What if you couldn’t get into your dream college simply because they used a specific algorithm to select candidates? What if an AI program made a judgement about your body that you do not agree with? One can only imagine the scale of disappointment in these situations.
Machine learning algorithms have mastered the art of finding patterns in vast swathes of data. To achieve better results, AI is used in collaboration with human decision-making, partly by letting people choose how it functions and teaching them to regard it as a trustworthy partner. No wonder that businesses and governments have been quietly adopting AI technology.
So far, so good. Yet there can be one important tradeoff – AI tools’ decisions may not always be transparent. As we increasingly rely on artificial intelligence (AI) to make critical decisions that affect our lives, such as in healthcare, finance, and criminal justice, it is essential that we understand how these decisions are made.
However, many AI systems are like black boxes, leaving us in the dark about how they arrived at their conclusions. This lack of transparency can lead to distrust in AI systems, and even ethical and legal issues. Therefore, the problem is the need for explainable AI in critical decision-making processes. Explainable AI can help increase transparency, accountability, and trust in AI systems, allowing us to make informed decisions that are fair and just.
According to a 2022 report by McKinsey & Company, “Explainability is the capacity to express why an AI system reached a particular decision, recommendation, or prediction.” Explainable AI, also known as explainable artificial intelligence (XAI), fundamentally means to shed light on what’s happening inside the “black box” that frequently envelops AI’s inner workings. Machine learning that can be explained is responsible and able to “display its work.”
The future…
Customers would want to be associated with companies that are transparent and provide solutions. Explainable AI is a need for the future. One reason being model drift. As more and more data is fed into a particular model over time, this new data may have an unintended impact on the model. Understanding an AI’s decision-making processes will allow us to influence it more effectively throughout its career, ensuring that its outputs are reliable and consistent.
Practically speaking, we can employ explainable AI to improve the initial accuracy and refinement of models. We will require both technical solutions and governance and regulatory measures to safeguard consumers from unfavourable outcomes as AI grows more pervasive in our lives.
An area where explainable AI can benefit everyone is in the hiring process. Hiring managers typically receive more applications than they can read in full because of the variety of employment needs and talent shortages they deal with. This indicates that there is a high demand for algorithms that can evaluate and screen applications. Nonetheless, there is bias in the hiring process, and many qualified candidates with diverse backgrounds are often overlooked. The ultimate solution to these issues would be explainable AI, which would make it clear why a model selected one candidate but rejected another. It aids in improving your model.
Ways to improve explainable AI
Model Interpretability Techniques: These techniques are used to make black-box models more transparent by providing visualisations, explanations, or feature importance scores. These techniques enable humans to understand how AI models are making predictions, leading to better decision-making and increased trust in AI systems.
Explainable Neural Networks: These are a type of neural network designed to provide interpretable outputs. They use techniques such as attention mechanisms, sparse activations, and hierarchical structures to produce human-readable explanations for their predictions.
Counterfactual Explanations: Counterfactual explanations provide insight into an AI’s decision-making process by presenting alternative scenarios that could have resulted from different inputs. By illustrating these different outcomes, these explanations can help humans understand the model’s decision-making process and identify areas for improvement.
Causal Inference: Causal inference is a statistical technique that enables AI systems to identify cause-and-effect relationships between variables. This approach can help AI systems provide more accurate and understandable explanations for their predictions.
Human-in-the-Loop: Human-in-the-Loop (HITL) approaches involve incorporating human feedback into an AI system’s decision-making process. By integrating human feedback, AI systems can learn from their mistakes, become more transparent, and provide better explanations for their decisions.
Hybrid Approaches: Hybrid approaches involve combining multiple XAI techniques to create more powerful and accurate models. For example, combining model interpretability techniques with counterfactual explanations can provide a more comprehensive understanding of the model’s decision-making process.
Overall, these new technologies and approaches are expected to improve XAI by making AI more transparent, interpretable, and understandable to humans.
Explainable AI (XAI) has advanced data-driven decision-making by eliminating the “black box” idea and making the AI process more transparent, verifiable, and trustworthy. Transparency is important because AI models are complex and may not be immediately understandable to users. XAI can provide a thorough and easy-to-understand explanation of the decision-making process.
Responsibility is another critical factor. Decision-makers must take responsibility for every choice they make, and XAI’s ability to explain the reasoning behind each proposal decreases the likelihood of inquiries into the decision.
Confidence is essential for an AI model that is being used. Future decisions taken by an organization can be impacted by the reliability of the AI model. XAI explains the accepted AI model in a straightforward and transparent manner, giving decision-makers more confidence to accept and implement the suggested actions.
The current state of Explainable AI (XAI) in critical decision-making processes is still evolving. While there has been significant progress in the development of XAI techniques, there is still much work to be done to ensure that AI systems are transparent, interpretable, and auditable in critical decision-making processes.
One of the challenges in implementing XAI in critical decision-making processes is the trade-off between accuracy and interpretability. Many complex AI models are difficult to interpret, but they can be very accurate. However, in critical decision-making processes, interpretability is often more important than accuracy because humans need to understand how decisions were made and be able to explain those decisions to others. Another challenge is the lack of standards and regulations around XAI in critical decision-making processes. Without clear guidelines, it can be difficult to ensure that AI systems are transparent, interpretable, and auditable.
While the current state of XAI in critical decision-making processes is still evolving, significant advancements have been made in recent years. With the development of new XAI techniques, the establishment of standards and regulations, and the growing awareness of the importance of XAI, we can expect to see further progress in the field. Ultimately, XAI has the potential to transform the way we use and interact with AI systems, enabling us to fully harness the potential of AI while ensuring that these systems are transparent, interpretable, and auditable.
This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.
