In this guide, we will explore which case would benefit from explainable AI principles? in detail and also see the top benefits of Explainable AI Principles.
In this post, we’ll explore when and how to apply explainable AI principles to ensure the best possible outcomes, When working with artificial intelligence (AI), it’s important to consider the impact of your actions on Explainable.
In business, there are many times when data scientists need to explain their methods and results to those who are not trained in the complexities of AI.
In these cases, it is beneficial to follow some principles of explainable AI. In this blog post, we will discuss a few cases where these principles could be applied.
When learning about Explainable AI Principles, you need to decide what case might benefit most from these Explainable AI Principles to learn the benefits of explainable AI and make sure that you are getting the right results from your machine learning models.
These are some of the things to think about when looking into this, or if you already have started down this path it will help you troubleshoot your models and systems when they don’t seem to be giving the best possible outcomes.
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What is Artificial Intelligence?
Machines, especially computers, simulate human intelligence processes through artificial intelligence.
In the past, “artificial intelligence” has been associated with machines that mimic and display cognitive skills associated with the human mind, such as “learning” and “problem-solving
Research on AI has since rejected this definition. Now, major AI researchers describe AI in terms of rationality and rational behavior, which does not limit the ways in which intelligence can be defined.
Machine or computer vision, speech recognition, natural language processing, and expert systems are some of the applications of artificial intelligence.
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What is Explainable AI?
A machine-learning system should provide explanations which are clear to its users so that they can understand what the system is doing.
In machine learning algorithms, explainable artificial intelligence (XAI) refers to a set of tools and processes that make the results and output understandable and trustworthy to human users.
The concept of explainable AI is used to describe the impact and potential biases of a certain AI model.
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Applications of Explainable AI
The Benefit From Explainable AI Principles are different and you need to know the right Application of Explainable AI also for the different AI projects:
1. Explainable AI is utilised in the healthcare industry, financial sector and many other domains.
2. Explainable AI (XAI) is a field of computer science and artificial intelligence concerned with questions regarding the explanation of artificial systems.
3. The Explainable AI tackles the problem of trust between humans and machines.
4. Explainable AI is becoming increasingly important in the world of artificial intelligence.
AI technology is transforming the world we live in.
5. We’re starting to see AI applied across industries from healthcare to retail, and even our smartphones are getting an AI upgrade.
6. What started out as a potentially dangerous weapon has evolved into something that can be used to make life easier (and more convenient).
The Key Examples of Explainable AI
The Following Cases Would Benefit From Explainable AI Principles, and this are the Top cases of Examples we mostly consider in Explainable AI:
1. Music Streaming Platform Recommendation
A song that is a good fit for their musical preferences, Music streaming platform recommending a song for medical students
I’m making a soundtrack for my game.
If you’re using the Alexa service, you can now ask her to play a song based on genre. Next: Alexa also supports most of the same music genres that it did before.
It’s also worth noting that Spotify doesn’t always seem to deliver the first song in a playlist as the one recommended for you.
2. Diagnosis System for Doctors
The AI based Diagnosis system for doctors helps lots, This doctor could make decisions faster than a human can.
Lots of doctors and scientists concluded that the AI-based system was right in 90% of cases.
This doctor would be a “stepping stone” to more accurate diagnosis. Next: Moore’s Law suggests that the number of transistors needed to power AI will fall and grow exponentially as we progress forward.
The AI-based system was successful in cases where the doctor’s choice of a medication was wrong.
The doctor will put the patient’s medical history into a computer, which will then make an AI-based diagnosis.
3. Navigation Platform for Route Finding
A navigation platform suggesting fastest routes based on your home location
MapQuest is a navigation platform suggesting fastest routes for pedestrians and vehicles. After you register, you’ll be able to select the data field that’s missing from your blog.
- Route-planning platform for car travel based on real-time traffic conditions.
- A navigation platform that suggests the fastest routes
- The navigation platform only suggests the fastest routes; it does not currently alert for traffic or accidents.
4. Social Media Platforms
This is an explanation of how artificial intelligence can be used to identify faces from images on a social media platform.
Facebook designed a system that uses advanced technology to automatically identify people in photos posted on the site.
The system then determines if the face has been previously identified the social media platform suggested I used other faces.
It usually focuses on inanimate objects, not people and then displays them at the top of your friend list
5. Explainable AI For HealthCare
Health care is a good place to start, in part because it’s an area where AI could be extremely useful.
Medical staff would be able to spend more time interpreting cases as opposed to taking care of repetitive tasks thanks to explainable AI.
As a result, healthcare is one of the best places to start, in part because AI could be extremely beneficial in this field.
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Explainable AI Principles are basically the principles of explainable AI that help you understand and interpret the predictions made by your machine learning models and to ensure the best possible outcomes.
It is beneficial to follow some principles of explainable AI and learn the benefits of explainable AI from one example, in order to understand Explainable AI Principles easily and more effectively.
There are many uses cases to apply Explainable AI Principles where there is an advantage of using explainable models like Interpretability in Medical Diagnosis, making decisions with fewer errors, among many others.
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