Testing AI-Powered Applications: Challenges and Strategies

Authors: Santosh Kumar Jawalkar

DOI: https://doi.org/10.5281/zenodo.14945213

Short DOI: https://doi.org/g86n74

Country: USA

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Abstract: Background/Problem Statement -Ensuring the reliability and fairness of artificial intelligence (AI) is of utmost importance in a world where AI is integrated into crucial applications like healthcare, finance, and autonomous systems. AI models are different from conventional software: they operate based on the patterns learned from data, which makes them prone to biases, interpretability issues, and unknown behaviors in real-world application. Traditional software testing methods cannot be directly applied to AI systems since traditional testing approaches do not know about model drift, adversarial attacks, and dynamic data dependencies. This paper tackles those challenges specifically for testing, one of the critical components in software development processes, by discusses optimal ways of effectively testing AI powered applications, including model accuracy, fairness, interpretability, performance in edge cases, real time inference performance, etc.
Methodology -In order to assess challenges with AI testing, we performed an extensive review of existing methodologies in particular casting a look into adversarial testing, explainability, bias detection frameworks and automated AI testing tools. Models were evaluated based on both traditional and session-based schemas for their data and performance over time, with techniques ranging from synthetic data generation for edge cases, real-time performance benchmarking, and continuous monitoring of the deployed environments. We used various automated tools (e.g., TensorFlow Model Analysis, DeepChecks, and fairness indicators) to evaluate how the AI behaves with respect to every condition. The next section presents insights on AI robustness best practices from industry case studies and experimental work.
Experimental Results -In financial AI applications, empirical studies showed model fairness improvement of up to 20% through bias detection and mitigation techniques. Self-driving car AI models fortified with adversarial testing performed 18% better in extreme weather conditions. In fact, explainability tools SHAP and LIME were found to align with human expert decisions in legal AI models around 85% of the time. Moreover, by applying performance tuning strategies like quantization, the latency of the fraud detection AI applications was reduced by 30%, thus making real-time decision-making possible. Such findings emphasize the value of domain-specific AI testing methodologies in achieving reliability across a wide range of domains.
Conclusion & Final Thoughts -Getting AI to agree with the results of these kinds of audits requires shifting away from traditional software testing methods, adding fairness audits, interpretability tests, edge case evaluations, and constant monitoring of the model’s post-deployment. Model reliability is not a one-off class, hence, my paper emphasizes on automated AI testing frameworks and scalable CI/CD pipelines that can enhance the long-term reliability of the model. Research Directions to SELF Test AI System, Quantum AI Assessment, and Establishment of Common AI Testing Standard Implementing thorough AI testing procedures will enable organizations to deliver more resilient, equitable, and dependable AI systems, thereby fostering responsible and trustworthy AI-based decision-making.

Keywords: AI Testing, AI Model Accuracy, Fairness in AI, AI Bias Detection, Explainable AI (XAI), AI Interpretability, Edge Case Testing, Adversarial Testing, AI Performance Evaluation, Real-Time Inference, AI Testing Frameworks, Continuous Integration and Deployment (CI/CD), AI Model Drift, Automated AI Testing Tools, Synthetic Data Generation, AI Robustness, AI Trustworthiness, AI Scalability, Machine Learning Testing, Deep Learning Validation

Paper Id: 232177

Published On: 2023-01-04

Published In: Volume 11, Issue 1, January-February 2023

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