We introduce MarkDiffusion, an open-source Python toolkit for generative watermarking of latent diffusion models. It comprises three key components: a unified implementation framework for streamlined watermarking algorithm integrations and user-friendly interfaces; a mechanism visualization suite that intuitively showcases added and extracted watermark patterns to aid public understanding; and a comprehensive evaluation module offering standard implementations of 24 tools across three essential aspects - detectability, robustness, and output quality - plus 8 automated evaluation pipelines. Through MarkDiffusion, we seek to assist researchers, enhance public awareness and engagement in generative watermarking, and promote consensus while advancing research and applications.

Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech data. It generalizes well to various speech recognition and translation benchmarks even in a zero-shot setup. However, the full pipeline for developing such models (from data collection to training) is not publicly accessible, which makes it difficult for researchers to further improve its performance and address training-related issues such as efficiency, robustness, fairness, and bias. This work presents an Open Whisper-style Speech Model (OWSM), which reproduces Whisper-style training using an open-source toolkit and publicly available data. OWSM even supports more translation directions and can be more efficient to train. We will publicly release all scripts used for data preparation, training, inference, and scoring as well as pre-trained models and training logs to promote open science.

Artificial intelligence is advancing rapidly, with recent estimates by PwC predicting that AI will contribute 13.7 trillion USD to the world economy by 2030. Consequently, there is a need for open-source toolkits that help developers build responsible AI systems. However, it is also critical to understand the implications of developing and deploying AI systems responsibly and ethically. The increased use of artificial intelligence (AI) has led to concerns about its potential impact on society. One way to mitigate these concerns is to develop responsible AI systems that consider the ethical principles of beneficence, non-maleficence, autonomy, and justice. There are several open-source toolkits available that can be used to develop responsible AI systems.

We present LEGOEval, an open-source toolkit that enables researchers to easily evaluate dialogue systems in a few lines of code using the online crowdsource platform, Amazon Mechanical Turk. Compared to existing toolkits, LEGOEval features a flexible task design by providing a Python API that maps to commonly used React.js interface components. Researchers can personalize their evaluation procedures easily with our built-in pages as if playing with LEGO blocks. Thus, LEGOEval provides a fast, consistent method for reproducing human evaluation results. Besides the flexible task design, LEGOEval also offers an easy API to review collected data.

According to research, it is estimated that AI could contribute $15.7 trillion to the global economy by 2030. As AI-enhanced products diffuse into markets and households, service providers have a gigantic task to facilitate interpretable algorithmic decision making. Over the past couple of years, many fairness tools have been introduced to make ML models more fair and explainable. In the next section, we list popular tools by the likes of Google and Microsoft, which are designed to imbue fairness into ML pipeline building. The Model Card Toolkit is designed to streamline and automate the generation of Model Cards.

IBM Security unveiled an open-source toolkit at RSA 2018 that will allow the cyber community to test their AI-based security defenses against a strong and complex opponent in order to help build resilience and dependability into their systems. The toolkit, called the Adversarial Robustness Toolbox, goes beyond the usual collection of attacks used to test an AI's ability, Sridhar Muppidi, IBM Fellow, VP and CTO IBM Security told SC Media at RSA this week. The toolbox has been released on Github and is available for download. "So far, most libraries that have attempted to test or harden AI systems have only offered collections of attacks. While useful, developers and researchers still need to apply the appropriate defenses to actually improve their systems," he said.