Generative AI (GenAI) is revolutionizing industries ranging from content generation and software engineering to healthcare and finance. Unlike traditional AI systems, GenAI models generate dynamic outputs, introducing significant challenges in testing and validation.
Generative AI Quality Testing: A Standards-Based Approach for Reliability Read More »
As AI systems become integral across industries, ensuring reliability, fairness, and trustworthy performance is critical. From healthcare and finance to autonomous driving and customer service, AI systems present unique challenges requiring comprehensive quality testing to manage risks, foster trust, and achieve consistent performance in diverse, real-world applications.
Advanced AI Quality Testing: Ensuring Robust and Trustworthy AI Systems Read More »
In 2024, cyberattacks surged by 16%, with ransomware posing the most immediate threat to critical infrastructure. Another report by Forescout Research – Vedere Labs highlighted that between January 2023 and January 2024, the world’s critical infrastructure had been attacked more than 420 million times, with attacks ranging in magnitude, indicating a 30% increase from 2022.
Software Quality: A Pillar of NIS2 Compliance and Resilience Read More »
As AI transforms industries and reshapes business landscapes, organizations face a dual challenge: harnessing its immense potential while ensuring responsible and ethical deployment. At code4thought, we understand that achieving this balance requires not only adherence to global standards but also the practical tools to implement them effectively.
The Ultimate Guide to AI Governance Standards Read More »
As businesses increasingly leverage AI for streamlined recruitment due to its ability to quickly sift through vast applicant pools, a clear-eyed view of fairness is essential. The New York City’s Bias Audit Law is an unprecedented regulation that fosters fairness by mandating bias audits in AI-assisted hiring tools. Also known as Local Law 144, it marks a crucial step towards ensuring AI doesn’t
Ensuring Fairness in AI-Assisted Hiring: Lessons Learned from the NYC Bias Audit Law Read More »
Amidst the transformative wave of artificial intelligence (AI) invoked in the digital ecosystem, the Organization for Economic Cooperation and Development (OECD) has introduced a comprehensive set of AI principles, a framework aimed at guiding responsible and effective AI adoption. These principles promise to revolutionize industries and redefine the way businesses operate.
OECD AI Principles: Guardrails to Responsible AI Adoption Read More »
We’re thrilled to announce a strategic partnership between code4thought and the University of Patras, one of Greece’s leading academic institutions renowned for its cutting-edge research and innovation. Through a newly signed Memorandum of Understanding (MoU), this collaboration is set to bridge the gap between academia and industry, driving long-term value for both parties.
code4thought announces partnership with the University of Patras Read More »
Today, software permeates every aspect of life, and artificial intelligence (AI) is becoming increasingly integral to business operations. As AI systems grow in complexity and ubiquity, ensuring their reliability, safety, and effectiveness is paramount.
Large Language Models (LLMs) have garnered significant attention since the mass-market introduction of pre-trained chatbots in late 2022. Companies keen to take advantage of LLMs quickly incorporate them into their services and processes.
OWASP Top 10 Vulnerabilities for Large Language Models (LLM): Impact and Mitigation Read More »
To address the risks and challenges of AI adoption and use, the National Institute of Standards and Technology (NIST) has developed the AI Risk Management Framework (AI RMF), a voluntary framework designed to help organizations manage the risks associated with AI technologies.
