Must Learn AI Security Compendium 9: The Argument for Safe AI
Out of Band 9
This post is part of an ongoing series to educate about new and known security vulnerabilities against AI.
The full series index (including code, queries, and detections) is located here:
https://aka.ms/MustLearnAISecurity
The book version (pdf) of this series is located here: https://github.com/rod-trent/OpenAISecurity/tree/main/Must_Learn/Book_Version
The book will be updated when each new part in this series is released.
Periodically, throughout the Must Learn AI Security series, there will be a need to envelop previous chapters and prepare for upcoming chapters. These Compendiums serve as juncture points for the series, even though they might function well as standalone articles. So, welcome! This post serves as one of those compendiums. It’ll all make much more sense as the series progresses.
AI security and responsible AI are related in that both are concerned with the ethical and safe use of artificial intelligence. AI security involves protecting AI systems from malicious attacks and ensuring the confidentiality, integrity, and availability of data used by AI systems. Responsible AI, on the other hand, involves designing and implementing AI systems in an ethical and transparent manner to avoid bias and discrimination, protect privacy, and ensure accountability. Both AI security and responsible AI are necessary to ensure that AI is used for the benefit of society and does not cause harm.
Historically, because two different teams (sometimes more than two) manage these as separate topics, they are many times considered two completely different areas. But they’re not. They both fall under a “Safe AI” umbrella and they are inseparable.
AI Security and Responsible AI are intertwined because they both contribute to building trustworthy, reliable, and safe AI systems. The intersection of these two concepts ensures that AI technologies are developed and deployed in a way that protects users and society from potential risks and negative impacts. Here are some key aspects that show how AI Security and Responsible AI are intertwined:
Data protection:
Responsible AI emphasizes the importance of protecting users' privacy and handling their data ethically.
AI Security ensures that the AI systems are protected from unauthorized access, data breaches, and malicious attacks, which can compromise users' privacy.
Bias and fairness:
Responsible AI seeks to minimize biases in AI systems to ensure fairness and prevent discrimination.
AI Security plays a role in preventing attackers from exploiting vulnerabilities in AI systems to introduce or amplify biases, which could lead to unfair outcomes.
Transparency and explainability:
Responsible AI promotes transparency in AI decision-making processes and creating explainable AI systems.
AI Security helps by ensuring that the AI systems are secure and trustworthy, allowing users and stakeholders to have confidence in their transparency and explanations.
Robustness:
Responsible AI aims to build AI systems that are robust and can handle different inputs and situations without breaking down or producing unexpected results.
AI Security ensures that the AI systems are protected from adversarial attacks, which could cause the system to behave in undesired ways.
Accountability:
Both AI Security and Responsible AI emphasize the need for AI systems to be accountable for their actions and decisions.
This includes having mechanisms in place to track, monitor, and audit AI systems to ensure that they are functioning as intended and adhering to ethical and legal guidelines.
AI security and responsible AI are related in the sense that both aim to ensure that AI systems are safe, ethical, and trustworthy. AI security focuses on protecting AI systems from malicious attacks, such as data poisoning, adversarial examples, or model stealing. Responsible AI focuses on designing and deploying AI systems that adhere to certain principles, such as fairness, transparency, accountability, privacy, and safety.
Some of the challenges and opportunities of AI security and responsible AI are:
Incorporating AI in cybersecurity strategies can also play a crucial role in identifying threats and improving response times. AI developers have a unique responsibility to design systems that are robust and resilient against misuse. Techniques like differential privacy and federated learning can be used to protect data.
Responsible AI is meant to address data privacy, bias and lack of explainability, which represent the “big three” concerns of ethical AI. Data, which AI models rely on, is sometimes scraped from the internet with no permission or attribution. Bias can result from unrepresentative or skewed data sets, or from human prejudices embedded in the algorithms. Explainability refers to the ability of AI systems to justify their decisions and how they reach their conclusions.
Microsoft outlines six key principles for responsible AI: accountability, inclusiveness, reliability and safety, fairness, transparency, and privacy and security. These principles are essential to creating responsible and trustworthy AI as it moves into mainstream products and services. They’re guided by two perspectives: ethical and explainable.
Responsible AI is a framework of principles for ethically developing and deploying AI safely, ethically and in compliance with growing AI regulations. It’s composed of five core principles: fairness, transparency, accountability, privacy and safety. These principles can help organizations avoid potential legal, reputational, or operational risks associated with AI.
In summary, AI Security and Responsible AI are intertwined as they both work towards creating AI systems that are safe, trustworthy, and ethically sound. By addressing the concerns of both AI Security and Responsible AI, organizations and developers can build AI systems that benefit society while minimizing potential risks and negative consequences.
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