Explainable AI

Explainable AI: Explainable AI is a term used to describe methods of making artificial intelligence more understandable to humans.

In recent years, Artificial Intelligence (AI) has become increasingly more prevalent in our daily lives. From driving cars to managing finances, AI is here and it’s making a big impact. But what about “Explainable AI?” What does that mean and why does it matter? It matters because explainable AI helps us understand the inner workings of this technology so we can trust its decisions and ensure they are right for us. Exploring explainable AI will help us better understand how these technologies make decisions, as well as their implications on human lives. In this article, an exploration of explainable AI will be undertaken through an analysis of its core components and benefits.

What Is Explainable AI Examples?

Explainable AI (XAI) is a branch of artificial intelligence that centres around the notion of making machines more interpretable, explainable, and transparent. XAI technologies have been developed to help humans understand how certain decisions are made by automated processes. It focuses on providing explanations for decision-making algorithms in order to make them easier to comprehend. Examples include decision trees, layer-wise relevance propagation, permutation importance, interpretable machine learning models, neural networks, decision trees with shapley values or model agnostic explanation methods.

This technology has become increasingly important as facial recognition technology becomes more ubiquitous in our lives. By increasing transparency and helping people better understand how these systems work, XAI can help ensure that decisions made using such systems are fair and accurate. Additionally, it can be used to improve trust between users and automated systems while promoting ethical use of Artificial Intelligence applications. Through the incorporation of Explainable AI into existing systems, we can gain deeper insight into their workings while also enabling us to create fairer and more efficient solutions.

What Is The Goal Of Explainable AI?

Explainable AI is a field of research that focuses on making the decisions and actions taken by intelligent systems more understandable to humans. It seeks to provide users with insight into how machine learning models make their predictions, why autonomous vehicles are taking certain paths, or why an algorithm has decided to reject an input. This goal is being achieved through various explainability techniques such as SHAP values, white box models, feature importance calculations and numerous other tools.

Explainable AI can be described as an effort to provide human users with the right to explanation for model decisions. This means giving users access to information about how a decision was made so they can understand it better. Explainable AI aims to bridge the gap between technical understanding of algorithms and decision-making approaches used in intelligent systems and what people intuitively expect from them.

The following list outlines some key aspects of explainable AI: 1) Explainable Machine Learning refers to efforts aimed at providing transparency around the inner workings of ML algorithms; 2) The Right To Explanation provides user access to explanations for algorithmic decisions; 3) Explainability Techniques involve using visualisations or methods like SHAP values which help humans understand the behaviour of ML models; 4) White Box Models involve developing transparent or interpretable models which allow insights into model behaviour without needing advanced technical expertise.

From autonomous cars navigating traffic jams to medical professionals diagnosing diseases, Explainable AI will play an important role in ensuring trustworthiness across all domains where intelligent systems impact human lives. By creating explainable models, developers can ensure that these machines are behaving responsibly and fairly while also enhancing user experience by providing clear explanations for complex decisions made by their algorithms.

Is Explainable AI Possible?

The question of whether explainable AI is possible has been a hot topic in the recent years. With the increasing influence and prevalence of Artificial Intelligence, people have become more aware of its potential to make decisions that are biased or wrong, as well as its lack of transparency. Explainable AI can be thought of as an attempt to address this problem by providing post-hoc explanations for machine learning models such as those used in deep learning and linear modelling. In order to achieve this, different types of explanation models are being studied which include interpretable models (also known as glass box models) that offer a clear understanding of how a model arrives at its decision, versus black box models which do not provide any insight into their inner workings.

While it is difficult to determine if explainable AI is entirely achievable due to the complexity and variability in Machine Learning models, research suggests that there is hope towards making them more transparent and accountable. For example, new methods such as counterfactual reasoning could possibly help identify bias in AI systems by allowing users to simulate alternative scenarios based on what was observed. Additionally, other approaches like game theory may prove useful when designing algorithms with built-in incentives for fairness. Ultimately, these efforts will require further development before they can be useful but suggest promise towards improving upon existing approaches such as using explainable AI techniques to reduce undesirable outcomes from automated decision making processes.

What Are The 4 Types Of Artificial Intelligence?

Explainable Artificial Intelligence (AI) is an emerging field of research that focuses on understanding the workings and decision making processes behind AI models. The four main types of artificial intelligence are:

neural networks,
deep learning,
model predictions
and deep reinforcement learning.

