What is AI safety?

AI safety refers to the field of research dedicated to preventing undesirable and potentially disastrous consequences that could result from artificial intelligence (AI) systems. The goal is to ensure AI systems operate safely and provide beneficial impacts to humanity.

AI safety is also sometimes referred to as AI alignment. This refers to the technical challenge of ensuring an AI's goals and incentives are aligned with human values. Rather than optimizing solely for a narrow objective, AI alignment aims to create AI that pursues goals and behaviors that are beneficial to humans.

Overall, the term AI safety encompasses efforts to develop techniques that allow AI systems to remain under human control. It aims to avoid scenarios where uncontrolled AI could potentially harm human well-being. AI safety research spans technical areas like transparency, robustness, and verification of AI systems ^[1].

Types of AI safety research

As an emerging field, AI safety research encompasses a range of technical and socio-ethical sub-disciplines:

• Technical AI safety research focuses on building safer AI systems through techniques like verification, transparency, and robustness. This involves areas like avoiding negative side effects, avoiding reward hacking, and safe exploration in AI systems.
• AI policy and governance research examines how laws, regulations, and institutions can shape the development of AI in safe and beneficial ways. This involves studying topics like liability, certification standards, and international coordination on AI.
• AI ethics research explores the values that should guide AI design, development, and deployment. It grapples with questions around autonomy, privacy, bias, and other social impacts of AI.
• AI risk assessment research seeks to assess and quantify potential risks from AI systems, like economic impacts or existential threats. This supports evidence-based policymaking and prioritization in AI safety.

By bringing together technical and social disciplines, the field of AI safety aims to realize the benefits of AI while mitigating risks.

Everyday examples of AI safety

AI safety comes up in many everyday situations that people may not realize. Here are a few common examples:

• Parent childproofing a home - Parents may use safety gates, cabinet locks, and other devices to restrict a child's access and prevent accidents. This is similar to restricting an AI system's capabilities and access to prevent unintended harm. (Marr, 2019)
• Testing pharmaceutical drugs thoroughly - Extensive clinical trials help ensure drugs are safe before being approved. Similarly, AI systems need rigorous testing to avoid negative outcomes. (Patel, 2022)
• Having airbags in cars - Airbags protect passengers in a crash. AI safety techniques aim to protect humans from potential AI failures. (IA Network, 2022)

AI safety for your team

Implementing AI safety practices is crucial for engineering teams building AI systems. Understanding potential risks and prioritizing alignment with human values from the start can help mitigate harmful impacts down the line.

Teams should focus on:

• Understanding AI risks - Being aware of potential dangers like reward hacking, scalable deceit, and unintended side effects will allow teams to proactively address safety.
• Implementing safety practices - Techniques like uncertainty modeling, AI safety gym, and red teaming can help teams build safer systems.
• Prioritizing ethics and alignment - Keeping human values central when designing objectives and constraints will result in more beneficial AI.

Taking a proactive approach to safety allows developers to harness AI positively while avoiding pitfalls. Establishing a culture of safety ultimately leads to more robust and trustworthy AI.

For more on implementing AI safety, check out the Google CTO guide.

AI safety for your customers

Investing in AI safety will help build increased trust in your AI systems among your customers. By researching ways to ensure safe and beneficial AI, you demonstrate dedication to mitigating risks and prioritizing ethics. This research into AI alignment techniques will lead to AI systems that reliably respect human values. Your customers will feel more comfortable adopting AI services knowing thoughtful precautions are being taken to avoid negative unintended consequences.

Robust AI safety practices will also reduce risks from potential AI failures or accidents. With rigorous testing, containment methods, and oversight protocols, you can catch and resolve issues before they lead to harm. Your customers will feel reassured that all efforts are being made to prevent detrimental outcomes.

Ultimately, focusing on AI safety will allow you to deliver more reliable and beneficial AI applications. Safety-oriented design principles will steer you towards AI that augments human intelligence responsibly. The more your AI is verifiably aligned with ethical human preferences, the more value it can provide to customers. Prioritizing safety leads to human-centric AI they can trust.