Remove Algorithm Remove Explainable AI Remove Information
article thumbnail

How Large Language Models Are Unveiling the Mystery of ‘Blackbox’ AI

Unite.AI

Thats why explainability is such a key issue. People want to know how AI systems work, why they make certain decisions, and what data they use. The more we can explain AI, the easier it is to trust and use it. Large Language Models (LLMs) are changing how we interact with AI. Lets dive into how theyre doing this.

article thumbnail

Navigating AI Bias: A Guide for Responsible Development

Unite.AI

Businesses relying on AI must address these risks to ensure fairness, transparency, and compliance with evolving regulations. The following are risks that companies often face regarding AI bias. Algorithmic Bias in Decision-Making AI-powered recruitment tools can reinforce biases, impacting hiring decisions and creating legal risks.

Algorithm 162
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Global executives and AI strategy for HR: How to tackle bias in algorithmic AI

IBM Journey to AI blog

The new rules, which passed in December 2021 with enforcement , will require organizations that use algorithmic HR tools to conduct a yearly bias audit. Although some third-party vendor information may be proprietary, the evaluation team should still review these processes and establish safeguards for vendors.

article thumbnail

Bridging code and conscience: UMD’s quest for ethical and inclusive AI

AI News

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. As AI increasingly influences decisions that impact human rights and well-being, systems have to comprehend ethical and legal norms.

article thumbnail

Who Is Responsible If Healthcare AI Fails?

Unite.AI

Similarly, what if a drug diagnosis algorithm recommends the wrong medication for a patient and they suffer a negative side effect? At the root of AI mistakes like these is the nature of AI models themselves. Most AI today use “black box” logic, meaning no one can see how the algorithm makes decisions.

article thumbnail

easy-explain: Explainable AI for YoloV8

Towards AI

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. The truth is, I couldn’t find anything.

article thumbnail

How to Build AI That Customers Can Trust

Unite.AI

Transparency = Good Business AI systems operate using vast datasets, intricate models, and algorithms that often lack visibility into their inner workings. This opacity can lead to outcomes that are difficult to explain, defend, or challengeraising concerns around bias, fairness, and accountability.