Ever found yourself wondering about the inner operations of artificial intelligence (AI) systems? If yes, then you’re not alone. AI has surely become incredibly smart and widely used in various sectors. However, its complex nature might still leave you, your stakeholders, and your users a bit skeptical at times.
That’s where explainable AI, or XAI for short, comes into play – the key to unraveling the intricacies of your advanced AI model!
Think of explainable AI as turning on the lights in a dark room. It’s all about making AI less of a puzzle by providing clear explanations for its predictions, recommendations, and decisions. This way, you’ll have at hand AI tools that are not only smart but also easy to understand and trustworthy.
Curious to learn more? Explore all the essentials of explainable AI – from its significance, workings, and principles to real-life applications in this article.
What Is Explainable AI?
Explainable AI refers to a set of techniques, principles, and processes created to help AI developers and human users better grasp how AI models work, including the logic behind their algorithms and the results they produce. In simpler terms, it demystifies the internal decision-making of an AI model.
So, what makes explainable AI different from regular AI?
While both are part of the same technology, the key difference lies in their transparency level. Traditional AI, often known as “black box” AI, uses complex machine learning algorithms to make decisions without explaining clearly their reasoning. This lack of transparency has sparked concerns about the fairness and safety of AI, especially in healthcare, law, and finance fields, where AI decisions might have serious real-world influences.
Explainable AI takes a different approach, focusing on transparency and clarity. It’s built to give clear and straightforward explanations of how its decisions are made. This doesn’t mean XAI is less sophisticated or capable than traditional AI. In fact, XAI models can be just as advanced or even smarter. The key distinction is that explainable AI strives to make the inner workings of these sophisticated models accessible and understandable to humans. This will build trust and allow users to review and refine AI’s decisions.
Why Does Explainable AI Matter?
While explainable AI is not new, it has recently gained great interest. Below are the three main justifications for the growing significance of XAI.
- Ethical Considerations
With artificial intelligence becoming a part of our daily lives, ethical considerations are more crucial than ever. Oftentimes, AI makes decisions that could directly affect people’s lives. For instance, it gives healthcare advice to financial loan approvals. Thus, there emerges one big ethical question: Aren’t we supposed to understand and explain these decisions, especially when they carry such enormous implications?
Consider a scenario where AI software denies a loan application, and of course, the applicant deserves to know why. XAI steps in to ensure decisions are fair and justified.
- Regulatory Requirements
The growing use of artificial intelligence comes with increased scrutiny from regulators. In many jurisdictions, there are already numerous regulations in play that demand organizations to clarify how AI arrived at a particular conclusion.
For instance, the European Union’s General Data Protection Regulation (GDPR) gives individuals the “right to explanation”. This means people have the right to know how decisions affecting them are being reached, including those made by AI. Hence, companies using AI in these regions need to make sure AI systems can provide clear explanations for their decisions.
- Trust and Adoption
Many are wary of AI because of its rather mysterious decision-making processes. If AI stays a black box, building trust with users and stakeholders will remain a big challenge.
This is where XAI comes in handy, providing transparent reasoning behind AI decisions, fostering trust, and encouraging the adoption of AI-driven solutions.
How Explainable AI Works
Explainable AI functions based on a foundation of interpretability and transparency. The former means an AI system can present its decisions in a way humans can understand. The latter, meanwhile, involves giving users insights into how the system makes certain decisions.
There are two main approaches to XAI. The first is leveraging decision trees or rules, also known as interpretable models. These models establish the relationship between inputs (data) and outputs (decisions), enabling us to follow the logical flow of AI-powered decision-making.
The other approach is using post-hoc explanations, in which the AI-based system clarifies its decisions after making them. Local Interpretable Model-Agnostic Explanations (LIME) is a common post-hoc technique for explaining the predictions of any machine learning classifier. It feeds the black-box model with small variations of the original data sample and investigates how the model’s predictions shift. From these variations, it trains an interpretable model that approximates the black-box classifier in close proximity to the original data sample. Locally, the interpretable model gives a precise approximation of the black-box model, although it’s not always a globally reliable approximator.
In addition, explainable AI employs other techniques to enhance the transparency and understandability of AI models’ decision-making process. For example, Feature Importance, Partial Dependence Plots, Counterfactual Explanations, and Shapley Value.
4 Key XAI Principles
Data scientists at the National Institute of Standards and Technology (NIST) have outlined four key explainable AI principles at length. Below is just a summary of these concepts:
- Explanation
The foremost principle – Explanation – indicates an AI-based system needs to provide evidence, support, or reasoning about an outcome or process.
Note that the quality of the explanation, whether it’s correct, informative, or easy to understand, is not explicitly measured by this principle. These aspects are elements of the meaningful and explanation accuracy principles, which we’ll explore in more detail below.
- Meaningful
The Meaningful principle is about ensuring that recipients can understand the provided explanations. To enhance meaningfulness, explanations should commonly focus on why the AI-based system behaved in a certain way, as this tends to be more easily understood.
You also need to consider your audience, keeping in mind that factors like prior knowledge shape what is perceived as a “good” explanation. Moreover, what is meaningful depends on the explanation’s purpose and context in a given scenario.
In short, achieving the Meaningful principle requires understanding the audience’s needs and expertise, as well as contextual awareness.
- Explanation Accuracy
The Explanation and Meaningful principles emphasize the need to provide clear explanations to the intended audience. However, they don’t demand that the explanation accurately reflects how a system generates its output.
The Explanation Accuracy principle seeks to ensure the truthfulness of an AI system’s explanations. Yet, researchers are still struggling to establish performance metrics specifically for explanation accuracy.
