Tootfinder

Series D, Episode 10 - Gold
KEILLER: Your friend, Vila, he wants no part in this?
SOOLIN: He doesn't trust you, Keiller. He thinks it's a trap.
KEILLER: Suspicious.
AVON: And frequently right. How much further?
https://blake.torpidity.net/m/410/99 B7B5

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Tahiya Chowdhury, Veronica Romero
https://arxiv.org/abs/2506.11072

Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Machine learning-based behavioral models rely on features extracted from audio-visual recordings. The recordings are processed using open-source tools to extract speech features for classification models. These tools often lack validation to ensure reliability in capturing behaviorally relevant information. This gap raises concerns about reproducibility and fairness across diverse populations and contexts. Speech processing tools, when used outside of their design context, can fail to capture b…

Designing DSIC Mechanisms for Data Sharing in the Era of Large Language Models
Seyed Moein Ayyoubzadeh, Kourosh Shahnazari, Mohammmadali Keshtparvar, MohammadAmin Fazli
https://arxiv.org/abs/2506.05379

Designing DSIC Mechanisms for Data Sharing in the Era of Large Language Models
Training large language models (LLMs) requires vast amounts of high-quality data from institutions that face legal, privacy, and strategic constraints. Existing data procurement methods often rely on unverifiable trust or ignore heterogeneous provider costs. We introduce a mechanism-design framework for truthful, trust-minimized data sharing that ensures dominant-strategy incentive compatibility (DSIC), individual rationality, and weak budget balance, while rewarding data based on both quality …

This https://arxiv.org/abs/2503.16920 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Assessing AI Explainability: A Usability Study Using a Novel Framework Involving Clinicians
An AI design framework was developed based on three core principles, namely understandability, trust, and usability. The framework was conceptualized by synthesizing evidence from the literature and by consulting with experts. The initial version of the AI Explainability Framework was validated based on an in-depth expert engagement and review process. For evaluation purposes, an AI-anchored prototype, incorporating novel explainability features, was built and deployed online. The primary funct…