Neural Networks are composed of interconnected nodes which process information based on input data; when presented with a new set of inputs, they can generate outputs in order to identify patterns or make decisions. Deep Learning uses multiple layers of processing within a network so that it can detect more complex patterns than conventional machine learning algorithms. Model Predictions involve using statistical methods to create mathematical models that accurately predict outcomes by analysing existing datasets; these predictions provide further insight into how the model works and helps improve its performance. Finally, Deep Reinforcement Learning is an AI technique that utilises rewards as feedback signals for improving decision-making processes over time.

By exploring each type of AI individually, researchers have been able to gain insights into their structure and function which could ultimately help explain why certain model predictions occur. However, this approach has limitations due to the complexity of many systems; thus, there needs to be alternative approaches for providing meaningful explanations about model behaviour such as visual explanations or using interactive tools from crossref Google Scholar libraries. Ultimately, Explainable Artificial Intelligence provides valuable opportunities for identifying potential flaws in decision making processes while also allowing users to understand how AI functions at a deeper level than before - paving the way for future applications like self-driving cars or medical diagnosis assistants.

What Are 3 Different Examples Of AI Doing Things Today?

The current question is what are three different examples of AI doing things today? In the realm of explainable AI, there is a plethora of applications utilising Artificial Intelligence (AI) that have enabled numerous practical and functional solutions. One such example includes machine learning algorithms supported by deep learning techniques to provide advanced robotics for autonomous systems. These robots can be deployed in areas where manual labour may not work or be feasible due to safety concerns, allowing machines to perform complex tasks with greater efficiency than humans ever could.

Another example relates to image recognition technology powered by AI which has dramatically improved accuracy levels when predicting outcomes based on images. This type of AI-driven application runs on neural network approaches and offers increased prediction accuracy compared with traditional methods. Such applications also facilitate user trust as they become more transparent thanks to values of openness inherent in their design.

Finally, another area where AI is being used successfully is its ability to make decisions faster and better than humans do through predictive analytics and decision making capabilities. By leveraging vast amounts of data alongside sophisticated computing techniques, companies are able to develop models that help them make better business decisions quickly while ensuring customer satisfaction at the same time. As these technologies continue to evolve, so too will their potential for use across many industries from healthcare to finance and beyond.

Conclusion

Explainable AI is a rapidly growing field of artificial intelligence that seeks to provide more transparency into the decisions produced by algorithms. It has been gaining traction in recent years as organizations look for ways to understand and explain their AI-powered systems better. The goal of explainable AI is to ensure fairness, accountability, and trustworthiness in decision making processes powered by machine learning models.

The four types of AI currently used are: Reactive Machines, Limited Memory Systems, Theory Based Expertise Models and Self-Learning Algorithms. These different forms can be applied to various fields such as robotics, natural language processing (NLP), image recognition or medical diagnosis. Examples include Alexa providing voice commands; self-driving cars using visual sensors; facial recognition software identifying people from images; chatbots responding quickly to customer inquiries; and digital assistants helping with complex tasks like financial planning.

Ultimately, Explainable AI offers many advantages including improved accuracy, speed and scalability while maintaining compliance with legal requirements regarding data privacy and ethical use of AI technology. Furthermore, it allows businesses to better understand how certain decisions have been made through machines’ learning process so they can adjust accordingly if needed. With its increasing popularity among organisations today, Explainable AI is expected to become an important tool for improving decision making processes in the future.

PREVIOUS NARROW AI GLOSSARY TERM

Expert Systems

NEXT NARROW AI GLOSSARY TERM

Framebased systems

Explainable AI Definition

Exact match keyword: Explainable AI N-Gram Classification: Explainable Artificial Intelligence, XAI Substring Matches: Explain, AI Long-tail variations: "Explainable Artificial Intelligence", "XAI" Category: Technology, Artificial Intelligence Search Intent: Information, Learn Keyword Associations: Machine Learning, Data Science, Natural Language Processing Semantic Relevance: Machine Learning Algorithms, Interpretability in AI, Knowledge Engineering Parent Category: Technology Subcategories: Machine Learning Algorithms, Bayesian Analysis and Probability Theory Synonyms: Interpretable AI,Explainability in AI Similar Searches: ML Interpretability Techniques , Enhancement of Human Reasoning Geographic Relevance: Global Audience Demographics: Scientists, Researchers , Engineers Brand Mentions : IBM Watson , Microsoft Azure , Amazon Alexa Industry-specific data : Model summarization techniques ,Model interpretability methods Commonly used modifiers : "Systems", "Research" ,"Example" Topically Relevant entities : ML Interpretability Techniques , Enhancement of Human Reasoning , Model Summarization Techniques , Model Interpretability Methods.

