Unite.AI
SEPTEMBER 13, 2023
Increasingly though, large datasets and the muddled pathways by which AI models generate their outputs are obscuring the explainability that hospitals and healthcare providers require to trace and prevent potential inaccuracies. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.
Marktechpost
OCTOBER 6, 2024
To address these challenges, researchers are exploring Multimodal Large Language Models (M-LLMs) for more explainable IFDL, enabling clearer identification and localization of manipulated regions. Although these methods achieve satisfactory performance, they need more explainability and help to generalize across different datasets.
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IBM Journey to AI blog
MAY 2, 2023
.” That is why IBM developed a governance platform that monitors models for fairness and bias, captures the origins of data used, and can ultimately provide a more transparent, explainable and reliable AI management process. The stakes are simply too high, and our society deserves nothing less.
AI News
OCTOBER 8, 2024
As artificial intelligence systems increasingly permeate critical decision-making processes in our everyday lives, the integration of ethical frameworks into AI development is becoming a research priority. Canavotto and her colleagues, Jeff Horty and Eric Pacuit, are developing a hybrid approach to combine the best of both approaches.
Dlabs.ai
AUGUST 16, 2022
This is the challenge that explainable AI solves. Explainable artificial intelligence shows how a model arrives at a conclusion. What is explainable AI? Explainable artificial intelligence (or XAI, for short) is a process that helps people understand an AI model’s output. Let’s begin.
AI News
JULY 31, 2024
Curtis Wilson, Staff Data Engineer at Synopsys’ Software Integrity Group , believes the new regulation could be a crucial step in addressing the AI industry’s most pressing challenge: building trust. “The greatest problem facing AI developers is not regulation, but a lack of trust in AI,” Wilson stated.
Unite.AI
JUNE 26, 2023
Who is responsible when AI mistakes in healthcare cause accidents, injuries or worse? Depending on the situation, it could be the AI developer, a healthcare professional or even the patient. Liability is an increasingly complex and serious concern as AI becomes more common in healthcare. Not necessarily.
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