Cookie Preference Centre

Your Privacy
Strictly Necessary Cookies
Performance Cookies
Functional Cookies
Targeting Cookies

Your Privacy

When you visit any web site, it may store or retrieve information on your browser, mostly in the form of cookies. This information might be about you, your preferences, your device or used to make the site work as you expect it to. The information does not usually identify you directly, but it can give you a more personalized web experience. You can choose not to allow some types of cookies. Click on the different category headings to find out more and change our default settings. However, you should know that blocking some types of cookies may impact your experience on the site and the services we are able to offer.

Strictly Necessary Cookies

These cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site may not work then.

Cookies used

ContactCenterWorld.com

Performance Cookies

These cookies allow us to count visits and traffic sources, so we can measure and improve the performance of our site. They help us know which pages are the most and least popular and see how visitors move around the site. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies, we will not know when you have visited our site.

Cookies used

Google Analytics

Functional Cookies

These cookies allow the provision of enhance functionality and personalization, such as videos and live chats. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies, then some or all of these functionalities may not function properly.

Cookies used

Twitter

Facebook

LinkedIn

Targeting Cookies

These cookies are set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant ads on other sites. They work by uniquely identifying your browser and device. If you do not allow these cookies, you will not experience our targeted advertising across different websites.

Cookies used

LinkedIn

This site uses cookies and other tracking technologies to assist with navigation and your ability to provide feedback, analyse your use of our products and services, assist with our promotional and marketing efforts, and provide content from third parties

OK
BECOME
A MEMBER
TODAY TO:
CLICK HERE
[HIDE]

Here are some suggested Connections for you! - Log in to start networking.

The Evolution of Machine Learning: Explainable AI - Sestek - ContactCenterWorld.com Blog

The Evolution of Machine Learning: Explainable AI

 

Once a scene in science fiction movies, artificial intelligence is a natural part of our daily lives today.  When we open a film from Netflix or follow a profile on Instagram and get movie or profile suggestions in just as we want, not to mention Amazon’s real-time pricing or Google search’s semantic ability.

Companies invest in their talents and technologies more and more every day to get the best performance, precision, and accuracy from their algorithms to provide the information that users are hoping to find.

....NOTE - content continues below this message

DON'T MISS THIS!

We invite you and your colleagues to join us LIVE as we take the highest rated industry conference to the next level! the 2022 World's Best! - join us and the elite in the industry at the 17th annual NEXT GENERATION Contact Center & Customer Engagement GLOBAL Best Practices Conference!

>>>>> FIND OUT MORE: HERE


....CONTENT CONTINUED BELOW

According to Ritu Jyoti, the Vice President for AI Research at International Data Corporation (IDC), AI has become the top topic of all industries for its versatile application areas and resilience - and pandemic only magnified the effect. The same report mentions that the expected growth in revenues for AI solutions will reach $500 billion by 2024.

AI is known for its “black-box” nature, lack of transparency while offering almost limitless possibilities for developers and scientists who train AI decision systems based on a specific domain with providing no visibility or rationale behind it. This can be negligible when we talk about getting movie suggestions from Netflix, but if this system is used for disease diagnostic, or sentence a criminal subject, the crucial need for explaining AI is inevitable. Moreover, today’s industries and governments require these technologies to make sure that users or customers trust the AI-based systems when making decisions; they have the right choice with the help of AI. Therefore, finding an approach and developing algorithms that transform black-box systems to glass-box systems are significant. It will be possible for people to trust machines on their decisions by understanding how they think and why they choose what they choose. This is where explainable AI comes into play.

What is Explainable AI?

Explainable AI (XAI) is a suite of machine learning techniques that produce more explainable models. The main goal of XAI is to explain how algorithms come up with a decision and which factors affected their decision points and eventually ended up with that solution. It is an emerging field that sits at the intersection of different focuses: transparency, causality, bias, fairness, and safety.

XAI progress has affected by three accelerating factors:

  • Increasing ethical concerns. The growing need for transparency, required by laws like GDPR about how personal data is used.
  • As explained with examples in the introduction, before putting trust in machines’ decisions, humans need to be convinced. And that would be only possible with the explainability of AI systems.
  • Better human-machine synergy. Machines are part of our daily lives more than ever and enhanced our life with their wide range of functionality and increased intelligence. So, it is important to create an environment where both humans and machines are working together.

XAI was first introduced in 2004 by Van Let et al. to explain a game simulation that they developed to train the US army. Although it is a game, it is especially designed to better train the soldiers without losing the effectiveness of the education material. Full Spectrum Command consists of users (soldiers) and non-player characters that AI controls. After a mission is done, users can click on subordinates and ask questions within the system or review key aspects presented by the case. This study also significantly influenced the gaming industry and its technologies.

Understanding Explainable AI

A simple illustration is shared below to understand the main concept of XAI. There are few highlights worth mentioning. First, today’s section of the picture represents how the classical mechanism of ML works. The system gets training data, applies ML processes, and concludes or makes a recommendation according to how the model is trained. It is also important to notice that there is one-way interaction with the system’s user; this shows no explainability in this system. The user only sees the final decision made by the system.

