Artificial Intelligence has taken over our lives silently but also in a remarkable fashion. A number of activities that we perform in our daily lives are now powered by AI. The best example can be seen on our smartphones and the way they surface the right apps based on time.
This process is equivalent to AI learning our habit over time and then offering inference in the best possible way. As AI expands beyond smartphones and becomes part of our social fabric, one of the questions to be asked is whether we can trust the results. This process is also defined as explainable artificial intelligence (XAI).
What is Explainable AI?
Explainable artificial intelligence (XAI) is defined as a set of processes and methods that allow human users to comprehend and trust the results or output generated by machine learning algorithms. With explainable AI, it is also possible to describe an AI model, understand its impact, and study the potential biases.
With advancement of AI and ML models, it is becoming difficult for humans to comprehend how the algorithm delivers a result. A number of computer scientists have now come to label this process as a “black box.” Even engineers and data scientists cannot understand or explain how the algorithm is reaching its conclusion.
With explainability, it is possible to build a system that meets regulatory standards. Explainable AI is our path to stay on top of AI algorithms taking over most of our work and challenge the decision made by them when the outcome is not the right one.
Does explainable AI matter?
The current state of AI shows that explainable AI matters more than anything else. With ethical AI researchers departing search giant Google suddenly, there is a need for accountability of AI more than ever. With explainable AI, it also becomes important for organisations to have full understanding of their AI’s decision making process.
Explainable AI matters because it forces organisations to think about model monitoring and accounting of AI. It also makes them not trust their AI models blindly. Lastly, it gives common people a chance to question the AI models and ensure they don’t remain black boxes that are impossible to interpret.
The primary reason that explainable AI matters is because it is one of the key requirements for implementing responsible AI. It is our gateway to ensuring AI models make decisions with fairness, accountability, and explainability. In simple terms, explainable AI makes it possible for AI to be trained with ethical guidelines, trust, and transparency.
What are explainable AI principles?
The US National Institute of Standards and Technology (NIST) has developed four principles for explainable AI. These principles define what explainable AI should try to explain and accomplish.
- Explanation: The first principle is that an AI system should be able to explain its output and provide evidence or reasons for all of its outputs. This is meant to explain the end-user benefit, gain trust in the system, meet regulatory standards, and even explain the benefit for the model’s owner.
- Meaningful: The explanation offered should be meaningful so that normal users are able to understand the outcome. The system should also provide multiple explanations if there is a range of users with different skill sets.
- Accuracy: The explanation offered by an AI model should be clear and accurate. By accuracy, the NIST is talking about accuracy of the explanation and not the output of the model.
- Limits: Lastly, the NIST calls for the model to operate within its designed knowledge limits and ensure that it delivers reasonable outcomes.
What are the benefits of explainable AI?
As mentioned earlier, an explainable AI comes with a number of benefits but the most important being the opportunity to build trust. IBM estimates that explainable AI leads to between three and eight times increased models in production and up to 30 percent increase in accuracy of models. Here is a look at three key benefits of explainable AI:
- Building trust in AI: With explainable AI, the organisations building AI models can bring trust and confidence. They can also effectively operationalise AI with interpretability and explainability of models. This process also simplifies model evaluation and increases transparency and traceability.
- Faster AI results: Explainable AI allows organisations to systematically monitor and manage models to optimise business outcomes. It also makes it easier for companies to continuously evaluate and improve model performance. The data scientists can also fine-tune their model development efforts based on this evaluation.
- Regulatory compliance: With an AI model that is explainable and transparent, organisations can manage regulatory compliance. They can also mitigate risk and minimise manual inspection and other errors. This benefit also extends to mitigating risk of unintended bias.
What are the important considerations for explainable AI?
The premise of explainable AI is to build AI in such a way that we get desirable outcomes. In order to ensure that desirable outcomes are achieved, IBM and other organisations note that there is a need to make five considerations for explainable AI.
- Fairness and debiasing: The first and foremost need is to manage and monitor fairness. This can be done by scanning your system, AI design and deployment for potential biases.
- Mitigate model deviation: Organisations need to ensure that their models are analysed and recommendations are made based on the most logical outcome. If a model deviates from the intended outcomes, organisations alert so that appropriate mitigation can be done.
- Risk management: Another need is to quantify and mitigate model risk. Whenever a model performs inadequately, there should be a mechanism to be alerted and understand what happens when deviations persist.
- Lifecycle automation: Another consideration to make is build, run, and manage models as part of integrated data and AI services. Organisations should consider unifying the tools and processes on a platform to monitor models and share outcomes.
- Ready for multi-cloud: Explainable AI should not only be considered as a cloud-first approach but also as a solution that can be deployed across hybrid clouds. Whether it is public cloud, private cloud, or on premise, the AI models should be deployable and able to promote trust and transparency.
How does explainable AI work and what are the techniques?
Explainable AI works by organisations gaining access to the underlying decision-making technology of artificial intelligence or machine learning model. The organisations are also empowered to make adjustments and explainable AI can even improve the user experience of a product or service.
With AI becoming advanced, there is a need for ML processes to be understood and controlled to ensure AI model results are accurate. There are three main methods and techniques used to turn AI into Explainable AI.
- Prediction accuracy: Accuracy acts as one of the key parameters to understand successful use of AI in everyday operations. The prediction accuracy can be determined by running simulations and comparing explainable AI output to the results in the training dataset.
- Traceability: Another key technique to accomplish explainable AI is traceability. Traceability is achieved by limiting the way decisions can be made and setting up a narrower scope for ML rules and features.
- Decision understanding: This is both crucial and critical aspect of building explainable AI. With people distrusting AI and having to work with it efficiently, there is a need for them to learn to trust it. This can be achieved by educating the team working with the AI to understand how and why an AI makes its decisions.