Intellectual hub of the topic
Understanding how decisions are made within complex systems is becoming a prerequisite for trusting them. In this collection, we gather materials dedicated to the principles of interpretability and the possibility of providing a rational explanation for processes that are often perceived as a «black box».We analyze not only the technical aspects of data architecture but also the ethical, legal, and social implications of their opacity or ambiguity.
OpenAI has detailed its Model Spec – an internal framework that governs AI model behavior and strikes a balance between safety and user freedom.
The popular method for comparing AI transcription services isn't as objective as it seems – we'll explore where it falls short.
What happens when AI starts acting on its own, and why its autonomy opens the door to attacks no one ever saw coming.
Researchers have proposed a method for identifying the capabilities of AI agents by their content, ensuring their identifier remains stable when transferred between platforms.
We explore why AI agents for data analysis often provide incorrect answers and how a special context layer helps fix this.
OpenAI has shared how it monitors deviations in the behavior of its internal code-writing AI agents and explained why this is crucial for safety.
We explore why AI agents don't guarantee consistent results and what can be done to make them trustworthy.
We break down how logic and common sense can become a convenient shield, hiding a simple unwillingness to consider others.
Researchers have proposed a new approach to evaluating the quality of AI responses, which, instead of a simple «yes/no», attempts to understand the reasons behind errors.
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