2016 Explainability Explainable AI 
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). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?
Pickl AI
APRIL 8, 2024
Algorithms on massive datasets, and if these datasets contain inherent biases, the AI will inherit them as well. Example In 2016, an investigation by ProPublica revealed that a risk assessment algorithm used in US courts to predict recidivism rates was biased against Black defendants. How Can We Ensure the Transparency of AI Systems?
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Pickl AI
JULY 24, 2024
The future of AI also holds exciting possibilities, including advancements in general Artificial Intelligence (AGI), which aims to create machines capable of understanding and learning any intellectual task that a human can perform. 2004: Discussions about Generative Adversarial Networks (GANs) begin, signalling the start of a new era in AI.
Heartbeat
FEBRUARY 2, 2024
In xxAI — Beyond Explainable AI Chapter. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments. Salewski, L., Koepke, A. Lensch, H. A., & Akata, Z. Open Access. DOI: [link] Das, A.,
Unite.AI
NOVEMBER 29, 2023
In the context of the electoral process, AI watchdogs are symbolized as AI-based systems to combat instances of disinformation to uphold the integrity of elections. Looking back at the recent past, the 2016 US presidential election result makes us explore what influenced voters' decisions.
Expert insights. Personalized for you.
52 
Let's personalize your content