Contact

To schedule a demo or learn more about our software products, please contact us:

"Larry will be our digital expert that will enable our sales team and add that technological advantage that our competitors don't have."

Kerry Smith
CEO, PFD Foods
$1.6 billion in revenue
PFD Food Services uses Complexica's Order Management System

"Lion is one of Australasia’s largest food and beverage companies, supplying various alcohol products to wholesalers and retailers, and running multiple and frequent trade promotions throughout the year. The creation of promotional plans is a complicated task that requires considerable expertise and effort, and is an area where improved decision-making has the potential to positively impact the sales growth of various Lion products and product categories. Given Complexica’s world-class prediction and optimisation capabilities, award-winning software applications, and significant customer base in the food and alcohol industry, we have selected Complexica as our vendor of choice for trade promotion optimisation."

Mark Powell
National Sales Director, Lion
Lion

"At Liquor Barons we have an entrepreneurial mindset and are proud of being proactive rather than reactive in our approach to delivering the best possible customer service, which includes our premier liquor loyalty program and consumer-driven marketing. Given Complexica’s expertise in the Liquor industry, and significant customer base on both the retail and supplier side, we chose Complexica's Promotional Campaign Manager for digitalizing our spreadsheet-based approach for promotion planning, range management, and supplier portal access, which in turn will lift the sophistication of our key marketing processes."

Richard Verney
Marketing Manager
Liquor Barons

"Dulux is a leading marketer and manufacturer of some of Australia’s most recognised paint brands. The Dulux Retail sales team manage a diverse portfolio of products and the execution of our sales and marketing activity within both large, medium and small format home improvement retail stores. We consistently challenge ourselves to innovate and grow and to create greater value for our customers and the end consumer. Given the rise and application of Artificial Intelligence in recent times, we have partnered with Complexica to help us identify the right insight at the right time to improve our focus, decision making, execution, and value creation."

Jay Bedford
National Retail Sales Manager
Dulux

"Following a successful proof-of-concept earlier this year, we have selected Complexica as our vendor of choice for standardizing and optimising our promotional planning activities. Complexica’s Promotional Campaign Manager will provide us with a cloud-based platform for automating and optimising promotional planning for more than 2,700 stores, leading to improved decision-making, promotional effectiveness, and financial outcomes for our retail stores."

Rod Pritchard
Interim CEO, Metcash - Australian Liquor Marketers
$3.4 billion in revenue
Metcash_ALM_logo

"After evaluating a number of software applications and vendors available on the market, we have decided to partner with Complexica for sales force optimisation and automation. We have found Complexica’s applications to be best suited for our extensive SKU range and large set of customers, being capable of generating recommendations and insights without burdening our sales staff with endless data analysis and interpretation.

Aemel Nordin
Managing Director, Polyaire
Polyaire chooses Complexica for sales force optimisation and automation

"DuluxGroup is pleased to expand its relationship with Complexica, a valued strategic partner and supplier to our business. Complexica’s software will enable DuluxGroup to reduce the amount of time required to generate usable insights, increase our campaign automation capability, personalise our communications based on core metrics, and close the loop on sales results to optimise ongoing digital marketing activity."

James Jones
Group Head of CRM, DuluxGroup

"Instead of hiring hundreds of data scientists to churn through endless sets of data to provide PFD with customer-specific insights and personalised recommendations, Larry, the Digital Analyst® will serve up the answers we need, when we need them, on a fully automated basis without the time and manual processes typically associated with complex analytical tasks.”

Richard Cohen
CIO, PFD Foods
$1.6 billion in revenue
PFD_Food_Services

"As a global innovator in the wine industry, Pernod Ricard Winemakers is always seeking ways to gain efficiencies and best practices across our operational sites. Given the rise of Artificial Intelligence and big data analytics in recent times, we have engaged Complexica to explore how we can achieve a best-in-class wine supply chain using their cloud-based software applications. The engagement is focused on Australia & New Zealand, with a view to expand globally."

Brett McKinnon
Global Operations Director, Pernod Ricard Winemakers
Pernod_Ricard_Logo

"70% - 80% of what we do is about promotional activity, promotional pricing -- essentially what we take to the marketplace. This is one of the most comprehensive, most complex, one of the most difficult aspect of our business to get right. With Complexica, we will be best in class - there will not be anybody in the market that can perform this task more effectively or more efficiently than we can."

