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Safe AI 101: What It Means

Safe AI prioritizes ensuring the safety and reliability of AI systems and of those using AI.
Davi Ottenheimer, VP of Trust and Digital Ethics
April 17, 2024

What Is Safe AI?

Safe AI encompasses a crucial aspect of the rapidly evolving field of artificial intelligence. Amid the multitude of terms such as Responsible AI, Ethical AI, and Trustworthy AI being used interchangeably today, it's essential to outline the important nuances that differentiate Safe AI from otherwise similar-sounding concepts. 

Let’s compare “Safe AI” and “Responsible AI” as an example of these nuances. “Responsible AI” encompasses a much broader spectrum of practices in AI development and deployment. It requires the alignment of AI technologies with ethics and societal values, ensuring privacy, consent, and fairness. It requires accountability, transparency, and attributing responsibility for AI decisions and processes. It requires bias mitigation and ensuring equitable outcomes across diverse groups. It requires safety and risk management controls, particularly in high-stakes domains like healthcare or autonomous vehicles. It requires legal compliance, proving adherence to current and emerging global regulations governing AI. 

Responsible AI emphasizes not only the technical safety of AI systems but also their alignment with ethical principles and values, as well as their broader implications for society. 

Safe AI instead emphasizes the more narrow but equally important concept of ensuring trusted systems operate as intended — reliable, secure, and free from harmful consequences — within defined safety parameters. It prioritizes mitigating risks in the deployment of AI systems and ensuring the safety of individuals and groups using them.

As society grows more concerned with how personal data is used by AI, organizations are prioritizing adopting emerging technologies that enable Safe AI by design. Solid Pods, for example, empower users with control over their data and promote transparency in AI practices, delivering today a promising pathway toward achieving and exceeding AI regulation objectives. 

Understanding these key distinctions and using the terminology accurately is crucial for fostering a comprehensive approach to AI governance and development, thereby choosing the right solutions for advancing benefits while mitigating risks.

Safe AI Frameworks Today

Several companies like IBM, with its AI Fairness 360 toolkit, and Google, with its Responsible AI Practices, are already actively working on promoting ethical considerations in the development and deployment of AI systems. IBM’s AI Fairness 360 provides algorithms and metrics to detect and mitigate biases in AI models, promoting fairness and equity. Google's Responsible AI Practices emphasize transparency, accountability, and user empowerment in AI development processes, aiming to build trustworthy AI systems that prioritize user safety and well-being. 

However, clear challenges persist in the Safe AI landscape due to the nature of AI “scraping” habits and centralization architectures. For instance, while fairness tools like IBM's offer valuable resources for detecting and mitigating biases, they often require significant expertise to implement effectively, limiting their accessibility to smaller organizations and non-experts (ironically impeding scalability). 

Additionally, the rapidly evolving nature of proprietary and closed AI technologies poses challenges for regulatory frameworks to keep pace with emerging risks and ethical considerations at a granular enough level to make AI safe to use. Technological solutions are needed where open standards meant to scale help the industry move beyond the artificial trade-offs and limitations imposed by closed silos. Companies and regulatory bodies must work collaboratively to address these challenges and ensure a technical path for standardized responsible development and deployment of AI technologies for the benefit of society.

The Path to Truly Safe AI

Given the diverse and always expanding terminology surrounding AI ethics and governance, the idea of working on Safe AI requires a proactive, continuous approach to mitigating risks and prioritizing the safety and well-being of individuals and society. Emerging frameworks and technologies like Solid, invented by Sir Tim Berners-Lee, offer a promising pathway towards realizing Safe AI objectives.

Solid Pods, provide a secure and distributed infrastructure for managing personal data, empowering individuals with control over their digital identities. By leveraging Solid, organizations can benefit from better access to better data while upholding principles of transparency, fairness, and accountability in AI practices, as well as mitigating risks associated with centralized data storage and unauthorized access.

By promoting safer AI ecosystems using innovative frameworks like Solid, we can stay ahead of and go beyond regulations to ensure a future where technology empowers individuals, drives value for organizations, and enriches society.

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