In sectors dealing with sensitive information such as finance, healthcare, law, and autonomous driving, Explainable AI (XAI) is already playing a significant role. XAI aims to make the decision-making process of AI systems clear and understandable. Therefore, it is an essential element in sectors with strict regulations to enhance trust in and adoption of AI technologies. XAI opens up the black box of AI systems, providing stakeholders the ability to understand, verify, and challenge the decisions made by these systems.
In the Financial Sector
In finance, AI is used for stock market analysis, credit rating, and real estate price prediction. XAI provides reliability to investors and analysts by clearly explaining the data and patterns on which these predictive models base their decisions. For example, research is underway using AI for investment portfolio optimization and credit risk assessment. Let’s look at some specific real-world applications of Explainable AI (XAI) in finance:
1. PayPal
PayPal uses machine learning models to detect fraudulent transactions. Their system analyzes millions of transactions in real-time to identify suspicious activities. With the application of XAI, PayPal better understands why the model classified a particular transaction as fraudulent and can more easily review or modify these decisions if necessary. The advanced machine learning models are crucial in real-time monitoring of transactions for detecting financial fraud.
2. ZestFinance
ZestFinance uses XAI to assess the credit risk of loan applicants. Their Zest Automated Machine Learning (ZAML) platform enables lending institutions to understand and explain the model’s decisions while assessing the riskiness of loan applicants. This allows lenders to make more accurate and fair loan decisions, even for applicants with low credit scores.
3. BlackRock
One of the world’s largest asset management companies, BlackRock, utilizes AI to develop investment strategies. Their AI platform analyzes vast data to identify investment opportunities, and XAI enables them to transparently explain these decisions to investment managers and clients.
In the Healthcare Sector
In healthcare, XAI helps medical professionals trust and understand the decision-making process of AI systems. This is particularly important for AI systems used in diagnosis, prognosis, and treatment planning. For example, research is underway on XAI to assist in analyzing patient data for diagnosis or suggesting personalized treatment plans. These systems provide tailored treatments and enhance the transparency of decision-making processes. Let’s examine specific real-world applications of Explainable AI (XAI) in healthcare:
1. IBM Watson
IBM’s Watson Health analyzes medical data to provide diagnostic support, treatment planning, and personalized patient management. Watson analyzes vast amounts of medical literature and patient data to recommend optimal treatment options for specific diseases and explains the basis of these recommendations to medical professionals.
2. Google DeepMind
Google’s DeepMind has developed an AI model used in ophthalmology to diagnose retinal diseases. The model analyzes retinal scans to detect the presence of disease and provides the rationale for its diagnoses. This approach assists ophthalmologists in explaining diagnostic results more clearly to patients.
3. PathAI
PathAI has developed an AI-based system to support the diagnosis of diseases like cancer in pathology. The system analyzes slide images of tissue samples to detect the presence of cancer cells, enhancing diagnostic accuracy. PathAI provides doctors with the information needed to make more accurate diagnoses.
In the Legal Sector
In the legal field, AI is utilized for case analysis, legal advice, and judgment prediction. XAI makes it possible for legal professionals to understand and trust the recommendations made by these systems by clearly explaining the basis of their decisions. For instance, systems are being developed to provide useful information to legal professionals through automatic classification of legal documents or case analysis.
1. SpeedLegal
This company provides contract analysis and management tools using AI technology. SpeedLegal leverages the latest developments in machine learning and AI to take contract automation to new levels, offering comprehensive capabilities for extracting and analyzing various types of contracts, including Non-Disclosure Agreements (NDAs).
2. Goldman Sachs
Goldman Sachs estimates that about 44% of legal work can be automated by AI. AI is used to simplify and accelerate the document review process.
In the Autonomous Driving Sector
In the field of autonomous driving, the application of Explainable AI (XAI) mainly focuses on understanding the decision-making process and the basis for decisions made by autonomous vehicles. In autonomous driving, XAI is particularly important because the decisions made by vehicles are highly complex and sometimes directly linked to life and death. XAI contributes to increasing the reliability and safety of these systems by making the system’s decisions understandable and explainable. Many companies are known to be moving toward applying XAI, but specific XAI application cases mentioned by actual companies were not found.
The insights into Explainable AI across sectors like finance, healthcare, and autonomous driving underscore its vital role in today’s technology landscape. By demystifying AI’s decision-making, XAI is crucial in fostering trust and ethical practices.
As we continue to unravel the potential of AI, the importance of transparency and accountability becomes more pronounced. Your perspectives on this evolving field are invaluable. We encourage you to share your thoughts and join us in further discussions about the future of AI and XAI.
