Artificial Intelligence (AI) & Emerging Technologies
Chat GPT, Generative AI, and Machine Learning
Speaker 1: Christian von Essen, Private Sector, Western European and Others Group (WEOG)
Speaker 2: Jenna Manhau Fung, Technical Community, Asia-Pacific Group
Speaker 3: Neema Iyer, Private Sector, African Group
Speaker 4: Luciana Benotti, Civil Society, Latin American and Caribbean Group (GRULAC)
Speaker 5: Lucia Russo, Intergovernmental Organization, Intergovernmental Organization
Takeshi Komoto, Private Sector, Asia-Pacific Group
Jim Prendergast, Private Sector, Western European and Others Group (WEOG)
Samantha Dickinson, Technical Community, Western European and Others Group (WEOG)
Round Table - 90 Min
How will AI be trained to recognize and desexualize content, and what safeguards might be put in place to ensure that algorithms are not biased or discriminatory? What measures are needed to protect freedom of speech and prevent censorship? How will search engines ensure that the desexualization of content does not inadvertently perpetuate harmful gender stereotypes or reinforce existing power imbalances?
What will participants gain from attending this session? Participants can expect to learn about the latest advancements in the field of AI and machine learning, including how AI bias is being identified and mitigated, as presented by industry, academia, and international organization experts. They will also learn about mitigation measures for AI and machine learning techniques that will help prevent bias in search results. International norms and standards and academic research on inclusive AI will be discussed. We will discuss the importance of transparency in search algorithms and how the use of AI and machine learning can help to increase transparency and provide more reliable results. Participants will have the opportunity to ask questions and engage in discussions which will hopefully lead to a deeper understanding of how AI and machine learning are revolutionizing the world of search engines and the impact this can have on businesses and consumers alike.
AI algorithms are trained using data that may reflect various kinds of bias, including gender bias or other forms of discrimination. Algorithms may replicate those biases in their outputs and recommendations, leading to AI systems that perpetuate harmful gender stereotypes and discriminate against women, people of color, and other marginalized groups. Such biases can include oversexualization of search results, including exposing children to inappropriate content. Addressing these concerns is of great importance. To address these biases, generative AI systems can be trained to identify and correct them. This can be achieved by diversifying the training data, incorporating ethical principles, continuously updating and refining the algorithm, and providing transparency. Proposed Agenda Welcome and Session Goals - Takeshi Komoto (Google) Opening Remarks Neema Iyer (Pollicy) on her research into Women and AI Lucia Russo/Celine Caira (OECD AI Observatory) on the OECD AI Principles and ways to ensure AI plays a positive role in closing the gender gap. Luciana Benotti (Universidad Nacional de Córdoba) on her research into language model strengths, errors, biases, and other weaknesses, particularly in non-English languages Christian von Essen (Google) on how Google engineers identified biases and reversed a trend of showing oversexualized search results. Interactive Discussion Moderator: Takeshi Komoto (Google) Panelists, including Jenna Fung (Asia Pacific Youth IGF) and the audience explore potential uses for this type of AI training and the key policy questions. Conclusion Quick summaries and a discussion on potential paths forward.
We place great importance on making this an interactive session, and will follow the presentation with an audience dialogue to gather insights on the effectiveness leveraging AI to counter bias, including gender bias. We hope this discussion will provide potential avenues to explore in follow-up work.
Hybrid Format: All participants, physically or virtual, will be required to log into Zoom, so we can manage the queue in a neutral manner. Our onsite and online moderators will work together closely to ensure that questions and comments from both groups are addressed. We recognize the unique challenges that remote participants may face, such as time zone differences, technical limitations, and differences in communication styles. To address these challenges, we will encourage our speakers to use clear and concise language, avoid technical jargon, and provide contextual information during the session. Furthermore, we will explore the use of polling tools such as Mentimeter or Poll Everywhere to gather feedback and questions from both onsite and online participants in real-time. By taking these steps, we aim to create an inclusive and engaging environment that caters to the needs of all participants, regardless of their location or mode of participation.