Top 10 Responsible AI Tools Businesses Should Use in 2022

Every organization needs to adopt Responsible AI to accelerate innovation with AI models

The constant innovation and development of the artificial intelligence infrastructure have transformed intercept workplaces today. AI has revolutionized the way we live our daily lives by constantly supporting avant-garde automated solutions. But experts have revealed that we have to integrate certain boundaries and maintain the responsible utilization of such an advanced tech as AI.  Responsible AI is known as a governance framework for an organization to follow and address multiple challenges related to Artificial Intelligence. It helps to ensure that AI models have human-centered programs incorporated with machine learning algorithms. Responsible AI is one of the major emerging areas of Artificial Intelligence governance while covering ethics and legal concerns. Responsible AI is the design, development, and deployment of AI to empower employees and businesses and have a fair impact on customers and society. Every organization needs to adopt Responsible AI to accelerate innovation with AI models efficiently and effectively to gain a competitive edge in the global market. Let's look at some of the top 10 Responsible AI tools businesses should use in 2022.

Fawkes

With the use of the algorithm and software application known as Fawkes, people may restrict the ability of unidentified third parties to monitor them by creating face recognition models from their publicly accessible pictures. Preventing harmful models from detecting personal photos entails distorting or cloaking the images.

Gluru

Gluru is one of the most widely used AI tools, which helps the user keep track of their calendar, track events, meetings, etc., and provides updates on deadlines, to-do lists, occasions, and various other appointments. It will automatically arrange all the e-mails, notes, and other information needed throughout these occasions.

AdverTorch

A Python toolkit for adversarial robustness research is called AdverTorch. AdverTorch specifically includes scripts for negative training and modules for producing adversarial perturbations and fighting against hostile instances. PyTorch has been used to implement the main functions.

TensorFlow Privacy

A Python module called TensorFlow Privacy contains TensorFlow optimizers that may be used to train machine learning models with differential privacy.

TensorFlow Federated

The Federated Learning (FL) method to machine learning, where a shared global model is built across multiple participating clients that maintain their training data locally, has been the focus of TFF's development to support open research and experimentation.

Conversica

Conversica is designed to reach out and connect with the gross sales leads. Conversica is a virtual sales assistant, which helps the sales team to focus on selling and closing the deals, rather than chasing down sales leads and prospects. This AI tool connects with the leads multiple times for whatever duration of time required, through automated, two-way email conversations.

Quill

Individuals looking to incorporate artificial intelligence in small businesses must try Quill. This tool uses certain specific sets of data and converts them into a written document, which helps to automate processes such as generating earnings reports, etc.

AI Fairness 360

To identify and reduce bias in machine learning models during the AI application lifecycle, the research community has created the extensible open-source AI Fairness 360 toolbox from IBM.

Fairlead

A Python library called Fairlearn gives creators of artificial intelligence (AI) systems the ability to evaluate their design's fairness and address any reported unfairness concerns. Fairlead includes metrics for model evaluation as well as mitigation methods.

TextAttack

TextAttack is a Python framework for NLP data augmentation, adversarial attacks, and training. With TextAttack, testing the robustness of NLP models is simple, quick, and seamless. Additionally, it helps with data augmentation, adversarial training, and the training of NLP models.