AI governance acts as a guiding hand in ensuring the safe and responsible development of AI technologies. It involves creating frameworks and regulations to uphold transparency, fairness, and accountability while mitigating risks like privacy violations and bias. As AI evolves, AI governance needs to evolve along with it to adapt to the various new challenges. 

Artificial intelligence (AI) governance refers to the oversight mechanisms or regulations set to ensure AI’s safe and ethical functioning in society or an enterprise. It is a set of frameworks consisting of rules and standards that guide the development of AI research and application to ensure fairness, transparency, and safety for human rights. Furthermore, it enables stakeholders and various organizations to benefit from AI-driven decision-making and automation. 

The main responsibility of AI governance is to manage and mitigate risks associated with AI, such as privacy violations, misuse of AI, and favoritism. AI governance involves collaboration between users, policymakers, developers, and others to ensure that AI is developed in a way that is friendly to human society. Using various AI policies and regulations, AI governance plans to align AI behaviors with human ethical standards while minimizing adverse effects.   

With AI’s continuous evolution, society and enterprises increasingly use AI for various purposes due to its convenience. Still, the potential for AI to harm has been forgotten. As AI uses machine learning(ML) algorithms to make decisions, a bias in machine learning can have a huge impact. For instance, a bias in ML can cause the AI to judge the situation incorrectly and can lead to issues like denial of healthcare, loans, and others. This shows the shortcomings of AI and highlights the importance of AI governance. Some of the examples of AI governance include:

• The Organisation for Economic Co-operation and Development (OECD): It has established AI principles that have been adopted by more than 40 nations. OECD prioritizes responsible management of trustworthy AI, and its principles underscore the importance of transparency, accountability, and fairness within AI systems. 
• Corporate AI Ethics Boards: A lot of companies have established committees or AI boards to make sure that their products and services match society’s values and ethical standards. The board members consist of individuals from different backgrounds, such as technical, legal, and policy.  

AI governance is important for overseeing the rapid progress of AI technology, especially after the development of generative AI. Generative AI is a popular technology capable of creating various content like images, codes, and text. It also has extensive potential across many different sectors. However, the need for robust AI governance has significantly grown due to its widespread utility. 

The principles of AI governance have become necessary for organizations to protect themselves and their users. The principles that can help with the application of AI technologies and moral development include:

• Transparency: It refers to the need for clear and open communication about how AI algorithms function and make decisions within an organization. Furthermore, they also need to be ready to explain the logic and reasoning behind AI-driven outcomes. 
• Empathy: Organizations should consider AI’s financial, technical, and social aspects. They need to anticipate and address the potential implications for all stakeholders. 
• Bias Control: It involves analyzing the training data to avoid embedding real-world biases into AI algorithms. This is done to promote fair and unbiased decisions. 
• Accountability: Organizations should take proactive steps to establish and uphold high standards for managing the changes that AI technologies can bring and acknowledge responsibility for AI’s impacts. 

Government agencies worldwide strive to govern AI in a way that supports its development while also considering its ethical concerns. On the other hand, private companies prioritize economic benefits and focus on efficiency and productivity. Government agencies and private companies also collaborate on developing standards or conducting research to improve AI.   

With the spread of AI governance, some common consensus on key regulations has emerged. These include transparency to users about AI use, reliability, security, and a commitment to social responsibility. The main priorities of AI governance include fairness, bias reduction, and clear accountability with responsible individuals and organization-wide education. 

In 2023, the U.S. White House issued orders to assure AI safety and security. This outlines a framework for developing new standards and reducing the risks associated with AI technology. The standard includes,

1. Safety and Security: AI system developers must share safety test results and crucial information with the U.S. government. Standards, tools, and tests were developed to ensure AI systems are trustworthy and safe.
2. Privacy Protection: This bill emphasizes the use of privacy-preserving techniques and strengthens research and technologies in this area. Federal agencies will be guided in assessing the effectiveness of these techniques.
3. Equity and Civil Rights: AI must not worsen discrimination or biases across sectors. Guidance will be provided to landlords and federal programs to address algorithmic discrimination and ensure fairness in the criminal justice system.
4. Consumer, Student, and Patient Protection: Advances responsible AI in healthcare and education, aiding in the development of life-saving drugs and supporting AI-enabled educational tools.
5. Support for Workers: Principles will be developed to reduce AI’s negative impact on jobs and workplaces, including addressing job displacement and promoting workplace equity.
6. Promoting Innovation: Stimulates AI research nationwide, fostering a fair and competitive AI ecosystem and facilitating the entry of skilled AI professionals.

• Assign Leaders and Fund Their Orders: Government agencies need accountable leaders with funded mandates to implement governance frameworks. It is important to recognize leaders who understand that data can be biased, who can be financially empowered, and who are held responsible for ensuring that AI is operated with respect to ethics and community values. 
• Provide Governance Training: To enhance AI governance, agencies should expand hackathons beyond operational efficiency to include ethics. Some key steps involve hosting an AI ethics keynote three months before pilots are presented and having agencies judge projects based on governance artifacts like audit reports, functional and non-functional requirements, and fact sheets. Additionally, providing training on artifact development and conducting full presentations with expert evaluation is crucial. Investing in ongoing AI literacy education is crucial to cultivating a continuous learning and adaptation culture and shedding outdated assumptions.
• Access Inventory Impacts: Organizations that develop multiple AI models often rely on algorithmic impact assessment forms to gather metadata and judge AI risks before deployment. There were a lot of concerns when these forms were used in isolation without rigorous education, communication, and cultural considerations. The problems include,

1. Individuals may lack incentives to fill out forms thoughtfully due to the pressure of meeting quotas.  
2. The forms might imply absolving model owners of risk and overlook subtle AI definitions and disparate impact. 

AI governance plays an important role in ensuring AI’s safe and ethical integration across society and various organizations. As AI applications increase, governments, corporations, and various stakeholders need to prioritize frameworks that uphold transparency, accountability, and equity. Furthermore, by taking proactive measures, we can foster the development of responsible AI that aligns with society’s values and safeguards against risks and biases. 

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