With time, artificial intelligence is becoming an increasing part of our lives. From image and facial recognitions to hyper-personalized applications, it is becoming crucial to trust these AI-based systems in decision-making. 

The future of artificial intelligence seems to be in an efficient collaboration that requires communication, understanding, transparency, and trust.

But ML/AI models are often thought of as black boxes that are opaque and intricate for people to decipher. This is where explainable AI, also known as (XAI) and transparent artificial intelligence, can come as a solution. 

Definition of explainable AI

Explainable Artificial Intelligence (or XAI) is an emerging field that integrates techniques in machine learning, statistics, cognitive science, and object-oriented programming. Explainable AI aims to create artificially intelligent systems that people can understand through explanations rather than relying on high-level rules.

We all want artificial intelligence to make better decisions for us. But these machines will only become more powerful if they can explain how they came to that decision. Explainable AI refers to that intelligent system’s ability to “translate complex human-like expert behaviors into algorithms that produce interpretation, prediction, and action.” Simply put, explainable AI is an exciting new technology that could change the way people interact with computers.

What makes explainable AI important?

Machine learning algorithms are fast becoming the basis of nearly everything we do online, from home assistants to predictive music recommendations. The difficulty faced by humans in understanding the reasoning behind AI decisions is well-known, though there are multiple ways of trying to explain AI decisions. Explainable Artificial Intelligence (XAI) aims at making AI decision-making understandable for both humans and machines alike. And this ‘interpretability’ aspect must be included when any machine learning algorithm is used. XAI has many advantages over existing explanations, especially when the tasks involve inductive reasoning or extensive abstraction.

Explainable Artificial Intelligence is becoming more critical for AI systems because it prompts research into the development of better explanations about the decisions made by AI systems. The main reason for this is that humans tend to find it difficult to understand why an Artificial Intelligence system has decided. Thus, explainable Artificial Intelligence provides an opportunity to develop new valuable technologies and help us create better AI systems in the future.

Loopholes of explainable AI

1. Confidentiality

AI algorithms are confidential, and explainable AI may lack the training information, model, or goal function to maintain security. As a result, ensuring that the system doesn’t have a biased perspective for confidential information can be challenging. Furthermore, it can be a security risk to reveal this information.

2. Complexity

Although algorithms are easy-to-understand, at the same time, they are highly complex. So, a non-specialist may find it illogical to understand it clearly. However, XAI methods may be helpful because they can create alternative algorithms that are easier to understand.

3. Immoderateness

If the algorithm is to make decisions based on data, it should be able to prove such decisions are logical and not influenced by outside factors. The problem is that there are no standards or procedures in place for creating AI algorithms.

For AI to be explainable, the AI must have a logical process underlying its decision-making.

Possible workaround solutions to overcome the loopholes of explainable AI

There’s no obvious shortcut to building an artificial intelligence system with the human brain’s capabilities. But there are ways, many of them, to get around these limits.

Model-agnostic approaches

Model-agnostic methods aim at solving AI’s limitations by treating the entire class of algorithms as a black box rather than focusing on the specific algorithm being studied. Researchers in the AI community have proposed model-agnostic approaches such as model-free optimization and model-free reinforcement learning. However, these approaches sidestep the need to understand the inner workings of the models but rely on deep-learning techniques instead.

Model-specific approaches

Model-specific approaches are used only for specific techniques or narrow classes of methods. This approach treats the internal workings of the model as a white box. While considering it, local interpretations focus on explicit data points. On the other hand, global interpretations focus on general patterns across all data points.

What is transparent artificial intelligence?

Transparent Artificial Intelligence is a term for a class of computer-implemented intelligence systems that explain how they arrived at an answer. In contrast to black-box AI, a transparent AI system explains the reasoning that an automated decision-making system did follow before arriving at its conclusion.

In other words, transparent artificial intelligence is a term used to describe an artificial intelligence system that can explain its users’ decisions. For example, the decision to “search for Alice” could be explained transparently by saying, “I searched for Alice because I learned that she is in the marketing department.”

The underlying connection between transparent artificial intelligence and explainable AI

Transparent artificial intelligence and explainable AI fall under the umbrella of so-called ‘artificial general intelligence or AGI. These terms refer to a future where artificial intelligence—computers that think, feel, perceive the world and communicate with humans—will have an intellectual capability equal to humans.

Transparent artificial intelligence is a paradigm in which a machine is created without hiding the computation behind its decisions. Explainable AI is where the software can explain its findings and reasoning to a person or other agent. Thus, transparency means that a machine has its state expressed in some form that people understand. Explainable AI is not an inherent property of AI – it is a product of incorporating transparency into the system.

Explainable AI, like transparent artificial intelligence (AI), is one of the most exciting applications of AI. In short, explainable AI allows for a machine to explain why it reached an inevitable conclusion.

The positive impact of transparent and explainable AI

Transparency and responsibility are crucial when it comes to the future of AI. Hence, it becomes essential for companies to have a certain degree of control over their AI models being deployed.

Some people fear we might lose control. But, by creating transparency and establishing clear-cut guidelines and procedures for creating AI applications, it is possible to realize the true potential of AI soon. 

Despite the Terminator-inspired narratives you read in media articles, most AI-powered algorithms being developed right now have a sense of innocence, and they do not make any high-impact decisions. 

Self-driving cars are a perfect example of transparent and explainable AI. In the near future, we will see self-driving cars that will have life and death situations programmed. This is where you can ensure that these algorithms are built correctly. 

In the long run, transparent and understandable AI will help avoid disasters and help realize the positive potential of AI in making significant contributions to our lives. 

There is an immense potential for AI but, it ultimately boils down to the amount of trust entrusted to it by the general public.

With the necessary considerations of transparency and responsibility in place, AI can make an enormous difference in our lives in the time to come!

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