Context
∙ The AI Governance Alliance (AIGA) released a series of three new reports on advanced artificial intelligence (AI).
About:
∙ The papers focus on generative AI governance, unlocking its value and a framework for responsible AI development and deployment.
∙ In the report “Generative AI Governance: Shaping Our Collective Global Future,” the highlight is on international cooperation.
∙ It also urges a more inclusive access to AI – both in terms of development and deployment.
∙ Unlocking Value from Generative AI: Guidance for Responsible Transformation guides stakeholders on how to adopt generative AI more responsibly.
∙ Particularly, it highlights use case evaluation, multistakeholder governance and transparent communication.
∙ The Presidio AI Framework: Towards Safe Generative AI Model underscores the need for a framework that standardizes model lifecycle management.
∙ It also focuses on shared responsibility and proactive risk management.
ο AI Governance Alliance (AIGA)The World Economic Forum launched the AI Governance Alliance in 2023.
∙ It is a dedicated initiative focused on responsible generative artificial intelligence (AI).
∙ It is a union of industry leaders, governments, academic institutions, and civil society organizations to champion responsible global design and release of transparent and inclusive AI systems.
What is Artificial Intelligence?
∙ Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.
∙ Artificial intelligence allows machines to model, or even improve upon, the capabilities of the human mind.
∙ From the development of self-driving cars to the proliferation of generative AI tools like ChatGPT and Google’s Bard, AI is increasingly becoming part of everyday life — and an area every industry are investing in.
Generative AI
∙ Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.
∙ Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.
∙ ChatGPT, DALL-E, and Bard are examples of generative AI applications that produce text or images based on user-given prompts or dialogue.
Need for the Regulation
∙ Lack of transparency of AI tools: AI and deep learning models can be difficult to understand, even for those that work directly with the technology.
∙ AI is not neutral: AI-based decisions are susceptible to inaccuracies, discriminatory outcomes, embedded or inserted bias.
∙ Manipulation through Algorithm: Online media and news have become even murkier in light of
ο AI-generated images and videos, AI voice changers as well as deep fakes infiltrating political and social spheres.
∙ Lack of Data Privacy: AI systems often collect personal data to customize user experiences or to help train the AI models.
∙ Uncontrollable Self AI: There also comes a worry that AI will progress in intelligence so rapidly that it will act beyond humans’ control — possibly in a malicious manner.
∙ Safety and Security: AI systems, especially those in critical domains like healthcare, transportation, and finance, must meet certain safety standards.
∙ International Cooperation: AI development is a global phenomenon, and regulatory frameworks can help establish common standards and principles.
∙ Avoiding Misuse: Without regulations, there is a risk of AI being used for malicious purposes, such as deepfake creation, cyber attacks, or autonomous weapons.
∙ Public Trust: Establishing clear regulations can enhance public trust in AI technologies.
Way Ahead
∙ AI systems can raise ethical issues, such as bias, discrimination, and invasion of privacy.
∙ Regulations are necessary to ensure that AI technologies adhere to ethical standards and do not contribute to social inequalities.
∙ These dangers may be mitigated by implementing legal regulations and by guiding AI development with human-centered thinking.
