Archives for Transparency
Meta's main revenue comes from advertising, and its straightforward integration of AI across its apps positions it well to capitalise on AI
The post Exploring Meta’s AI Endeavours: From Personas to Advantage+ & More appeared first on Analytics India Magazine.
A responsible AI framework enables companies to track and mitigate bias and create transparent and explainable AI models, prevent misuse and adverse effects of AI, determine who to be held responsible if something goes wrong, and ensure compliance with security, privacy, and associated regulations.
The post Council Post: Towards An Ethical Tech Revolution: Building Responsible AI Practices appeared first on Analytics India Magazine.
In our previous article, we detailed out the need for Trusted AI and discussed one of IBM Research Trusted AI toolkit called AIF360. I recommend you to read this article first for better understanding. In this article, we are going to discuss about AI Explainability 360 toolkit. The growing interactions of the world with AI…
The post Guide To AI Explainability 360: An Open Source Toolkit By IBM appeared first on Analytics India Magazine.
In our previous article, we detailed out the need for Trusted AI and discussed one of IBM Research Trusted AI toolkit called AIF360. I recommend you to read this article first for better understanding. In this article, we are going to discuss about AI Explainability 360 toolkit. The growing interactions of the world with AI…
The post Guide To AI Explainability 360: An Open Source Toolkit By IBM appeared first on Analytics India Magazine.
Private enterprises use several algorithms for decision making on a day-to-day basis. Many of them have also realised or have been made aware of the consequences of using algorithms that might be biased towards individuals or sections of society. To avoid such incidents from occurring again, many tech companies are taking steps to ensure FATE…
The post Top Third-party Initiatives To Ensure Algorithmic FATE For Enterprises appeared first on Analytics India Magazine.
Explainable AI refers to methods and techniques in the application of artificial intelligence such that the results of the solution can be understood by human experts. It contrasts with the concept of the "black box" in machine learning and enables transparency. The need for transparency could be seen in the increased interest of the researchers.…
The post What Makes Explainable AI So Difficult appeared first on Analytics India Magazine.
Due to the ambiguity in Deep Learning solutions, there has been a lot of talk about how to make explainability inclusive of an ML pipeline. Explainable AI refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by human experts. It contrasts…
The post 8 Explainable AI Frameworks Driving A New Paradigm For Transparency In AI appeared first on Analytics India Magazine.
Whether it is the market crash or a wrong diagnosis, the after-effects will be certainly irreversible. Hence, tracking the development of machine learning algorithm throughout its life cycle becomes crucial. Neural network activations have an underlying compositional, combinatorial structure. Visualising the behaviour of neural networks has been of great interest lately for two reasons —…
The post How Loss Change Allocation For Machine Learning Training Can Aid Transparency appeared first on Analytics India Magazine.
Deep learning education and tools are becoming more and more democratic each day. There are only a few major deep learning frameworks; and among them, PyTorch is emerging as a winner. PyTorch is an open-source machine learning library inspired by Torch. It has primarily been developed by Facebook‘s artificial intelligence research group, and Uber‘s Pyro…
The post 9 Reasons Why PyTorch Will Become Your Favourite Deep Learning Tool appeared first on Analytics India Magazine.
Will The Latest IBM Proposal For Supplier’s Declaration Improve Transparency in AI Algorithms?
Deep learning has had enormous impact on the fields of computer vision, natural language and many other fields. But deep learning models have also been plagued with unexplainability and lack of transparency. The black box nature of DL models is the chief cause for non-interpretability. Now, to overcome these shortcomings, researchers are focusing on ‘Explainable […]
The post Will The Latest IBM Proposal For Supplier’s Declaration Improve Transparency in AI Algorithms? appeared first on Analytics India Magazine.