Generative AI
The role of explainable AI in bridging the gap between AI and humans
To maximise the benefits of AI and minimise risks, users who are affected by its use cases must understand how the technology makes decisions and offers solutions. Companies with at least 20% of EBIT from AI use cases have AI explainability in their best practices. Those that leverage AI transparency also see more than 10% of annual revenue growth.
However, modelling techniques such as deep learning and neural networks can be complex for everyone to understand. For this reason, AI algorithms and machine learning engines mostly remain opaque. Explainable AI (XAI) discloses the following information to show how an AI program works –
- Strengths and weaknesses.
- Errors the program is prone to.
- How to correct these errors.
- Criteria a program uses to arrive at a decision.
- How the program chooses an alternative from available options.
This article explains the principles of explainable AI, how it works, and its use cases and benefits for an organisation.
Principles of explainable AI
These foundational guidelines ensure the transparency and trustworthiness of an XAI system and are crucial for technologies humans can trust and depend on.
Transparent decision-making process
If necessary, the AI system’s decision-making and operations should be accessible for examination. This principle underscores the creation of AI systems whose actions humans can easily understand and trace without the knowledge of advanced data science.
Interpretability for humans
It refers to the extent to which a human can understand and interpret the cause of a decision. This helps validate the AI model’s decisions against human logic and ethical concerns.
Ethical practices
This ensures that the AI system makes decisions without bias against individuals and groups. It promotes equality, justice, and fairness by making the AI model available for audit whenever necessary.
Reliability and safety from adverse outcomes
The AI model must work reliably and safely under all circumstances. A provision to do regular testing and validation ensures that the users are safe from any harmful and adverse outcomes.
Accountability
The AI system decisions should have a clear line of responsibility where its developers and operators can be held accountable. This encourages the developers to create a careful design, deployment, and monitoring process.
Privacy of sensitive information
Privacy principles focus on safeguarding personal and sensitive user information. This principle ensures that the AI model respects user content against the legal standards applicable to data protection in your region.
How does explainable AI work?
The XAI methodologies ensure greater transparency and understanding for humans and include the following categories –
Intrinsic explainability
This comprises AI models that are intrinsically explainable due to a structure that makes decision-making transparent and understandable. For example, generalised additive models (GAMs), decision trees, and linear regression.
Post-hoc explainability
This technique applies explainability after you train complex AI models such as ensemble methods and deep neural networks. You can achieve this through feature importance, surrogate models, decision rules, and model visualisation.
Explainable AI use cases
Due to the need for transparency and accountability, explainable AI is gaining popularity. Some of the use cases are –
Healthcare
Provide explanations of the AI diagnostic model about the patient condition and treatment outcomes. This helps gain the trust of doctors and the patient by providing the rationale behind a prediction of a disease.
Finance
Explainable AI justifies its decisions in credit scoring, risk management, and fraud detection. Thus, the customer knows why the bank approved or rejected a loan. This transparency helps the financial institution comply with regulations and builds customer trust.
Autonomous vehicles
Manufacturers can enhance the safety and reliability of autonomous vehicles by knowing the explanations for an AI decision, such as those for braking, accelerating, or swerving. This also provides crucial data to investigate accidents.
How does explainable AI benefit organisations?
Explainable AI creates an environment in which technologists, legal and risk professionals, and business professionals can get the most value. Its benefits are –
Higher productivity
Explainable AI reveals errors and areas of improvement faster, thus making it easier for machine learning to monitor and do course corrections. For example, technical teams can understand if an AI prediction is one-off or reusable in the future.
Build trust and adoption
Explainability builds trust with customers, internal teams, and regulatory bodies. For example, a sales team would feel more confident if they knew the reason behind the recommendation of an AI model during a negotiation.
Unpack value-generating interventions
By knowing the reason behind AI recommendations and decisions, businesses can uncover interventions. For example, AI prediction of customers leaving a shopping cart is helpful, but if you know the reason, you can take action to correct the course.
Mitigate regulatory risks
AI systems that function as a black box can run into public, media, and regulatory investigations. In such situations, legal teams can use the explainability to confirm if the system is compliant with the policies and regulations.
How can Infosys BPM supports human AI interaction?
Infosys Topaz is an AI-first set of services, solutions, and platforms that rely on generative AI with explainability. It helps organisations create value and lead the Gen-AI evolution.
Read more about explainable generative AI systems at Infosys BPM.