- Knowledge Limits
The Knowledge Limits principle highlights the importance of AI systems recognizing situations where they weren’t designed or authorized to operate or where their answer may be unreliable. This way, they can avoid giving misleading, dangerous, or unjust outputs.
Take an AI system predicting stock market trends as an example. It’s crucial to express the uncertainty or confidence level in the model predictions. You could do this by presenting error estimates or confidence intervals, offering a comprehensive picture that allows for more well-informed AI-driven decisions.
9 Major Explainable AI Use Cases In Real-Life
Now, one big question “Which case would benefit from explainable artificial intelligence AI principles?”.
Discover nine noticeable XAI use cases in healthcare, finance, and judicial systems, along with interesting examples that you can try experimenting with your own business.
XAI in Healthcare
AI in healthcare is an excellent place to start. As medical operations get more and more sophisticated, XAI plays an important role in guaranteeing the reliability of even the tiniest details provided by AI models.
1. Disease Diagnosis and Risk Assessment
Explainable AI is a game-changer when it comes to medical diagnosis. Google’s DeepMind is a case in point. It has crafted an AI system capable of spotting eye conditions like diabetic retinopathy from medical scans. Similar AI models also step into the spotlight, offering lucid explanations for cancer diagnoses and enabling doctors to make well-informed treatment decisions.
2. Drug Discovery Acceleration
Pharmaceutical companies are increasingly embracing XAI to save medical professionals an enormous amount of time, especially by expediting the process of medicine discovery. Explainable AI-powered machines examine large amounts of data and then pinpoint potential drug candidates faster than conventional methods, with transparent AI systems’ reasoning at hand.
Indeed, amid the COVID-19 pandemic, Pfizer successfully employed AI to explore plausible treatments swiftly, showcasing the technology’s pivotal role in public health.
3. Patient Monitoring and Early Warnings
Explainable AI can also take on the vigilant guardian role, continuously monitoring patient data and detecting early signs of deterioration.
What AI tools do here is analyze important signs and patient records to alert medical staff to any alarming changes promptly.
This proactive approach enhances patient care by facilitating timely interventions and minimizing medical errors. It also lets doctors concentrate on the interpretative tasks of medicine rather than a repetitive duty.
XAI in Finance
A key driver propelling the expansion of the XAI market is the increasing integration of AI models in the finance sector. Whether it’s banking or insurance, the incredible applications of XAI are reshaping the industry’s operations that inherently uphold transparency and clarity more than anything else.
4. Credit Scoring and Risk Assessment
Explainable AI-powered techniques can be of great use in credit risk assessment since they give applicants explicit reasons for each decision. To do that, they have to take into account many different factors to evaluate an applicant’s creditworthiness.
In reality, banks and lending institutions have widely leveraged FICO’s explainable AI models to make lending decisions more transparent and fairer for their customers.
5. Fraud Detection and Prevention
At the forefront of explainable AI applications in finance is detecting fraudulent activities. By analyzing real-time transaction data, financial institutions can identify irregular patterns that might signal fraud.
As an illustration, Mastercard employs AI-powered cybersecurity solutions to safeguard its cardholders. They spotted and blocked suspicious transactions, stopping over $35 billion in fraud losses over the last three years.
6. Portfolio Management
What’s more, investment firms can harness explainable AI to fine-tune portfolio management. AI algorithms excel at delving into vast market data and investor preferences, showing insightful suggestions for investment strategies.
Wealthfront stands out as an exemplary case, providing clients with AI-driven investment plans to help them reach logical decisions and boost returns.
XAI in Judiciary or Legal Services
Traditional AI models often operate like mysterious black boxes, posing great challenges for legal professionals to understand the rationale behind AI-generated decisions fully. However, several use cases of explainable AI are transforming this narrative.
7. Legal Research and Case Law Analysis
Explainable AI lends a hand to legal practitioners by looking into vast legal documents to uncover relevant case law and precedents, with clear reasoning presented. Such tools both save law firms loads of time and boost their legal research’s overall quality.
8. Predictive Policing for Crime Reduction
Law enforcement agencies take great advantage of explainable AI applications, such as predictive policing, to identify potential crime hotspots and allocate resources strategically in a trustworthy manner. What AI focuses on is analyzing massive historical crime data, allowing for the effective deployment of officers, which ultimately reduces crime rates in certain areas.
9. Sentencing Recommendations
Within the judiciary, XAI contributes to fairer decision-making by giving data-driven sentencing recommendations to judges. These recommendations, drawing from precedents, circumstances, and other relevant factors, foster transparency and consistency in the criminal justice system while mitigating potential biases in sentencing.
Leverage XAI with Neurond AI
Looking ahead, explainable artificial intelligence is set to experience significant growth and advancement. The demand for transparency in AI decision-making processes is expected to rise as industries increasingly recognize the importance of understanding, verifying, and validating AI outputs.
Having said that, the development of explainable AI comes with multiple challenges. E.g., the sheer complexity of AI itself, the costly trade-off with performance, data privacy concerns, and the risk of competitors copying machine learning models’ inner workings.
Tackling these obstacles will demand extensive and ongoing collaboration among various stakeholder organizations. Academic and research organizations need to make new advancements, commercial entities must provide and utilize the technology for profit, and governmental bodies must take care of regulating and overseeing its integration into society.
As AI gets more and more interwoven with our lives, there’s one thing for sure – developers of AI tools and applications will be compelled to adopt responsible and ethical principles to build trust and transparency. This shift, in turn, promises to steer us toward a future where AI power is applied equitably and to the benefit of all.
Neurond AI commits to providing you with the best AI solutions, guided by the core principle of responsible AI. We strive to deliver the most transparent, trustworthy, accountable, and ethical AI systems so that together, we can pave the way for smarter, more reliable decisions.
Feel free to contact us now!