Generative AI Systems and Their Growing Use
Generative AI systems are becoming more common and are used in various fields such as medicine, news, politics, and social interaction. These systems create content in different formats, such as text or graphics, using natural language output. To make these systems more versatile, there is a trend to improve them to work with additional formats like audio and video.
However, as the use of generative AI systems increases, it is important to evaluate the potential risks associated with their deployment. Concerns about public safety arise as these technologies become more integrated into various applications. Therefore, AI developers, policymakers, regulators, and civil society need to prioritize the assessment of the potential risks posed by generative AI systems. The development of AI systems that can spread false information raises ethical questions about their impact on society.
Assessing the Social and Ethical Hazards of AI Systems
A recent study conducted by researchers from Google DeepMind offers a comprehensive approach to evaluating the social and ethical risks of AI systems. The DeepMind framework assesses these risks at three levels: the capabilities of the system, human interactions with the technology, and the broader systemic impacts it may have.
The researchers highlight the importance of considering the specific context in which highly capable AI systems are used. They emphasize that these systems may only cause harm if used problematically within a certain context. The framework also focuses on real-world human interactions with the AI system, considering factors such as user demographics and whether the system functions as intended.
Furthermore, the framework evaluates the potential risks that may arise from widespread adoption of AI. It examines how AI technology influences larger social systems and institutions. The researchers emphasize the importance of understanding the context in determining the risks associated with AI. Contextual concerns permeate each layer of the framework, highlighting the significance of knowing who will use the AI and why. For example, even if an AI system produces accurate outputs, users’ interpretation and dissemination of the information may have unintended consequences within specific contextual constraints.
A Case Study on Misinformation and Actionable Insights
The researchers provide a case study focusing on misinformation to demonstrate their evaluation strategy. The assessment includes analyzing an AI system’s tendency for factual errors, observing how users interact with the system, and measuring any subsequent repercussions, such as the spread of incorrect information. This approach connects the behavior of the AI model with the actual harm that occurs in a given context, leading to actionable insights.
The Importance of a Context-Based Approach for AI Systems
DeepMind’s context-based approach emphasizes the need to move beyond isolated model metrics. It highlights the critical importance of evaluating how AI systems function within the complex reality of social contexts. This holistic assessment is essential for harnessing the benefits of AI while minimizing associated risks.