Responsible AI in the Global Context: Maturity Model and Survey

• URL: http://arxiv.org/abs/2410.09985v1
• Date: Sun, 13 Oct 2024 20:04:32 GMT
• Title: Responsible AI in the Global Context: Maturity Model and Survey
• Authors: Anka Reuel, Patrick Connolly, Kiana Jafari Meimandi, Shekhar Tewari, Jakub Wiatrak, Dikshita Venkatesh, Mykel Kochenderfer,
• Abstract summary: Responsible AI (RAI) has emerged as a major focus across industry, policymaking, and academia. This study explores the global state of RAI through one of the most extensive surveys to date on the topic. We define a conceptual RAI maturity model for organizations to map how well they implement organizational and operational RAI measures.
• Score: 0.3613661942047476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Responsible AI (RAI) has emerged as a major focus across industry, policymaking, and academia, aiming to mitigate the risks and maximize the benefits of AI, both on an organizational and societal level. This study explores the global state of RAI through one of the most extensive surveys to date on the topic, surveying 1000 organizations across 20 industries and 19 geographical regions. We define a conceptual RAI maturity model for organizations to map how well they implement organizational and operational RAI measures. Based on this model, the survey assesses the adoption of system-level measures to mitigate identified risks related to, for example, discrimination, reliability, or privacy, and also covers key organizational processes pertaining to governance, risk management, and monitoring and control. The study highlights the expanding AI risk landscape, emphasizing the need for comprehensive risk mitigation strategies. The findings also reveal significant strides towards RAI maturity, but we also identify gaps in RAI implementation that could lead to increased (public) risks from AI systems. This research offers a structured approach to assess and improve RAI practices globally and underscores the critical need for bridging the gap between RAI planning and execution to ensure AI advancement aligns with human welfare and societal benefits.

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