The AI Accountability Framework developed by ITI aims to promote responsible development and deployment of AI systems, particularly in high-risk scenarios. It emphasizes shared responsibility among developers, deployers, and integrators, and outlines key practices to enhance transparency and accountability in AI governance.
The AI Accountability Policy Report emphasizes the importance of accountability mechanisms in the AI ecosystem, enabling stakeholders to expose issues and hold responsible entities accountable. It advocates for transparency and independent evaluations to promote a trustworthy AI marketplace where risks are managed effectively.
AI designers and developers are responsible for considering the design, development, decision processes, and outcomes of AI systems. Every individual involved in the creation of AI must account for its impact on the world and adhere to clear company policies regarding responsibility and accountability.
This workshop-based exploratory study investigates accountability in Artificial Intelligence (AI) through risk governance. It identifies key challenges and characteristics necessary for effective AI risk management methodologies, aiming to bridge the gap between conceptual understanding and practical application in the industry.
Organizations often struggle with managing and deploying AI systems responsibly. The U.S. Government Accountability Office has developed a framework to ensure accountability throughout the AI life cycle, focusing on governance, data, performance, and monitoring.
Artificial Intelligence (AI) is increasingly influencing our lives, necessitating the incorporation of ethical principles to uphold human values in its design. The ART design principles—Accountability, Responsibility, and Transparency—are essential for the development of AI systems that are sensitive to these values.
The paper discusses the challenges of moral accountability and safety assurance in the use of artificial intelligence-based clinical tools in healthcare. It emphasizes the need to update our understanding of accountability due to the opaque decision-making processes of these systems and suggests involving AI developers in the assessment of patient harm.
The document discusses the accountability and governance implications of AI, emphasizing the importance of compliance with data protection laws for AI systems that process personal data. It highlights the necessity of conducting data protection impact assessments (DPIAs) to identify and manage risks associated with AI technologies.