Artificial intelligence (AI) is set to have a significant impact on society, including healthcare, transportation, finance, and national security. Industry practitioners and citizens are actively discussing the many ways AI could be used.
It is important to consider the real-world consequences of AI deployment, beyond just suggestions for streaming videos or shopping preferences. The key question of our time is how to use AI for the greater good of society, while also addressing potential risks such as biases and security threats.
In this digital era, where cybersecurity concerns are increasing, AI introduces new vulnerabilities. However, we must not lose sight of the bigger picture. The world of AI has both positive and negative aspects, and it is evolving rapidly. We must drive the adoption of AI, defend against its risks, and ensure responsible use to unlock its full potential for advancements without compromising progress.
Overview of the NIST Artificial Intelligence Risk Management Framework
The NIST AI Risk Management Framework (AI RMF) is a comprehensive guideline developed by NIST to help organizations manage risks associated with AI systems. It aims to enhance trustworthiness and minimize potential harm from AI technologies. The framework is divided into two main parts:
Planning and understanding: This part focuses on evaluating the risks and benefits of AI and defining criteria for trustworthy AI systems. Trustworthiness is measured based on factors like validity, reliability, security, resilience, accountability, transparency, explainability, privacy enhancement, and fairness with managed biases.
Actionable guidance: This section outlines four key steps – govern, map, measure, and manage. These steps are integrated into the AI system development process to establish a risk management culture, identify and assess risks, and implement effective mitigation strategies.
Information gathering: Collecting essential data about AI systems, such as project details and timelines.
Govern: Establishing a strong governance culture for AI risk management throughout the organization.
Map: Framing risks in the context of the AI system to enhance risk identification.
Measure: Using various methods to analyze and monitor AI risks and their impacts.
Manage: Applying systematic practices to address identified risks, focusing on risk treatment and response planning.
The AI RMF is a valuable tool to assist organizations in creating a strong governance program and managing the risks associated with their AI systems. It is not mandatory under any current proposed laws, but it can help companies develop a robust governance program for AI and stay ahead with a sustainable risk management framework.
