Forgery of educational certificates is a serious issue that can have far-reaching consequences. To detect and prevent forgery, educational institutes issuing academic certificates or transcripts must meet certain requirements to ensure... more
Forgery of educational certificates is a serious issue that can have far-reaching consequences. To detect and prevent forgery, educational institutes issuing academic certificates or transcripts must meet certain requirements to ensure the authenticity of the certificates. One method of detecting forgery and fabrication in educational certificates and academic transcripts is by using cryptography and QR codes. This involves converting certificate details into a ciphertext, encoding it in a QR code, and printing the same on the certificate or transcript to be issued. Certificate owners, other institutes, employers, and third-party verifiers can verify certificates for forgery and fabrication using the scanning app of the issuing institute. Another method to prevent certificate fraud is by issuing digital certificates with blockchain technology. This ensures that certificates cannot be forged, and verifiers can instantly tell if they are fake or not. Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. It helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision making. Explainable AI is crucial for an organization to build trust and confidence when putting AI models into production. Bias, often based on race, gender, age, or location, has been a long-standing risk in training AI models. Further, AI model performance can drift or degrade because
production data differs from training data. This makes it crucial for a business to continuously monitor and manage models to promote AI explainability while measuring the business impact of using such algorithms. Explainable AI can help humans understand and explain machine learning (ML) algorithms, deep learning, and neural networks. It also promotes end user trust, model auditability, and productive use of AI. It mitigates compliance, legal, security, and reputational risks in production AI.
Key Words: Explainable AI, Educational Certificate, machine learning algorithms, deep learning, neural networks, Forgery Detection, Cryptography, and QR codes.
production data differs from training data. This makes it crucial for a business to continuously monitor and manage models to promote AI explainability while measuring the business impact of using such algorithms. Explainable AI can help humans understand and explain machine learning (ML) algorithms, deep learning, and neural networks. It also promotes end user trust, model auditability, and productive use of AI. It mitigates compliance, legal, security, and reputational risks in production AI.
Key Words: Explainable AI, Educational Certificate, machine learning algorithms, deep learning, neural networks, Forgery Detection, Cryptography, and QR codes.
