Governance Labs
Responses
In your opinion, what outcomes would make the first Global Dialogue on AI Governance a success?
identification of key factors that are impeding AI Governance Processes and the challenge related to so many completing AI products for evaluation (costs, timelines, expertise requirements etc.)
From your perspective, which of the following thematic areas identified by the General Assembly Resolution 79/325 for the AI Dialogue reflect your priorities for urgent action and active engagement?
- Safe, secure and trustworthy AI
- Interoperability of governance approaches
- Social, economic, ethical, cultural, linguistic and technical implications of AI
Please briefly explain your selection.
2
These are closely interrelated issues as safe and secure AI must be done consistently and include proper metrics with demographic and other (eg. Linguistic) analysis, otherwise AIs accuracy and hence value is inconsistent.
In your opinion, are there any cross-cutting or emerging issues not captured by the listed themes above? If so, please explain.
1
Industry providers of AI technologies are generally giving lip-service to AI Governance from an evaluation perspective. Perhaps due to lack of knowledge and expertise in the evaluation of AI, these firms provide incomplete assessments of the tools they provide or have very inconsistent use of metrics and analysis methods (eg. not doing demographic bias assessments). As buyers of AI are generally not experts in AI evaluation, nor do they have the time and money to do all the assessments themselves, there is a gap between what is needed (complete, accurate evaluations) and what is offered by vendors making both the use of AI being riskier and less likely to happen.
How are the governance gaps and related developments/advances in the thematic areas you selected above affecting your country, region, or sector? Please highlight the most significant challenges.
Industry providers of AI technologies are generally giving lip-service to AI Governance from an evaluation perspective. Perhaps due to lack of knowledge and expertise in the evaluation of AI, these firms provide incomplete assessments of the tools they provide or have very inconsistent use of metrics and analysis methods (eg. not doing demographic bias assessments). As buyers of AI are generally not experts in AI evaluation, nor do they have the time and money to do all the assessments themselves, there is a gap between what is needed (complete, accurate evaluations) and what is offered by vendors making both the use of AI being riskier and less likely to happen.
What role can the AI Dialogue play in advancing international cooperation on AI governance?
Build standards bodies (eg like CHAI.org) to set global standards for use-case specific metrics and evaluation processes.
What are some of the existing initiatives, partnerships, or mechanisms that the AI Dialogue should build upon or connect with, and what added value could the AI Dialogue bring?
CHAI.org is a good start. What we are doing at Governance Labs a layer on top of that.
How can different stakeholders contribute to the AI Dialogue? Please share recommendations for the format and structure of the AI Dialogue.
Each needs to bring in their expertise, but also their concerns.
Which voices, communities, or perspectives are currently underrepresented in global discussions on AI governance? How could they be included?
Evaluation (QA) expertise is often sidelined. They need to be part of the process of guardrails on AI, not only considered an expense but as a key value to the organization.
What innovative engagement formats could most effectively foster meaningful and dynamic engagement during the AI Dialogue?
workshops and seminars.
Please share examples of policies, practices, platforms, or approaches that promote effective AI governance or offer concrete solutions to addressing its challenges.
1
A simple one is to always require demographic bias analysis of the accuracy results for any AI to uses human information or impacts a human in any way.