Artificial Intelligence (AI) has transformed the world of technology, enabling systems to learn, adapt, and make decisions without explicit programming. From autonomous vehicles to medical diagnostics and flight control systems, AI promises unprecedented efficiency and capability. However, when it comes to safety-critical systems—where failure could result in injury, loss of life, or significant damage—the use of AI introduces profound challenges that go far beyond traditional software engineering. Unlike conventional software, which behaves predictably according to its programmed logic, AI is built on learning and training. Its decisions and outputs depend heavily on the data it has been trained on and the patterns it recognizes during runtime. This adaptive, data-driven behavior means that an AI system’s responses may vary with changing inputs or environments, often in ways that are not explicitly defined or foreseen by developers. While this flexibility is a strength in many applica...
The success or failure of a software application may be defined by it's User Interface (UI) design. A mobile application with a non-intuitive UI design may be abandoned or uninstalled by the user after first impression resulting in loss of business to the company. In the domain of safety-critical applications such as healthcare or transportation, poorly incorporated UI design may result in erroneous selection of input by the user, which may have catastrophic effects. In general, poor UI design can lead to frustration, confusion, and inefficiency for users. "People ignore design that ignores people.” (Frank Chimero) Scientists and researchers have studied and defined the principles and practices to improve the usability of interactive applications. Big tech companies have translated those general set of principles / heuristics to domain specific UI design guidelines. The main objective of UI design is to create a positive first impression and attracting or retaining customers...