Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Algorithmic Accountability: Explainability ensures accountability in machine learning and AI systems.

Explainability 52

Explainability Machine Learning Deep Learning Explainable AI 52

IBM Journey to AI blog

APRIL 11, 2023

The new rules, which passed in December 2021 with enforcement , will require organizations that use algorithmic HR tools to conduct a yearly bias audit. It is important to choose an auditor that specializes in HR or Talent and trustworthy, explainable AI, and has RAII Certification and DAA digital accreditation.

AI Strategy 221

AI Strategy Algorithm Automation AI 221

ML @ CMU

MARCH 31, 2023

This blog post discusses the effectiveness of black-box model explanations in aiding end users to make decisions. However, while numerous explainable AI (XAI) methods have been developed, XAI has yet to deliver on this promise. We introduced an algorithmic-based evaluation called simulated user evaluation (SimEvals) [ 2 ].

ML 98

ML Explainability Algorithm Machine Learning 98

ML @ CMU

MARCH 30, 2023

This blog post discusses the effectiveness of black-box model explanations in aiding end users to make decisions. However, while numerous explainable AI (XAI) methods have been developed, XAI has yet to deliver on this promise. We introduced an algorithmic-based evaluation called simulated user evaluation (SimEvals) [ 2 ].

ML 52

ML Explainability Algorithm Machine Learning 52