Explainable artificial intelligence is more important than ever before.
Businesses and consumers are increasingly relying on artificial intelligence to make critical decisions. We’re not just using the latest AI solutions to boost productivity and efficiency. We’re turning to AI models for help planning schedules, creating products, designing workplaces, and more.
When the decisions we make based on AI input can affect everything from individual rights to human safety and business operations, it’s important to ensure we can trust intelligent insights.
Ensuring AI is trustworthy starts with ensuring it’s “explainable”. Businesses, consumers, and government leaders need to understand how and why AI models generate insights to ensure they make the right decisions. McKinsey’s research even shows that the companies that see the best bottom-line returns from AI are also the ones that focus on explainability.
Explainable AI improves decision-making accuracy, ensures companies can preserve their consumers’ trust and maintains compliance with evolving governance standards. But ensuring AI is explainable isn’t easy.
What is Explainable Artificial Intelligence?
Explainable artificial intelligence (or XAI) describes the processes and methods leveraged in AI development and usage to ensure humans can comprehend and trust machines’ output. It’s the key to eliminating the issues caused by “black box AI,” which leaves users confused about how AI systems work and why they generate specific responses.
As AI continues to grow more advanced, with intuitive machine learning algorithms, new training methods, and applications, it can be difficult to see how these systems work. Innovations in generative AI even caused some consumers to question whether some AI tools might be sentient.
When not even engineers or data scientists who create an algorithm or system know what’s happening “inside” of the solution, this causes significant problems. It makes it impossible to ensure AI outputs are accurate and determine why mistakes and errors occur.
Explainable AI helps users to understand the impact specific solutions can have on uses, any potential biases they might have, and how they ensure accuracy. In the business landscape, explainable AI is crucial to building trust with consumers, and ensuring new systems can be deployed with confidence.
Additionally, explainable AI is a core facet of developing “responsible” AI. Responsible or “ethical AI”, focuses on creating models that prioritize human oversight, enhance wellbeing, and protect users from damaging experiences. To create responsible AI, developers need to understand how their models, algorithms, and apps work.
How Explainable Artificial Intelligence Works
With explainable AI and interpretable machine learning, organizations and developers can constantly gain access to the underlying technology in their systems. This means they can track the processes that lead to certain decisions, actions, or outputs.
Ultimately, this means that human beings can “check the work” of their AI models, assessing it for any faulty thought processes that might lead to inaccurate outcomes. It also means that when AI systems make mistakes, developers know where those errors came from.
Unlike in standard AI systems, where an algorithm might arrive at a response with no explanation of how it got there, XAI systems use methods to trace and explain every processing stage. Developers rely on various strategies to create explainable AI, usually focusing on:
- Prediction accuracy: Developers run simulations and compare the outputs of XIA systems to the results in training data sets. This can help them to understand the prediction accuracy of the model. The most common method used for this is the “LIME”, or Local Interpretable Model-Agnostic Explanations model.
- Traceability: Another key component of explainable artificial intelligence, traceability is achieved often by restricting how decisions can be made by models. Developers create a narrower scope for ML rules and features, using tools like DeepLIFT to show a clear link between each activated “neuron” in a neural network.
- Decision understanding: This is the human element of explainable AI. Experts need to be trained on how AI models should work and which algorithms they use. That way, they can better understand how these systems make decisions.
Another element of explainable AI is continuous model evaluation. This basically means comparing model predictions, quantifying model risks, and looking for ways to troubleshoot issues and optimize models consistently.
Examples of Explainable AI Use Cases
The adoption of explainable artificial intelligence solutions is already commonplace in a variety of sectors – including highly regulated industries. Some examples of the use cases for XAI in different industries include:
- Healthcare: In healthcare, explainable AI can help accelerate diagnostic processes, improve image analysis, and enhance resource optimization. It can also improve transparency and traceability in decisions made about patient care and pharmaceutical development.