Doug Misener
CEO, Liquor Marketing Group
1,400+ retail stores
Liquor Marketing Group LMG uses Complexica's Promotional Campaign Manager

"The key thing that makes such a difference in working with Complexica is their focus on delivering the business benefits and outcomes of the project."

Doug Misener
CEO, Liquor Marketing Group
1,400+ retail stores
Liquor Marketing Group LMG uses Complexica's Promotional Campaign Manager

"Australia needs smart technology and people, and it has been a great experience for me to observe Complexica co-founders Zbigniew and Matt Michalewicz assemble great teams of people using their mathematical, logic, programming, and business skills to create world-beating products. They are leaders in taking our bright graduates and forging them into the businesses of the future."

Lewis Owens
Chairman of the Board, SA Water

"Having known the team behind Complexica for some years ago now, I am struck by their ability to make the complex simple - to use data and all its possibilities for useful purpose. They bring real intelligence to AI and have an commercial approach to its application."

Andrew McEvoy
Managing Director, Fairfax Media - Digital

"I have worked with the team at Complexica for a number of years and have found them professional, innovative and have appreciated their partnership approach to delivering solutions to complex problems."

Kelvin McGrath
CIO, Asciano

“Working with Complexica to deliver Project Automate has been a true partnership from the initial stages of analysis of LMG’s existing processes and data handling, through scoping and development phase and onto delivery and process change adoption. The Complexica team have delivered considerable value at each stage and will continue to be a valued partner to LMG."

Gavin Saunders
CFO, Liquor Marketing Group
Liquor Marketing Group LMG uses Complexica's Promotional Campaign Manager

“Complexica’s Order Management System and Larry, the Digital Analyst will provide more than 300 Bunzl account managers with real-time analytics and insights, to empower decision making and enhanced support. This will create more time for our teams to enable them to see more customers each day and provide the Bunzl personalised experience.”

Kim Hetherington
CEO, Bunzl Australasia

"The team behind Complexica develops software products that are at the cutting edge of science and technology, always focused on the opportunities to deliver a decisive competitive edge to business. It has always been a great experience collaborating with Matthew, Zbigniew and Co."

Mike Lomman
GM Demand Chain, Roy Hill Iron Ore

"The innovations that the Complexica team are capable of continue to amaze me. They look at problems from the client side and use a unique approach to collaborating with and deeply understanding their customers challenges. This uniquely differentiates what they bring to market and how they deliver value to customers."

John Ansley
CIO, Toll Group
toll_logo

"Rather than building out an internal analytics team to investigate and analyse countless data sets, we have partnered with Complexica to provide our sales reps with the answers they need, when they need them, on a fully automated basis. We are excited about the benefits that Larry, the Digital Analyst will deliver to our business.”

Peter Caughey
CEO, Coventry Group
Coventry_Group_v2

Kim Hetherington
CEO, Bunzl Australasia

"After an evaluation process and successful proof-of-concept in 2016, we have chosen to partner with Complexica to upgrade the technological capability of our in-field sales force. The next-generation Customer Opportunity Profiler provided by Complexica will serve as a key tool for sales staff to optimise their daily activities, personalise conversations and interactions with customers, and analyse data to generate actionable insights."

Stephen Mooney
Group Sales Capability Manager, DuluxGroup
$1.7 billion in revenue
Dulux Group uses Complexica's Customer Opportunity Profiler

"After evaluating a number of software systems available in the marketplace, we have ultimately selected Complexica as our vendor of choice for sales force automation and CRM. Given the large SKU range we carry and very long tail of customers we serve, Complexica’s applications are best suited to deal with this inherent complexity without burdening our staff with endless data entry."

Nick Carr
CEO, Haircaire Australia
Australia's largest distributor of haircare products
Haircare Australia to use Complexica's Customer Opportunity Profiler, CRM and Order Management System

“Asahi Beverages is Australia’s largest brewer, supplying a leading portfolio to wholesalers and retailers, including some of Australia’s most iconic brands. Last year Asahi Beverages acquired Carlton & United Breweries, which is its Australian alcohol business division. To harness the strength of our expanded portfolio, we partner with our customers to run multiple and frequent trade promotions throughout the year, delivering long-term growth for both our business and theirs. Given the inherent complexity in optimising promotional plans and our continued focus on revenue and growth management, we have selected Complexica as our vendor of choice after a successful Proof-of-Concept of its world-class optimisation capabilities.”

Kellie Barnes
Group Chief Information Officer
Asahi Beverages
Asahi