Session
Organizer 1: Private Sector, Western European and Others Group (WEOG)
Speaker 1: Gerlinde Weger, Private Sector, Western European and Others Group (WEOG)
Speaker 2: Alpo Varri, Technical Community, Western European and Others Group (WEOG)
Speaker 3: Patricia Shaw, Private Sector, Western European and Others Group (WEOG)
Speaker 4: Allison Gardner, Government, Western European and Others Group (WEOG)
Speaker 2: Alpo Varri, Technical Community, Western European and Others Group (WEOG)
Speaker 3: Patricia Shaw, Private Sector, Western European and Others Group (WEOG)
Speaker 4: Allison Gardner, Government, Western European and Others Group (WEOG)
Format
Roundtable
Duration (minutes): 60
Format description: The session will consist of two halves of thirty minutes each. During the first 30min, there will be an initial introduction to the overall topic of context dependent AI governance requirements followed by an overview of the IEEE 7003-2024 standards and the way it captures context dependence in managing AI Bias. The second half of the session will focus on use cases provided by the audience to jointly explore how AI Bias concerns in those use cases can be addressed through the process detailed in the IEEE 7003-2024 standard.
Duration (minutes): 60
Format description: The session will consist of two halves of thirty minutes each. During the first 30min, there will be an initial introduction to the overall topic of context dependent AI governance requirements followed by an overview of the IEEE 7003-2024 standards and the way it captures context dependence in managing AI Bias. The second half of the session will focus on use cases provided by the audience to jointly explore how AI Bias concerns in those use cases can be addressed through the process detailed in the IEEE 7003-2024 standard.
Policy Question(s)
1. How can AI policies address context dependent requirements for effective AI governance?
2. How can AI governance frameworks adapt to the unique context of stakeholders from the Global South?
3. How can AI governance standards help to reduce the global digital divide?
What will participants gain from attending this session? Participants will engage in a practical discussion about contextual factors that shape decisions around AI Governance approaches. The discussion will be framed around use cases provided by the participants.
Participants will gain familiarity with he core concepts of the IEEE 7003-2024 standard for Algorithmic Bias Considerations, providing them with practical insights into understanding the many contextual dimensions of AI Bias and ways to practically deal with them across the various stages of an AI system’s lifecycle.
The use cases based examples of addressing AI Bias concerns will also provide participants with a foundation for assessing context factors in a broad range of AI governance issues, including concerns around explainability, robustness, human oversight and more.
SDGs
Description:
In this workshop we will discuss AI Governance considerations through a range of context lenses, highlighting the need for approaches capable of being tailored to the context in which the AI systems is developed and used. Contextual factors may including, among others: • the type and level of risk to individuals, organizations or society that a potential failure of an AI system could pose; • the existence of non-AI related safeguards that are already in place in regulated or otherwise controlled application domains; • cultural risk appetite and expectations regarding the role of government and market drivers in shaping the use of emerging technologies; Following a brief introduction to the contextual factors that may shape AI governance needs, the workshop will present the IEEE 7003-2024 Algorithmic Bias Considerations standard as an example of a context adaptable AI governance tool. Bias in AI systems is recognized as one of the key risks associated with the use of AI in decision making scenarios, with potential repercussions to individuals and society but also to the successful execution of the task that an organization wants the AI to perform. IEEE 7003-2024 recognizes the context dependent nature of AI Bias and provides a process for managing bias concerns throughout the life-cycle of AI systems. The second half of the workshop will explore the use of the IEEE 7003-2024 standard through an interactive discussion with the audience. During this discussion we will outline how the standard can support Algorithmic Bias Considerations in contexts based on audience supplied use cases.
In this workshop we will discuss AI Governance considerations through a range of context lenses, highlighting the need for approaches capable of being tailored to the context in which the AI systems is developed and used. Contextual factors may including, among others: • the type and level of risk to individuals, organizations or society that a potential failure of an AI system could pose; • the existence of non-AI related safeguards that are already in place in regulated or otherwise controlled application domains; • cultural risk appetite and expectations regarding the role of government and market drivers in shaping the use of emerging technologies; Following a brief introduction to the contextual factors that may shape AI governance needs, the workshop will present the IEEE 7003-2024 Algorithmic Bias Considerations standard as an example of a context adaptable AI governance tool. Bias in AI systems is recognized as one of the key risks associated with the use of AI in decision making scenarios, with potential repercussions to individuals and society but also to the successful execution of the task that an organization wants the AI to perform. IEEE 7003-2024 recognizes the context dependent nature of AI Bias and provides a process for managing bias concerns throughout the life-cycle of AI systems. The second half of the workshop will explore the use of the IEEE 7003-2024 standard through an interactive discussion with the audience. During this discussion we will outline how the standard can support Algorithmic Bias Considerations in contexts based on audience supplied use cases.
Expected Outcomes
The exchange between panel and audience during this workshop will provide the participants with practical insights to help them to better understand the context related aspects of AI governance.
Participants will gain a practical understanding of how the IEEE 7003-2024 standard for Algorithmic Bias Considerations can be applied to specific AI system use cases to manage concerns about AI Bias. These examples will also provide participants with insights on understanding context related challenges for other AI governance aspects.
Participants will have the opportunity to participate in future case studies developments around the application of IEEE 7003-2024 for addressing AI Bias concerns.
Hybrid Format: Prior to the session organizers will invite participants to submit descriptions of AI system use cases that they would like to have discussed during the session.
A preparation call will be organised for all speakers, moderators and co-organisers so that everyone has the chance to meet and prepare for the session.
During the session the onsite and online moderators will merge onsite and online attendees. Onsite participants will be encouraged to connect to the online platform to stay informed and engage with discussions in the chat.