Let's look at three useful practices for implementing AI security, let's develop guidelines for AI security. Any tool, physical or digital, including AI, can be used unethically. There is a need to create a culture of AI safety at every stage of machine learning (ML) development, from data collection to product implementation. The harms that can result from a machine learning model can occur at the individual, group, social and environmental levels.
In particular, AI supports workplace safety by identifying anomalies in data. The algorithms are trained on a basis of common patterns, and the software provides unparalleled threat detection capability. In addition, AI helps identify potential compromised areas by analyzing images, videos, and other data to differentiate threats from standard activity. The growing awareness of artificial intelligence in American society is also reflected in the growing field of research on AI security.
The security of AI, although a young field of research, has already found significant problems in current machine learning paradigms that deserve to be studied. In addition, recent years have given the field of AI security the necessary measures to make it more empirical and organized. As a result, we can expect research on AI safety to advance significantly in the future. In this article, the authors emphasize the lack of an exhaustive repository of environments based on AI security, which has led them to develop the code for basic RL environments that are victims of AI security problems.
In the article “Concrete problems in AI security"8, researchers from Google Brain (an AI research team from Google), OpenAI (developers of the aforementioned DALL-E), the University of California at Berkeley and Stanford exposed the problems of recent advances in machine learning that could deserve to be studied in research on AI security. Since there have been no major changes in the way AI is built in the last five years, it's fair to understand that these models are in the same line of continuity as the development of AI. As a result, this means that AI safety research will have more concrete means to measure its results. The specification, as its name suggests, is the study of how to create AI systems whose observed behavior is aligned with the behavior expected by their developers.
The fact that AI tools are easily adopted in healthcare settings depends on increasing public trust in AI. With more AI applications coming to market and healthcare systems starting to develop machine learning algorithms internally, it is essential that providers take a thoughtful approach to implementing AI in the healthcare process. At the federal government level, the National Institute of Standards and Technology recently published the AI Risk Management Framework, a voluntary document that allows people using AI systems to measure their risks. Following the publication of “Concrete Problems in AI Security”, the developers of AlphaGo and AlphaFold published their own article focusing on AI safety, “AI Safety Gridworlds” 9.Although serious research on AI safety has started a bit late to the liking of some, publications such as “AI Safety Gridworlds” have given the field not only more problems to study, but also a comprehensive engineering resource for future software development.
