AI’s ability to increase productivity and creativity has revolutionized a wide range of industries. However, effective governance is required as AI systems become more and more integrated into daily life. AI governance refers to the frameworks, rules, and policies that regulate the creation and responsible use of AI technologies. This guide will look at what is AI governance, why it’s important, and how it’s being used in different industries.
AI Governance | Definition
The term artificial intelligence (AI) governance refers to the rules that ensure AI systems and tools are and will remain morally and safely sound. It develops frameworks, guidelines, and standards to guide AI research, development, and application, ensuring safety, justice, and the preservation of human rights.
Why is AI governance important?
When machine learning algorithms are used for decision-making, AI governance becomes imperative. Biases in machine learning, especially when it comes to racial profiling, can lead to the incorrect identification of basic user data. This may lead to unfair denials of loans and healthcare to certain people, as well as deceiving law enforcement into believing certain people are criminal suspects. The best course of action in situations where AI-based decisions may be unfair or violate human rights is determined by AI governance.
Concerns about AI ethics, transparency, and compliance with other laws, such as the General Data Protection Regulation, have been raised by the industries’ rapid adoption of AI tools, systems, and technologies. If artificial intelligence (AI) systems are not properly governed, they have the potential to make biased decisions, violate privacy, and mishandle data. AI governance aims to protect users’ rights and prevent harm while encouraging the beneficial use of AI technologies.
AI Governance Pillars
- Innovation: supporting initiatives in science and business to maximize the advantages of artificial intelligence.
- Trustworthy AI: ensuring transparency in AI and avoiding violations of data privacy, the rule of law, or civil liberties.
- Educating and training: promoting the application of AI to increase access to and opportunities for new sectors of the economy, fields of study, and jobs.
- Infrastructure: concentrating on increasing the availability of models, data, computational infrastructure, and other infrastructure components.
- Applications: increasing the use of AI in both public and private industries, such as healthcare, education, and transportation.
- International cooperation: encouraging global cooperation and alliances based on analytical research, multistakeholder involvement, and evidence-based strategies.
Examples of AI governance
- The General Data Protection Regulation (GDPR): An illustration of AI governance, especially about privacy and the protection of personal data. While many of the GDPR’s provisions are extremely relevant to AI systems, particularly those that process the personal data of individuals within the European Union, the regulation is not solely focused on AI.
- The Organization for Economic Co-operation and Development (OECD): The AI Principles, which have been embraced by more than 40 nations, place a strong emphasis on the ethical management of reliable AI, including accountability, justice, and transparency in AI systems.
- Corporate AI Ethics Boards: To ensure that AI initiatives are in line with ethical standards and societal values, many businesses have set up ethics boards or committees. To make sure that new AI services and products adhere to IBM’s AI principles, the company, for instance, has established an AI Ethics Council. These boards frequently consist of cross-functional groups with backgrounds in technology, law, and policy.
What is AI model governance?
- Model ownership: Teams of individuals typically work on a model during the AI development process. Monitoring the work that each team member completes is essential for guaranteeing model success, enhancing teamwork, and averting problems like needless duplications.
- Rules and regulations: By putting a set of rules into place, model development processes like data quality, feature engineering, and documentation are made error-free and comply with legal and regulatory requirements that reduce the risks associated with artificial intelligence.
- Data quality: To guarantee the security and quality of training data sets used to train AI models, standards must be in place. For the model to learn from the data and generate the intended results, the data must be impartial and accurate.
- Continuous monitoring: A model needs to be closely watched to make sure it is operating as intended once it enters the postproduction stage. Model governance guarantees that procedures are in place for a model’s ongoing training and necessary monitoring.
Conclusion
What is AI governance, makes sure that technology is created and used ethically, upholding human rights, safety, and justice. It addresses issues like bias, privacy, and data misuse and provides frameworks, standards, and guidelines for supervising AI systems. To promote ethical AI use globally, key pillars include innovation, reliable AI, education, infrastructure, applications, and international cooperation.
