Learning the Importance of AI Auditing and Risk Management: A Report on a Fascinating Lecture
This is Johann from the Global Engineering Department of the GLB Division. I wrote an article summarizing the content of the session based on reports from Mr. Ichimura participating in Data + AI SUMMIT2023 (DAIS).
Today, I would like to introduce a very interesting lecture for those who are interested in AI technology, AI system developers, and auditors. The title of the lecture is "De-Risking Language Models for Faster Adoption," and the presenter is the Chief Scientist of VMA and VNH's AR, who is also a visiting policy member at a business school. The theme and purpose of this lecture are to explain AI auditing and risk management and to promote the safe, reliable, and unbiased development of AI systems.
This blog is composed of one part, and this is the first part. Now, let's take a look at the content of the lecture.
The Importance of Risk Management in AI Systems
As AI technology rapidly advances and deeply permeates our daily lives and businesses, the importance of AI auditing and risk management is increasingly growing. In this lecture, methods to promote the safety, reliability, and unbiased development of AI systems were discussed.
Choosing Standards and Considering the Supply Chain
When developing AI systems, it is essential to select appropriate standards and consider the supply chain. By choosing standards, you can ensure the quality and safety of the system and reduce risks through the supply chain.
Adopting an Objective Adversarial Mindset
In managing the risks of AI systems, it is crucial to have an objective adversarial mindset. This allows you to identify vulnerabilities and potential issues in the system and take corrective measures.
Reviewing Past Known Incidents
By reviewing past known incidents, you can take measures to prevent similar problems from recurring. Additionally, learning from past incidents allows you to predict new risks and prevent them from happening.
Utilizing Data Quality
Data quality significantly affects the performance and reliability of AI systems. By using high-quality data, you can reduce the risks of the system and develop a safer and more reliable system.
Involving Stakeholders
In the development of AI systems, it is essential to involve stakeholders. By incorporating the opinions and demands of stakeholders, you can reduce the risks of the system and develop a safer and more reliable system.
Latest Concepts, Features, and Services
In this lecture, the latest concepts, features, and services were also discussed. By incorporating these topics, you can more effectively manage the risks of AI systems.
Cutting-Edge Technologies for AI Auditing and Risk Management
By utilizing the latest technologies, you can more effectively audit and manage the risks of AI systems. For example, there are technologies to improve the interpretability of machine learning models and to detect and correct biases in datasets.
Leveraging AI Services
By leveraging AI services, you can efficiently manage risks and audits. For instance, by using cloud-based AI services, you can develop high-quality AI systems while saving resources and costs. Understanding the importance of AI auditing and risk management and taking appropriate measures will enable you to develop safer and more reliable AI systems and contribute to society. It is essential to continue managing the risks of AI systems by utilizing the latest technologies and services.
Summary
In this lecture, we were able to learn about the importance of AI auditing and risk management and the appropriate measures to take. Many factors, such as choosing standards, considering the supply chain, adopting an objective adversarial mindset, reviewing past known incidents, utilizing data quality, and involving stakeholders, affect the safety and reliability of AI systems. By incorporating the latest concepts, features, and services, you can effectively manage risks and audits and achieve rapid adoption of AI systems. Let's continue to apply this knowledge and strive to develop safe and reliable AI systems.
Conclusion
This content based on reports from members on site participating in DAIS sessions. During the DAIS period, articles related to the sessions will be posted on the special site below, so please take a look.
Translated by Johann
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