Artificial Intelligence (AI) is everywhere. Recent successes in machine learning (ML) and deep learning (DL) have led to a new wave of applications offering extensive benefits to diverse fields.
However, most of the outputs generated by ML and DL models are produced by data-driven algorithms or black boxes that are impossible to interpret. They produce results without revealing any information about their internal workings, and they cannot explain their autonomous decisions and actions to human users. Consequently, all AI models must be continuously monitored and managed to explain their use and the outputs of algorithms.
For some applications of AI, explanations might not be necessary. Nevertheless, explanations are necessary for users to comprehend, have confidence in, and effectively manage these new, artificially intelligent partners in many crucial applications in defense, healthcare, finance, and law. The purpose of an explainable AI (XAI) is to make the AI behavior more intelligible to humans by providing explanations.
The goal of explainability is to ensure that a given model makes sense to a human observer using language that is meaningful to users. Explainability is viewed as being logical and providing a transparent outcome; that is, it is readily understandable. An explainable model should also be presented to the user with visual or textual artifacts to aid transparency.
Benefits of XAI
- Reduce the influence of model bias: Systems can reduce unintended consequences and bias by explaining decision-making criteria and monitoring the model.
- Increase user trust and accelerate adoption: Users will adopt AI more quickly as they understand why and how models make decisions.
- Risk management and compliance: Reduce the possibility of unintended consequences while adhering to privacy, industry standards, and potential regulations.
- Obtain actionable insights: For more impactful insights, XAI encourages humans to understand how and why an algorithm determines its output.
Why does XAI matter?
There are numerous reasons for certain AI deployments to be explainable. Many researchers recommend XAI for the following reasons:
To progress from proof of concept to full implementation, you must be confident that your system meets certain intended requirements and does not exhibit undesirable behaviors. If the system makes a mistake, organizations must identify what is wrong. To correct the situation or even shut down the Al system. By monitoring performance, flagging errors, and providing a mechanism to turn the system off, XAI can assist your organization in maintaining control over Al.
Regarding data privacy, XAI can help ensure that only permitted data is used for agreed-upon purposes and that data can be deleted if necessary. In instances of a black box system, developers frequently try to solve problems by ‘throwing data’ at Al. Having visibility into the data and features that Al models use to generate output can help ensure that issues are understood, and a level of control is maintained. Interpretable Al systems can illuminate any adverse training drift in systems that learn through customer interactions.
Building trust in artificial intelligence requires demonstrating to many stakeholders that the algorithms are making the right decisions for the right reasons. Up to a point, explainable algorithms can provide this. Nonetheless, the context problem persists, even with cutting-edge machine learning evaluation methods and highly interpretable model architectures: Al is trained on historical datasets with implicit assumptions about how the world works. Events such as an earthquake, a new central bank policy, or a new technology can occur and render historical training data invalid.
Individuals responsible for the model can detect when it is likely to fail and take appropriate action by intuitively understanding its behavior. XAI also contributes to trust by improving interpretable models’ stability, predictability, and repeatability. When stakeholders see consistent results, their confidence grows over time. Once that trust is established, end users will find it easier to trust other applications they haven’t seen before. This is especially important in the development of Al because models are likely to be used in situations where their use may change the environment, potentially invalidating future predictions.
Risk and vulnerability assessment
Understanding how a system works can help you assess risk. This is especially important when a system is deployed in a new environment where the user cannot know how effective it is. Explainability can also help developers understand how a system might be vulnerable to adversarial attacks, in which actors attempting to disrupt a system identify a small number of carefully chosen data points to alter to cause the system to produce incorrect results. This is particularly important in safety-critical tasks.
Understanding who is responsible for an AI system’s decisions is critical. This, in turn, necessitates a thorough XAI-enabled understanding of how the system works, how it makes decisions or recommendations, how it learns and evolves, and how to ensure it works as intended. To assign responsibility for an adverse event caused by Al, a chain of causality from the Al agent back to the person or organization that can be held reasonably responsible for its actions must be established. Depending on the nature of the adverse event, different actors within the causal chain that leads to the problem will bear responsibility. It could be the person who decided to use the Al for an inappropriate task or the original software developers who failed to include adequate safety controls.
Several concerns have been raised about the safety and security of AI systems, particularly as they become more powerful and widespread. This can be attributed to various factors, including intentionally unethical design, engineering oversights, hacking, and the effect of the environment in which Al operates. XAI can assist in identifying these types of flaws. To protect against hacking and deliberate manipulation of learning and reward systems, it is also critical to collaborate closely with cyber detection and protection teams.
Transparency or explainability can be useful in enforcing legal rights surrounding a system, demonstrating that a product or service meets regulatory standards, and navigating liability questions. Many policy instruments already exist to promote or impose some level of explainability in the use of data and AI.