Teaching Software Engineering for AI-Enabled Systems
원문 초록
Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components. Systems with artificial-intelligence or machine-learning (ML) components raise new challenges and require careful engineering. We designed a new course to teach software-engineering skills to students with a background in ML. We specifically go beyond traditional ML courses that teach modeling techniques under artificial conditions and focus, in lecture and assignments, on realism with large and changing datasets, robust and evolvable infrastructure, and purposeful requirements engineering that considers ethics and fairness as well. We describe the course and our infrastructure and share experience and all material from teaching the course for the first time.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation
FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)
My Answer Is NOT 'Fair': Mitigating Social Bias in Vision-Language Models via Fair and Biased Residuals
The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas
Internal and External Impacts of Natural Language Processing Papers
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.