What is AI Governance?
AI governance is essential for managing rapid advancements in AI technology, particularly with the emergence of generative AI. Generative AI, which includes technologies capable of creating new content and solutions, such as text, images and code, has vast potential across many use cases.
From enhancing creative processes in design and media to automating tasks in software development, generative AI is transforming how industries operate. However, with its broad applicability comes the need for robust AI governance.
The principles of responsible AI governance are essential for organizations to safeguard themselves and their customers. These principles can guide organizations in the ethical development and application of AI technologies, which include:
In late 2023, The White House issued an executive order to help ensure AI safety and security. This comprehensive strategy provides a framework for establishing new standards to manage the risks inherent in AI technology. The US government's new AI safety and security standards exemplify how governments approach this highly sensitive issue.
AI safety and security: Mandates developers of powerful AI systems to share safety test results and critical information with the US government. It requires the development of standards, tools and tests to help ensure AI systems are safe and trustworthy.
Privacy protection: Prioritizes developing and using privacy-preserving techniques and strengthens privacy-preserving research and technologies. It also sets guidelines for federal agencies to evaluate the effectiveness of privacy-preserving techniques.
Equity and civil rights: Prevents AI from exacerbating discrimination and biases in various sectors. This includes guiding landlords and federal programs, addresses algorithmic discrimination and helps to ensure fairness in the criminal justice system.
Consumer, patient and student protection: Helps advance responsible AI in healthcare and education, such as developing life-saving drugs and supporting AI-enabled educational tools.
Worker support: Develops principles to mitigate AI's harmful effects on jobs and workplaces, including addressing job displacement and workplace equity.
Promoting innovation and competition: Catalyzes AI research across the US, encourages a fair and competitive AI ecosystem and facilitates the entry of skilled AI professionals into the US.
Global leadership in AI: Expands international collaboration on AI and promotes the development and implementation of vital AI standards with international partners.
Government use of AI: Helps ensure responsible government deployment of AI by issuing guidance for agencies' use of AI, improving AI procurement and accelerating the hiring of AI professionals.
While regulations and market forces standardize many governance metrics, organizations must still determine how to best balance measures for their business. Measuring AI governance effectiveness can vary by organization; each organization must decide what focus areas they must prioritize. With focus areas such as data quality, model security, cost-value analysis, bias monitoring, individual accountability, continuous auditing and adaptability to adjust depending on the organization's domain, it is not a one-size-fits-all decision.