This literature review discusses the legal challenges related to transparency and accountability in artificial intelligence (AI) systems, emphasizing their impact on individual and societal wellbeing. It aims to provide insights into responsible AI governance by examining these challenges from the perspectives of users, providers, and regulators.
This paper investigates the ethical guidelines defined by organizations for developing transparent and explainable AI systems, highlighting the importance of explainability as it relates to transparency and trustworthiness. It proposes a model of explainability components and a template to assist practitioners in defining explainability requirements effectively.
On September 19, 2024, California Governor Gavin Newsom signed the California AI Transparency Act, requiring generative AI system providers to implement measures for content disclosure. This law, effective January 1, 2026, mandates the use of AI detection tools and clear labeling of AI-generated content to enhance transparency.
AI transparency is crucial as it ensures that users understand the workings and impacts of AI systems, fostering trust and informed consent. This post examines the definition of AI transparency, its levels including explainability, governance, and impact, and emphasizes its importance in building confidence among stakeholders.
Transparency and explainability are crucial concepts in AI that help establish trust in systems and their outcomes. While transparency focuses on providing general information about the AI system, explainability seeks to clarify individual decisions or outcomes for users and stakeholders.
This conceptual paper explores transparency in artificial intelligence (AI) from both socio-legal and computer scientific perspectives. It emphasizes the need for a multidisciplinary understanding of AI transparency to effectively contribute to its governance in society.
The AI Governance Alliance is dedicated to fostering inclusive and ethical AI practices across industries, ensuring that AI adoption enhances human capabilities and promotes global prosperity. Their work focuses on responsible AI usage, developing regulatory frameworks, and advancing technical standards for safe AI systems.
Deloitte calls for transparency and responsibility in artificial intelligence (AI), emphasizing the need for explainability in AI-driven decisions that impact daily lives. The publication discusses the risks associated with AI, including bias and misuse, while advocating for ethical frameworks and governance to ensure AI benefits society.
AI has a significant transparency problem, with many business executives acknowledging its importance but often suspending AI tool deployment due to ethical concerns. To address these challenges, organizations need to reconcile misconceptions about AI transparency and adopt responsible practices to build trust with their customers.
The article discusses the importance of transparency, explainability, and interpretability in AI and machine learning models used for credit underwriting. It emphasizes that these factors are crucial for ensuring fair lending practices and mitigating bias in financial decision-making.