ExplainabilityTrustworthinessTransparencycs.DC
ALPACA -- Adaptive Learning Pipeline for Comprehensive AI
Simon Torka, Sahin Albayrak
December 14, 2024
arXiv.org
3 citations
Key Takeaways
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Accessibility matters for AI adoption: ALPACA's design for "experts, professionals from different fields and laypeople" highlights that responsible AI requires making complex systems usable across different skill levels, not just for specialists—ease of use and trust are identified as central to whether people will actually accept AI systems.
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Explainable AI is a planned priority: The paper explicitly states that future versions will incorporate "explainable AI methods" alongside security improvements, recognizing that users need to understand how AI systems make decisions to trust them responsibly.
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Federated learning addresses privacy concerns: The planned support for "federated learning" indicates a commitment to processing data in ways that protect individual privacy, allowing AI systems to learn without centralizing sensitive information in one location.
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Real-world applications require robust infrastructure: ALPACA's use of Kubernetes for scalability and MongoDB for data management demonstrates that responsible AI deployment needs reliable, well-designed technical foundations to handle the full pipeline from data collection through evaluation—not just the model itself.
Abstract
The advancement of AI technologies has greatly increased the complexity of AI pipelines as they include many stages such as data collection, pre-processing, training, evaluation and visualisation. To provide effective and accessible AI solutions, it is important to design pipelines for different user groups such as experts, professionals from different fields and laypeople. Ease of use and trust play a central role in the acceptance of AI systems. The presented system, ALPACA (Adaptive Learning Pipeline for Advanced Comprehensive AI Analysis), offers a comprehensive AI pipeline that addresses the needs of diverse user groups. ALPACA integrates visual and code-based development and facilitates all key phases of the AI pipeline. Its architecture is based on Celery (with Redis backend) for efficient task management, MongoDB for seamless data storage and Kubernetes for cloud-based scalability and resource utilisation. Future versions of ALPACA will support modern techniques such as federated and continuous learning as well as explainable AI methods to further improve security, usability and trustworthiness. The application is demonstrated by an Android app for similarity recognition, which emphasises ALPACA's potential for use in everyday life.