A governance paradigm called "responsible AI" describes how a particular organization handles the ethical and legal issues around artificial intelligence (AI). Liable AI projects are primarily motivated by the need to clarify who is responsible if something goes wrong. The data scientists and software engineers who create and implement an organization's AI algorithmic models are responsible for developing appropriate, reliable AI standards. This indicates that each organization has different requirements for the procedures needed to stop prejudice and ensure transparency. Supporters of responsible AI believe that a widely accepted governance framework of AI best practices will make it simpler for organizations worldwide to ensure that their AI programming is human-centered, interpretable, and explainable, much like ITIL provided a common framework for delivering IT services.

Researchers from Citadel developed a deep learning method to generate DNS amplification attacks. Deep learning algorithms have lately been shown to be quite effective at identifying and preventing cybersecurity assaults. Various deep learning techniques, such as those used for image classification and natural language processing, have been the target of numerous cybercriminals' recent development of new attacks. The most common of these strategies are adversarial attacks, which use altered data to deceive deep learning algorithms into categorizing it incorrectly. This could lead to the failure of numerous deep learning-based apps, biometric systems, and other systems.

Being able to quickly get your hands on datasets for testing algorithms and building models has become a vital skill for contemporary data scientists and machine learning engineers. Being able to move a semi-complete dataset to one that is fully annotated, fleshed out with additional augmented data, and ready to move on to building models with is equally important. For those of us holding incomplete data, and needing some help getting it from "not quite" to "let's go!", here are 3 projects found around GitHub that can help with your data acquisition, annotation, and augmentation tasks. Need to build yourself a custom image dataset for your project? Google Images Download might be able to help.

This article talks about the concept of adversarial examples as applied to NLP (natural language processing). The terminology can be confusing at times, so we'll begin with an overview of the language used to talk about adversarial examples and adversarial attacks. We'll conclude with some thoughts on the future of this area of research. An adversarial example is an input designed to fool a machine learning model [1]. An adversarial example crafted as a change to a benign input is known as an adversarial perturbation.

While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop NLP attacks and utilize them to improve model performance. This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack's modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets, including BERT and other transformers, and all GLUE tasks. TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness. TextAttack is democratizing NLP: anyone can try data augmentation and adversarial training on any model or dataset, with just a few lines of code. Code and tutorials are available at https://github.com/QData/TextAttack.