Generative ai systems, which create content in different formats, are increasingly widespread. These systems are used in various fields, including medicine, news, politics, and social interaction, providing companionship. Using natural language output, these systems produced information in a single format, such as text or graphics. To make generative ai systems more adaptable, there is a growing trend to enhance them to work with additional formats, such as audio (including voice and music) and video.
The increasing use of generative ai systems highlights the need to evaluate the potential risks associated with their implementation. As these technologies become more prevalent and integrated into various applications, concerns arise regarding public safety. Consequently, assessing the potential risks posed by generative ai systems is becoming a priority for ai developers, policymakers, regulators, and civil society.
The increasing use of these systems highlights the need to evaluate the potential dangers related to the implementation of generative ai systems. As a result, it is increasingly important for ai developers, regulators and civil society to assess the potential threats posed by generative ai systems. The development of ai that could spread false information raises moral questions about how such technologies will affect society.
Accordingly, a recent study by Google DeepMind researchers offers a comprehensive approach to assessing the social and ethical dangers of ai systems at various contextual layers. The DeepMind framework systematically assesses risks at three distinct levels: system capabilities, human interactions with the technology, and the broader systemic impacts it may have.
They stressed that it is crucial to recognize that even highly capable systems can only necessarily cause harm if they are used problematically within a specific context. Additionally, the framework examines real-world human interactions with the ai system. This involves considering factors such as who is using the technology and whether it works as intended.
Finally, the framework tests how ai delves into the risks that can arise when it is widely adopted. This assessment considers how technology influences broader social systems and institutions. The researchers emphasize how important context is in determining how risky ai is. Each layer of the framework is infused with contextual concerns, emphasizing the importance of knowing who will use ai and why. For example, even if an ai system produces objectively accurate results, users’ interpretation and subsequent dissemination of these results may have unintended consequences that are only apparent within certain contextual constraints.
The researchers provided a case study focused on misinformation to demonstrate this strategy. The evaluation includes assessing an ai‘s tendency to make factual errors, observing how users interact with the system, and measuring any subsequent repercussions, such as the spread of incorrect information. This interconnection of model behavior with actual harm occurring in a given context leads to actionable insights.
DeepMind’s context-based approach underscores the importance of going beyond isolated model metrics. It emphasizes the critical need to evaluate how ai systems operate within the complex reality of social contexts. This holistic assessment is crucial to reaping the benefits of ai while minimizing the associated risks.
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Rachit Ranjan is a consulting intern at MarktechPost. He is currently pursuing his B.tech from the Indian Institute of technology (IIT), Patna. He is actively shaping his career in the field of artificial intelligence and data science and is passionate and dedicated to exploring these fields.
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