Intelligent Artifacts Blog

Regulation-Ready AI: The Impact of Editability

As AI regulations continue to develop worldwide, it is crucial to consider the editing capabilities of AI systems as part of the analysis process. Editability enables the modification and/or removal of learned records from an AI’s memory.

Written by:
Emily Mathwich

Auditing Information: Traceable Artificial Intelligence

An auditable, verifiable, and validation-ready AI system should be capable of following information backward from prediction to the original trained record(s) that produced that prediction.

Written by:
Emily Mathwich

Making Distinctions: AI Interpretability vs. Explainability

Whereas XAI gives you insight into why an AI system produced a specific prediction set, interpretability refers to the ability of humans to understand how an AI system works.

Written by:
Emily Mathwich

Real-time AI: The Impact of Computable Time-to-Predictions

Artificial intelligence systems employed for safety and mission-critical decision support are not useful if results arrive after the time of need, regardless of the quality of outputs. Computable artificial intelligence is AI designed with deterministic, efficient processes and algorithms that enable the exact calculation of response times from query to predictions for real-time operations.

Written by:
Emily Mathwich

What Is Explainable Artificial Intelligence?

Explainable Artificial Intelligence (XAI) refers to artificial intelligence (AI) systems that are transparent and understandable to humans. The goal of XAI is to develop AI models that can provide clear explanations of their decision-making processes so that humans can trust and verify their outputs.

Written by:
Emily Mathwich

Black Box AI is the New Mysticism…and More Dangerous

Using unexplainable deep learning models in safety-critical industries is equivalent to using tea-leaf reading to pilot a plane.

Written by:
Sevak Avakians

Decoupling Data, Memory, and Algorithm

Users should have clear guidelines when evaluating any given AI technology especially for use in safety-critical applications that can affect life, death, and well-being. Decision-makers should be able to evaluate an AI’s data, memory, and algorithmic structures individually.

Written by:
Emily Mathwich