Holistic AI’s Post

⚖️ The High Cost of Non-Compliance: Penalties Issued for AI under Existing Laws Organizations are increasingly investing in AI tools and systems to enhance their processes and products, and maximize value. AI’s integration with businesses is expanding globally, with recent estimates suggesting that about 42% of companies are using AI in some way. However, the risks associated with AI systems can cause major harms, especially if appropriate business practices and safeguards are not put in place. Learn more: https://lnkd.in/dBX5cf9w #AIGovernance #AIPenalties #AICompliance #ResponsibleAI

📊 What is data contamination? LLMs get their general knowledge of the world from pretraining, where they are fed a wide variety of information from hundreds of thousands or even millions of sources. If a particular model is going to be used in a specific context, then it also might be fine-tuned, or fed information pertinent to its use case. This results in a chatbot that can deal with all types of questions, but “specializes” in a certain area. If this chatbot that is not up to company standards, it could risk reputational damage or even result in harm. Learn more: https://lnkd.in/eBPZCTfQ #AI #MachineLearning #LLM #AIGovernance #ResponsibleAI

💫 Blog post: New York City's Local Law 144: What HR Leaders Nationwide Need to Know New York City Local Law 144 requires annual, independent, impartial bias audits of automated employment decision tools used to evaluate candidates for employment or employees for promotion in New York City. Learn about how this law affects employers inside and outside of New York City: https://lnkd.in/e3At7s2T #ResponsibleAI #NYCLocalLaw144 #AICompliance #HRTech

💫 How to Audit Large Language Models (LLMs) Large language model auditing is not merely a technical necessity but a crucial matter of ethics. These models are trained on extensive datasets that, if not properly vetted, can lead to the propagation of biases, misunderstandings, or even the dissemination of false information. An effective audit aims to pinpoint these issues, ensuring that LLMs function as intended and adhere to high ethical standards. Read more: https://lnkd.in/d7UjteJ9 #LLM #AI #AIAuditing #ResponsibleAI #AIGovernance

🤔 What are the implications of the EU AI Act for Non-EU Leadership Teams? One of the most significant aspects of the EU AI Act is its extraterritorial reach. The Act applies not only to companies operating within the EU but also to non-EU companies that offer AI products or services within the EU market. This means that any company, regardless of its geographic location, must comply with the EU AI Act if it targets EU users or affects EU citizens. Read more: https://lnkd.in/g5DrUhhY #EUAIAct #ResponsibleAI #AIGovernance #AICompliance

🌟 What are Graph Neural Networks? Graph Neural Networks (GNNs) are a type of AI that excels at analyzing complex relationships within graph data. They can be used for a number of applications such as: ➡ Recommending products or content ➡ Detecting fraud and anomalies ➡ Discovering new drugs and materials ➡ Analyzing social networks and interactions Learn about how they are implemented, what their limitations are, and how to make them trustworthy here: https://lnkd.in/efqrUsRz #DataScience #GraphNeuralNetworks #AI #ResponsibleAI

💫 Blog post: High-Risk AI Systems Under the EU AI Act The EU AI Act represents a pivotal regulatory framework designed to govern the development and deployment of artificial intelligence across Europe. This article explores the significance of identifying AI systems as high-risk, detailing the criteria outlined in the Act's Article 6 and Annex III. Read here: https://lnkd.in/eCsJq2aZ #AIGovernance #EUAIAct #ResponsibleAI

🔔 New blog post! An Overview of Data Contamination: The Causes, Risks, Signs, and Defenses One of the defining corporate challenges of our time is how to deploy AI in a way that not only cuts costs, maximizes productivity, and energizes products, but also is safe, ethical, and adheres to legal standards. This has led to an explosion in the number and types of benchmarks available to evaluate and monitor LLM performance. However, there is a subtle issue lurking: data contamination. Data contamination occurs when parts or characteristics of a test set leak into a training set, meaning the two datasets are no longer separate. This can lead to artificially high scores on benchmarks, decreasing their value, as well as amplified biases in models. Read more: https://lnkd.in/eBPZCTfQ #AI #MachineLearning #LLM #AIGovernance #ResponsibleAI