construction, contribute significantly to mitigating inherent biases in LLMs. Likewise, we will see how the use of practical strategies demonstrate how this approach not only addresses crucial challenges, but also drives innovation towards a future where NLPs and LLMs more accurately and fairly reflect the diversity of our society. In conclusion, we propose a path towards collaboratively building a more inclusive and equitable technological future.
The session will be a dynamic exchange of online and onsite participants about open data, collaborative development, and gender inclusivity. We'll explore how this combination can be harnessed to create unbiased deep language models (LLMs). Through discussion and practical examples, on Zoom room discussions and groups onsite, we'll delve into how open data's transparency and a gendered perspective in its construction can combat bias in LLMs. This interactive session will showcase how this approach tackles challenges and fosters innovation for NLPs and LLMs that truly reflect our society's diversity. We'll conclude by proposing a collaborative path towards a more equitable and inclusive technological future.