- Finance: In the financial landscape, explainable AI can improve customer experiences by ensuring a transparent loan and credit approval process. It can streamline wealth management and financial crime assessments and accelerate the resolution of issues and complaints.
- Criminal justice: For criminal justice groups using AI, explainability can help optimize processes for risk assessment, and reduce the risk of bias being shown towards certain communities. It can also accelerate resolutions with explainable insights into DNA analysis, and evidence assessment.
- Automotive: In the automotive space, where autonomous vehicles are becoming increasingly common, XAI ensures developers can explain how systems make driving decisions. This makes it easier to guarantee that AI solutions are making choices based on not just traffic conditions, but driver and public safety.
- Military: In the military landscape, AI-based systems need to be explainable to ensure trust between service people, and the equipment they use to make decisions about tasks and actions. Explainable AI helps to ensure professionals can trust and rely on the information AI tools share about specific scenarios.
The Benefits of Explainable Artificial Intelligence
On a broad level, explainable AI is crucial to ensuring companies developing AI systems can understand how they work and fine-tune their performance. It’s also critical to maintaining compliance with evolving governance standards around AI.
As companies and consumers continue to use more advanced models, new governance standards are emerging worldwide. Many focus on ethics, transparency, explainability, and data protection. Developing XAI models ensures leaders can navigate some of the challenges associated with AI compliance. They’ll be able to more easily understand why some models show bias, or discrimination, or surface incorrect information.
Additionally, innovators who prioritize explainability are more likely to earn the trust of their users, boosting adoption rates. Some of the biggest benefits of explainable artificial intelligence include:
Increasing Productivity for AI Teams
Using techniques to enable explainability in artificial intelligence systems can help transform how companies create AI tools. Explainability allows users to reveal errors and areas for improvement quickly and effectively to rapidly optimize AI performance.
For instance, understanding which data a generative AI tool like ChatGPT used to respond to a specific question can help technical teams identify whether the system uses relevant and up-to-date resources. This can help developers determine what kind of data needs to be fed into a model to ensure it delivers more accurate responses.
Increasing Trust and Adoption
Explainable artificial intelligence is also crucial to helping customers, regulators, and other users trust the systems they’re using. People relying on AI for everything from schedule planning to content creation and even medical research need to be confident that these systems are working correctly.
Even the most advanced AI systems won’t earn adoption if intended users can trust them, or don’t understand how they work. This is one of the reasons why leaders in the AI are focusing on making their systems more transparent and understandable.
Unlocking New Insights
Unpacking how a model or system works can deliver incredible insights to developers and users in the AI landscape. In some cases, simply understanding how a machine made a prediction or created a piece of content can deliver more value than the output itself.
For instance, in the customer experience landscape, knowing why a system suggested certain customers were at risk of churn (based on sentiment or other data), can help organizations develop more advanced strategies for retention.
Ensuring AI Delivers Value
As mentioned above, explainable artificial intelligence makes it easier to assess exactly how a model is working, and whether it’s performing according to expectations. When a technical team knows how the system functions, they can confirm more accurately whether it’s driving the right results.
This can help early adopters and innovators in companies that champion the use of AI to explain the return on investment offered by a specific system. It can also help businesses make better decisions about which AI tools to continue using.
Reducing AI Risks
Perhaps most importantly, explainability helps organizations to avoid the common risks associated with AI implementation. AI systems that perform unethically, or put certain data at risk can lead to significant public and regulatory scrutiny.
In some environments, explainability has even become a requirement for all companies using AI. For instance, insurance companies in California using AI systems need to be able to explain how their tools work to manage policies and payouts. New regulations like the EU AI Act, also include specific requirements surrounding AI explainability.
The Challenges of Explainable Artificial Intelligence
Although explainable artificial intelligence is clearly important, it’s challenging to get right. Ensuring AI is explainable means developing a deep, clear, and comprehensive understanding of how an AI system reached a specific decision or made a certain recommendation.
