Artificial Intelligence (AI) System Evaluation Task

An Artificial Intelligence (AI) System Evaluation Task is a software system evaluation task for assessing AI system properties (such as: AI system accuracy, AI system learning capability, AI system ethical implications).

• Context:
  • input: an AI System.
    • input (optional): a AI System Benchmark, a Standardized AI Testing Protocol.
  • output: a AI System Evaluation Result.
  • Task Performance Measure: evaluation accuracy, test coverage, assessment reliability
  • ...
  • It can (typically) implement standardized tests to measure system performance.
  • It can (typically) analyze statistical significance of performance metrics.
  • It can (typically) document system behavior across test cases.
  • It can (typically) assess model robustness through stress testing.
  • It can (often) be supported by an AI System Evaluation System.
  • It can (often) utilize AI System Evaluation Methods, for AI system assessment.
  • It can (often) compare system outputs against human baselines.
  • It can (often) measure computational efficiency and resource usage.
  • ...
  • It can range from being a Quantitative AI System Evaluation Task to being a Qualitative AI System Evaluation Task, depending on its assessment type.
  • It can range from being an Offline AI System Evaluation Task to being a Production AI System Evaluation Task, depending on its deployment phase.
  • It can range from being a Manual AI System Evaluation Task to an Automated AI System Evaluation Task, depending on its automation level.
  • ...
  • It can require User Study-Based AI System Evaluation with human participants.
  • It can include Expert Review-Based AI System Evaluation by domain specialists.
  • It can involve Ethical Assessment of AI system impacts.
  • It can incorporate Bias Testing for fairness evaluation.
  • ...
• Examples:
  • Domain-Specific AI Evaluation Tasks, such as:
    • Chatbot Evaluation Tasks assessing conversational ability, response accuracy, and user satisfaction.
    • Image Recognition System Evaluation Tasks measuring classification accuracy, processing speed, and reliability.
    • Recommendation System Evaluation Tasks analyzing relevance, personalization, and engagement.
    • ...
  • Critical AI System Evaluation Tasks, such as:
    • Self-Driving Car System Evaluation Tasks examining navigation safety, obstacle detection, and adaptive performance.
    • Healthcare AI System Evaluation Tasks assessing diagnosis accuracy, recommendation reliability, and data analysis precision.
    • Financial AI System Evaluation Tasks testing prediction accuracy, risk assessment, and compliance.
    • ...
  • Specialized AI Testing Tasks, such as:
    • Video Turing Test (VTT) for visual intelligence assessment.
    • Natural Language Processing System Evaluation Tasks for language capability assessment.
    • Reinforcement Learning System Evaluation Tasks for decision-making capability assessment.
    • ...
• Counter-Examples:
  • Non-Technical AI System Evaluation Tasks, which assess management strategies rather than AI systems.
  • Hardware AI System Evaluation Tasks, which focus on physical components rather than algorithmic aspects.
  • Financial AI System Analysis Tasks, which evaluate financial performance rather than AI capabilitys.
• See: AI System Development, Machine Learning Model Evaluation, User-Centered Design, Software Testing, AI System Benchmark, AI System Assessment Framework.

---

---

References

2021

• (Reddy et al., 2021) ⇒ S. Reddy, W. Rogers, V.P. Makinen, E. Coiera, et al. (2021). “Evaluation Framework to Guide Implementation of AI Systems into Healthcare Settings.” In: BMJ Health & Care Informatics. [URL: ncbi.nlm.nih.gov]
  • NOTE: It provides an evaluation framework that can be applied at any development or deployment stage of AI systems, with a focus on assessing technical capabilities within healthcare contexts.

2020

• (Jin et al., 2020) ⇒ C. Jin, W. Chen, Y. Cao, Z. Xu, Z. Tan, X. Zhang, L. Deng, et al. (2020). “Development and Evaluation of an AI System for COVID-19.” [URL: pesquisa.bvsalud.org]
  • NOTE: It discusses the development and evaluation of an AI system for COVID-19, detailing its comparative performance against radiologists in specific medical imaging applications.

2020

• (McKinney et al., 2020) ⇒ S.M. McKinney, M. Sieniek, V. Godbole, J. Godwin, et al. (2020). “International Evaluation of an AI System for Breast Cancer Screening.” In: Nature. [URL: nature.com]
  • NOTE: It focuses on evaluating a new AI system for breast cancer screening, emphasizing the system's development and its effectiveness in cancer detection in mammograms.

---