Insights

Explore our latest thought leadership, research, and analysis across industries and capabilities.

A Guide to Differential Privacy for Data Scientists and AI Engineers

Differential privacy is a mathematical framework for protecting individual privacy while still allowing for useful data analysis. This guide answers key questions about its principles, mechanisms, and real-world applications.

A Proposal for Justifiable AI Decisions

This report provides a comprehensive analysis of the JADS Framework, an architectural pattern designed to solve the problem of explainability and legitimacy in artificial intelligence (AI) systems.

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AI Explainability: Output vs. Decision

This report will conduct an exhaustive comparative analysis of two competing paradigms that define this conflict. The first, which will be termed Model-Output Explanation, represents the current mainstream approach.

A Guide to Neuro-Symbolic AI in Financial Regulation

The Knowledge Acquisition Bottleneck (KAB) is the profound and long-standing challenge of translating vast amounts of unstructured human knowledge—found in documents, expert intuition, and procedures—into the structured, formal formats that computers require for logical reasoning.

Explainable AI: Methods, Implementation, and Frameworks - Part I : Foundations of Explainable AI (XAI)

XAI is a cornerstone of trustworthy AI, essential for building user confidence, ensuring regulatory compliance and accountability, helping developers debug and improve models, and mitigating harmful biases.

Explainable AI: Methods, Implementation, and Frameworks - Part II: A Comprehensive Taxonomy of XAI Methods

To navigate the diverse landscape of Explainable AI, methods are classified along key dimensions, including whether they are intrinsic ("white-box") or applied post-hoc ("black-box"), model-specific or model-agnostic, and whether they provide global or local explanations.

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A Comprehensive Technical Framework for AI Risk Mitigation and Compliance

This report presents a comprehensive technical framework for mitigating AI risk and ensuring compliance, designed for a technical audience of architects, engineers, and governance leaders. It moves beyond high-level principles to detail the specific governance structures, algorithmic techniques, security controls, and operational practices required to build and maintain trustworthy AI.

A Technical Review of Novel Mitigation Strategies for Risks in the MIT AI Repository

To provide a structured understanding of this complex landscape, the MIT AI Risk Repository offers a comprehensive, living database that synthesizes over 1600 risks from numerous academic, governmental, and industry frameworks. The objective of this report is to provide an exhaustive technical analysis of novel mitigation strategies corresponding to each of the seven risk domains.

The 10^25 FLOPs Tipping Point: Navigating Systemic Risk and Compliance Under the EU AI Act

While much of the EU AI Act focuses on specific high-risk use cases, a distinct and consequential set of rules has been created for a category of technology that underpins the modern AI ecosystem: General-Purpose AI models. Understanding the significance of the regulatory thresholds applied to these models requires a precise grasp of the Act's foundational definitions and its unique conception of "systemic risk."