AI Governance Best Practices
AI holds enormous potential for revolutionizing industries and fundamentally reshaping how humans work and live. But the far-reaching implications of artificial intelligence require effective governance practices to ensure that the development, deployment, and use of AI technologies align with ethical standards, transparency, accountability, and fairness. AI governance is critical to addressing the ethical, legal, and societal implications of AI systems. This article explores what AI governance is, why it’s needed, and fundamental principles and strategies for fostering the responsible use of AI ecosystems.
What Is AI Governance?
Artificial intelligence governance is a collection of frameworks, policies, and best practices that serve as guardrails to ensure that AI technologies are developed and used in a way that minimizes potential risks from bias and maximizes intended benefits. AI governance addresses AI algorithms, decision-making, data privacy and security, and the potential economic and social impacts that accompany the use of this technology.
Why Is AI Governance Needed?
Governance is an indispensable part of using AI to benefit the organization and society. Here are four reasons why AI governance should be a priority.
Respecting ethical and moral considerations
AI systems can have significant societal consequences. For example, decisions made with AI-enabled tools can introduce biases that unfairly disadvantage certain individuals and communities. AI governance introduces a standard of accountability, requiring organizations to consider the societal impacts these systems can produce and ensure they’re implemented fairly, transparently, and in alignment with human values and respect for individual rights.
Complying with legal and regulatory compliance
AI-specific regulations are rapidly becoming a focus of governments worldwide. In the evolving regulatory environment, AI governance best practices ensure that these technologies are developed and used in accordance with existing laws and regulations. In data security and privacy, AI governance standards are essential in protecting individuals' data and ensuring compliance with data security and privacy laws.
The use of AI technologies presents many risks and challenges. These include the loss of trust, erosion of valuable skills stemming from an overreliance on AI, and the introduction of harmful biases into decision-making processes. AI governance practices provide a framework for successfully identifying and managing these and other risks.
AI algorithms can be opaque, presenting significant challenges for business leaders and other stakeholders to understand their decision-making processes. AI governance supports transparency and explainability, requiring organizations to provide detailed information about their AI systems, including the data sources and algorithms used in AI-assisted decision-making. By providing a detailed accounting of how their AI systems work, organizations can build and maintain greater trust with their employees, customers, and community stakeholders.
AI Governance Best Practices
AI governance best practices guide the responsible and effective implementation of artificial intelligence (AI) systems within an organization. Here are five foundational best practices for AI governance.
Building internal governance structures
Successful AI governance depends on robust internal governance mechanisms. Working groups composed of AI experts, business leaders, and key stakeholders can provide expertise, focus, and accountability, helping organizations craft policies for how AI is used within a company. Internal governance structures can meet numerous governance objectives, including defining the business use cases of AI systems, assigning roles and responsibilities, enforcing accountability, and assessing outcomes.
Transparent communication is vital for all groups with a stake in how AI is developed and used. These stakeholders may include employees, end users, investors, and community members. By explaining to each stakeholder group how AI works, how it is being used, and the anticipated benefits and drawbacks for them, organizations can foster transparency and trust with those most likely to be affected. Developing formal policies around stakeholder engagement helps establish how communication will be conducted.
Evaluating AI’s human impact
Well-governed AI systems respect the privacy and autonomy of individuals and avoid discrimination that can unfairly disadvantage certain populations. Risks that require mitigation include using poor quality training data, lack of diversity in the development team, and data sampling methodologies that lead to bias. Risk management strategies help ensure the models are being used responsibly.
Managing AI models
AI models can degrade over time. Organizations must conduct ongoing monitoring, model refreshes, and continuous testing to guard against model drift and ensure the system is performing as intended.
Addressing data governance and security
Modern businesses frequently collect and use sensitive consumer data for various purposes, including artificial intelligence. This data may include online purchasing patterns, social media activity, location information, and detailed demographic data. In the context of AI governance, implementing robust data security and governance standards safeguards the quality of AI system outcomes and ensures that relevant data security and privacy regulations are respected. By creating AI-specific data governance and security policies, organizations reduce the chances that sensitive consumer data will be compromised or misused.
Support AI Governance Best Practices with Snowflake
Snowflake's platform was built for the cloud from the ground up to support machine learning and AI-driven data science applications. Leveraging the most sophisticated cloud security technologies available, the Snowflake Data Cloud is secure, resilient, and perfectly aligned with AI governance best practices for data governance and security. Snowflake provides industry-leading features that ensure the highest levels of governance for your account and users and all the data you store and access in Snowflake.