{"id":19,"date":"2024-06-16T15:13:24","date_gmt":"2024-06-16T15:13:24","guid":{"rendered":"https:\/\/collab.di.uniba.it\/qualaiproject\/?page_id=19"},"modified":"2026-03-26T15:24:16","modified_gmt":"2026-03-26T15:24:16","slug":"goals","status":"publish","type":"page","link":"https:\/\/collab.di.uniba.it\/qualaiproject\/goals\/","title":{"rendered":"Goals"},"content":{"rendered":"
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QualAI<\/strong> is a two-year Italian national research project aimed at building a comprehensive framework to continuously monitor, assess, and improve the quality of ML-based software systems \u2014 from data and models to deployment and operations.<\/p>\n

Why it matters<\/strong><\/div>\n<\/div>\n
In 2020, a Google Health AI classifier for diabetic retinopathy achieved over 90% accuracy in the lab \u2014 yet failed in practice, discarding more than one-fifth of real hospital images and causing diagnostic delays of months. This case illustrates a fundamental truth: the quality of an ML-based system is far more than the accuracy of its model<\/strong>. QualAI addresses the full spectrum of quality challenges that arise throughout the lifecycle of AI systems, from training data and model design to integration, deployment, and live operation.<\/div>\n

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Research objectives<\/strong><\/p>\n

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OB1 – M<\/span>onitoring Framework: Define a shared knowledge base drawing on source code, notebooks, and SE-related data sources, providing the data foundation for all quality assessment approaches.<\/p>\n<\/div>\n

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OB2 – <\/span>Data & ML Model Quality:\u00a0 Detect and mitigate quality issues in training data and ML models, covering robustness, fairness, privacy, interpretability, efficiency, and reproducibility \u2014 including computational notebooks.<\/p>\n<\/div>\n

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OB3 – <\/span>ML Integration Quality: Identify quality smells and communication gaps between data scientists and software engineers, address technology mismatches, and safeguard system security at the integration level.<\/p>\n<\/div>\n

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OB4\u00a0 – <\/span>Deployment & Operations: Detect configuration smells in CI\/CD pipelines and container images, monitor live systems for data drift, and automatically suggest corrective actions to keep deployed models reliable.<\/p>\n<\/div>\n

The research will be conducted in line with the principles:<\/p>\n<\/div>\n

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\u2696\ufe0f Cost-Effective Recommendations<\/div>\n
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Every recommendation ranks issues by the ratio of remediation cost to quality benefit, helping teams prioritise what matters most.<\/div>\n
<\/div>\n<\/div>\n<\/div>\n
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\ud83d\udd0d Explainable Outputs<\/div>\n
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Each recommender provides human-readable rationale \u2014 in text or visual form \u2014 so practitioners can understand and trust the suggestions they receive.<\/div>\n
<\/div>\n<\/div>\n<\/div>\n
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\ud83d\udd04 CI\/CD Integration<\/div>\n
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QualAI slots into existing pipelines, triggering quality checks automatically on every commit throughout the development lifecycle.<\/div>\n
<\/div>\n<\/div>\n<\/div>\n
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\ud83d\udd2cEmpirical Validation<\/div>\n
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All proposed approaches are validated through mixed-method studies combining repository mining, surveys, and interviews with industrial practitioners.<\/div>\n<\/div>\n<\/div>\n<\/div>\n

<\/p>\n

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Expected Outcome<\/strong><\/div>\n

QualAI will deliver a suite of approaches to assess and monitor the quality of an ML-based system across multiple dimensions \u2014 from data integrity to operational stability. All tools and datasets will be released under open-source licences.<\/p>\n<\/div>\n<\/div>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

QualAI is a two-year Italian national research project aimed at building a comprehensive framework to continuously monitor, assess, and improve the quality of ML-based software systems \u2014 from data and models to deployment and operations. Why it matters In 2020,<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-19","page","type-page","status-publish","hentry"],"yoast_head":"\nGoals - QualAI - Continuous Quality Improvement of AI-based Systems<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/collab.di.uniba.it\/qualaiproject\/goals\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Goals - QualAI - Continuous Quality Improvement of AI-based Systems\" \/>\n<meta property=\"og:description\" content=\"QualAI is a two-year Italian national research project aimed at building a comprehensive framework to continuously monitor, assess, and improve the quality of ML-based software systems \u2014 from data and models to deployment and operations. 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