Is it possible to make artificial intelligence more transparent?

In order to make artificial intelligence more ethically sound and practical, it is crucial to enhance the interpretability of deep neural networks.

Transparency around AI efforts can cause headaches for organizations integrating the technology into their daily operations. So what can be done to allay concerns about the need for explainable AI?

The profound benefits of AI in any industry are well known. We realize how this technology is helping thousands of businesses around the world speed up their operations and use their employees more imaginatively. Additionally, the long-term costs and data security benefits of AI have been documented countless times by technology columnists and bloggers. However, artificial intelligence does have its fair share of problems. One of the problems is that the technology's decision-making is sometimes questionable. But more importantly, the bigger issue is the slight lack of explainability whenever AI-driven systems go wrong in embarrassing or catastrophic ways.

Humans make mistakes every day. However, we know exactly how errors arise. A clear set of corrective actions can be taken to avoid the same mistakes in the future. However, some errors in AI are unexplainable because data experts have no idea how the algorithm reaches a specific conclusion in its operation. Therefore, explainable AI should be a top priority for both organizations planning to implement the technology into their daily work and those already incorporating it.

What makes artificial intelligence explainable

A common fallacy about artificial intelligence is that it is completely infallible. Neural networks, especially in their early stages, can make mistakes. At the same time, these networks carry out their orders in a non-transparent manner. As mentioned earlier, the path an AI model takes to reach a specific conclusion is not clear at any point during its operation. Therefore, it is almost impossible for even experienced data experts to explain such errors.

The issue of transparency in artificial intelligence is particularly acute in the healthcare industry. Consider this example: A hospital has a neural network or a black box AI model that diagnoses a patient’s brain disease. The intelligent system is trained to look for patterns in data from past records and patients' existing medical files. With predictive analytics, if a model predicts that a subject will be susceptible to a brain-related disease in the future, the reasons behind the prediction are often not 100 percent clear. For both private and public institutions, here are 4 main reasons to make AI efforts more transparent:

1. Accountability

As mentioned before, stakeholders need to know about AI models The inner workings and reasoning behind the decision-making process, especially for unexpected recommendations and decisions. An explainable AI system can ensure that algorithms make fair and ethical recommendations and decisions in the future. This can increase compliance and trust in AI neural networks within organizations.

2. Greater Control

Explainable artificial intelligence can often prevent system errors from occurring in work operations. More knowledge about existing weaknesses in AI models can be used to eliminate them. As a result, organizations have greater control over the output provided by AI systems.

3. Improvement

As we all know, artificial intelligence models and systems require continuous improvement from time to time. Explainable AI algorithms will become smarter during regular system updates.

4. New discoveries

New information clues will enable mankind to discover solutions to major problems of the current era, such as drugs or therapies to treat HIV AIDS and methods to deal with attention deficit disorder. What's more, these findings will be backed up by solid evidence and a rationale for universal verification.

In AI-driven systems, transparency can be in the form of analytical statements in natural language that humans can understand, visualizations that highlight the data used to make output decisions, and visualizations that show the points that support a given decision. Cases, or statements that highlight why the system rejected other decisions.

In recent years, the field of explainable artificial intelligence has developed and expanded. Most importantly, if this trend continues in the future, businesses will be able to use explainable AI to improve their output while understanding the rationale behind every critical AI-powered decision.

While these are reasons why AI needs to be more transparent, there are some obstacles that prevent the same from happening. Some of these obstacles include:

AI Responsibility Paradox

It is known that explainable AI can improve aspects such as fairness, trust, and legitimacy of AI systems. However, some organizations may be less keen on increasing the accountability of their intelligent systems, as explainable AI could pose a host of problems. Some of these issues are:

Stealing important details of how the AI ​​model operates.

The threat of cyberattacks from external entities due to increased awareness of system vulnerabilities.

Beyond that, many believe that exposing and disclosing confidential decision-making data in AI systems leaves organizations vulnerable to lawsuits or regulatory actions.

To not fall victim to this “transparency paradox,” companies must consider the risks associated with explainable AI versus its clear benefits. Businesses must effectively manage these risks while ensuring that the information generated by explainable AI systems is not diluted.

Additionally, companies must understand two things: First, the costs associated with making AI transparent should not prevent them from integrating such systems. Businesses must develop risk management plans that accommodate interpretable models so that the critical information they provide remains confidential. Second, businesses must improve their cybersecurity frameworks to detect and neutralize vulnerabilities and cyber threats that could lead to data breaches.

The black box problem of artificial intelligence

Deep learning is an integral part of artificial intelligence. Deep learning models and neural networks are often trained in an unsupervised manner. Deep learning neural networks are a key component of artificial intelligence involved in image recognition and processing, advanced speech recognition, natural language processing and system translation. Unfortunately, while this AI component can handle more complex tasks than conventional machine learning models, deep learning also introduces black box issues into everyday operations and tasks.

As we know, neural networks can replicate the work of the human brain. The structure of artificial neural network is to imitate real neural network. Neural networks are created from several layers of interconnected nodes and other "hidden" layers. While these neural nodes perform basic logical and mathematical operations to draw conclusions, they are also smart and intuitive enough to process historical data and generate results from it. Really complex operations involve multiple neural layers and billions of mathematical variables. Therefore, the output generated from these systems has little chance of being fully verified and validated by AI experts in the organization.

Organizations like Deloitte and Google are working to create tools and digital applications that break through the black boxes and reveal the data used to make critical AI decisions to increase transparency in intelligent systems.

To make AI more accountable, organizations must reimagine their existing AI governance strategies. Here are some key areas where improved governance can reduce transparency-based AI issues.

System Design

In the initial stages, organizations can prioritize trust and transparency when building AI systems and training neural networks. Paying close attention to how AI service providers and vendors design AI networks can alert key decision-makers in the organization to early questions about the capabilities and accuracy of AI models. In this way, there is a hands-on approach to revealing some of the transparency-based issues with AI during the system design phase for organizations to observe.

Compliance

As AI regulations around the world become increasingly stringent when it comes to AI responsibilities, organizations can truly benefit from having their AI models and systems comply with these norms and standards. Benefit. Organizations must push their AI vendors to create explainable AI systems. To eliminate bias in AI algorithms, businesses can approach cloud-based service providers instead of hiring expensive data experts and teams. Organizations must ease the compliance burden by clearly instructing cloud service providers to tick all compliance-related boxes during the installation and implementation of AI systems in their workplaces. In addition to these points, organizations can also include points such as privacy and data security in their AI governance plans.

We have made some of the most astounding technological advances since the turn of the century, including artificial intelligence and deep learning. Fortunately, although 100% explainable AI does not yet exist, the concept of AI-powered transparent systems is not an unattainable dream. It’s up to organizations implementing these systems to improve their AI governance and take the risks to achieve this.

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