This means developers need to know exactly how the model operates, what data was used to train it, and which challenges the system might need to overcome. That might sound simple enough, but more sophisticated AI solutions, like generative AI systems powered by large language models and machine learning are becoming increasingly complex.
The more sophisticated an AI system becomes, the harder it is to pinpoint exactly why it delivered a specific output. As advanced AI engines interpolate and build on data, the insight trail required for explainable AI becomes harder to track.
Plus, it’s worth noting that different users of AI systems may have different needs when it comes to explainability. For instance, a bank using an AI engine to help make credit distribution decisions needs to be able to explain to consumers how their system arrived at a specific outcome. Loan officers using these systems will need to explain which risk factors influence those decisions, and how they’re weighted by different AI algorithms.
On top of that, XAI systems typically achieve lower performance metrics compared to black box models, because they need to “show their work” for every process. Additionally, training these systems is extremely difficult. Creating a system that can explain its reasoning takes a lot of technical expertise. There’s even a risk that training processes could lead to security issues.
For instance, if an XAI system leverages confidential data, the transparency of an explainable platform could leave that data exposed to the wrong people.
How Organizations Can Implement Explainable AI
The path to truly explainable artificial intelligence will be complex to navigate. Success requires on a collaborative approach to designing transparent systems and development practices, while ensuring the data fed into AI tools stays secure.
Primarily, organizations will need to focus on a few key areas:
AI Governance
A strong AI governance strategy is critical to the development of explainable AI. Leading innovators like IBM and Google even have their own “governance committees”, dedicated to tracking and understanding the performance of various AI models.
For smaller companies investing in AI, building an AI committee may be complex, as it will require a cross-functional set of experienced business leaders, technical experts, and legal professionals to work together. However, this committee will be essential to setting clear standards for AI explainability across the organization, based on the potential risks defined for each application.
With the right AI governance team, companies will be able to ensure that the right facets of their AI systems are explainable for the correct reasons, whether they’re trying to comply with regulatory standards, or simply improve the performance of new models.
Constant Experimentation
Developing explainable AI, and ensuring it performs according to specific standards requires constant fine-tuning and experimentation. Companies will need to ensure human teams are constantly reviewing how AI systems work. This means not only checking outcomes for accuracy, but tracking how each model arrived at those outcomes.
Teams will also need to consider whether to go beyond basic explainability requirements, to dive deeper into how each system works. Sometimes, in-depth insights into the functional processes of a system can increase trust and adoption rates. However, it could also lead to performance issues.
Ultimately, experimenting with different processes and techniques to make AI more explainable will ensure organizations can achieve the best balance between performance and compliance.
Talent Development
Creating explainable artificial intelligence systems that track and share their “thought processes” with users is great. However, the benefits of this process won’t be fully recognized if users can’t understand what they’re actually seeing.
A focus on digital literacy, and helping users to understand exactly what kind of processes different AI technologies use will be crucial to the future of explainable AI. The more people understand the inner workings of machine learning algorithms and other AI systems, the more comfortable they’ll be assessing the accuracy and output of their tools.
As adoption of AI grows, companies investing in the latest explainable solutions will need to invest heavily in training teams on AI systems. Plus, they’ll need to ensure they can help their users understand why explainable AI is so important.
The Future of Explainable Artificial Intelligence
As the world of AI has matured, increasingly complicated, and opaque models have become more common, particularly among developers looking to solve complex problems. Although the performance of these tools can be excellent, when they fail to behave as expected, it can be almost impossible to track the cause of common issues.
Explainable AI will help meet the demands of AI engineering processes by providing clear insights into model accuracy and outcomes. It will lead to the development of more powerful, reliable, and trustworthy models for various future tasks.
Explainable, and transparent AI will also continue to grow more important as regulators and government groups focus on finding ways to improve ethical AI strategies. As new compliance standards emerge to govern how people use AI to make critical decisions that can affect human beings on a critical scale, explainability will be essential.
Going forward, we can expect the focus on explainable AI to increase as demand for accurate, reliable, and ethical systems grows.