On the other hand, there are explainable models and explainable interface layers in the XAI concept instead of the learned function. Explainable models are responsible for taking the task and offering a recommendation or an action; the interface is responsible for justifying the cause of why the system has made that decision. Then the user makes a final decision based on the explanation, so there is a two-way interaction between the system and the user. Explainability interfaces can benefit from Human-Computer Interaction (HCI) techniques to generate effective explanations.

Figure 1: Comparison of AI and XAI concepts proposed by DARPA

Some Application Areas

Explainable AI has a wide variety of application areas, including healthcare, finance, insurance, law, etc. Here healthcare and law case studies are briefly shared to give a holistic overview of how XAI contributes to industries.

Explaining Medical Diagnosis

While describing XAI, it is mentioned that XAI is deployed to convert a black box into a white box. No matter how advanced the decision model is, there is always a need for a human in the loop for approving that model gives the right decision and handles unexpected scenarios. The medical domain is a suitable topic for describing such cases. Convolutional neural networks used to interpret medical diagnostics by using computer-aided diagnostic systems. Patient data collected through Magnetic resonance imaging (MRI) and computed tomography (CT) scans and existing diagnostics archives were processed, and the model trained to make accurate diagnostics by looking at the patient’s scans. In this way, the research team trained the model to identify disease patterns by looking at the existing atlas.

XAI in Legal

Explainable AI has great potential in legal applications. Courts can benefit from XAI for its pragmatic and transparent approach like judicial reasoning, bottom-up approach while making decisions, case-by-case considerations of delicate cases, and even stimulating the paperwork required for legal settings and audiences. It is also believed that XAI helps law to become more transparent and become independent from private actors by becoming more publicized. Judge is the main consumer of XAI algorithms recommendations and decisions. Taking reasonable explanations for sentences, the likelihood of that crime will occur, due processes, etc. require explanations from XAI models. As real-life cases, data collected, and model is trained by the collected data; common law of XAI will be created. This law can be used to compensate the explanation requirements by criminal, civil, administrative law settings, or it can be used by judges, juries, defendants, etc.

Final Thoughts

Explainable AI emerged as an answer to the increasing need to understand machine learning and AI better. Being able to comprehend AI will help us build better human-machine collaboration and contribute to a transparent approach where ethical concerns and trust issues can be resolved easily.

 

Author: Gülşah Keskin, Product Analyst, Sestek

Source: https://www.sestek.com/2021/07/the-evolution-of-machine-learning-explainable-ai/

Publish Date: July 13, 2021


2022 Buyers Guide Contact Center Assessments

 
1.) 
The House of Contact Centers

LevelX4
Assess the roadmap to a higher potential for your customer contact center:

• Discover strenghts and opportunities in only 30 minutes.
• Receive a FREE roadmap and actionplan to growth.
• Determine in 39 statements the foundation for a futureproof contact center.

Click on the link below to start your FREE assessment!

2.) 
Snapshotz

Snapshotz
Snapshotz is a web-based audit tool with strong analytics that helps large and small organisations understand and benchmark customer experience delivery. Snapshotz reports contain validated CX practices, technology use and operational metrics from over 3,000+ customers globally. These are benchmarked against the ISO 18295 customer service standard, digital service delivery, health & safety and mental health and other global standards enabling validated roadmaps for CX strategy and investment
 



View more from Sestek

Recent Blog Posts:
Conversational Analytics: The Secret to Quality Customer ServiceSeptember 17, 2022
Perfecting the Airport Experience with Conversational AIAugust 16, 2022
CEO Interview: Knovvu is the Beginning of a New Era for SestekJuly 6, 2022
Employee Experience, Customer Experience, Total Experience: How are They All Connected?May 16, 2022
Speech Analytics Come to Rescue for Better CXApril 13, 2022
Voice: Still the Most Natural, the Most Comfortable and the SafestDecember 20, 2021
From Single-Use Bots to Intelligent One-for-All BotsNovember 11, 2021
Chatbot? Virtual Assistant? Digital Assistant? What’s The Difference?September 21, 2021
The Evolution of Machine Learning: Explainable AIJuly 13, 2021
Making Conversational AI Smarter: 4 Hints to Design an Intelligent Conversational AI SolutionMay 25, 2021

About us - in 60 seconds!

Submit Event

Upcoming Events

17th Annual NEXT GENERATION BEST PRACTICES CX & CC Conference & Expo aimed at those who operate in North and South Americas, Europe, Middle East & Africa, Asia Pacific will help you with award-winning strategies and tactics from the best in the regio... Read More...
 2813 

Newsletter Registration

Please check to agree to be placed on the eNewsletter mailing list.

Latest Americas Newsletter
both ids empty
session userid =
session UserTempID =
session adminlevel =
session blnTempHelpChatShow =
CMS =
session cookie set = True
session page-view-total = 1
session page-view-total = 1
applicaiton blnAwardsClosed =
session blnCompletedAwardInterestPopup =
session blnCheckNewsletterInterestPopup =
session blnCompletedNewsletterInterestPopup =