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SE4RAI '22: Proceedings of the 1st Workshop on Software Engineering for Responsible AI
ACM2022 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
ICSE '22: 44th International Conference on Software Engineering Pittsburgh Pennsylvania 19 May 2022
ISBN:
978-1-4503-9319-5
Published:
03 February 2023
Sponsors:
In-Cooperation:
IEEE CS
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Abstract

SE4RAI'22 is a forum where researchers, innovators, and leading professionals from both academia and industry can discuss the state and future of software engineering for responsible AI. SE4RAI'22 also aims to bring together researchers and practitioners from diverse disciplines such as software engineering, AI and social science to help tackle the end-to-end engineering challenges in developing AI systems responsibly. We hope that SE4RAI'22 will actively encourage a growing number of researchers to join this area.

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research-article
Open Access
Operationalizing machine learning models: a systematic literature review

Deploying machine learning (ML) models to production with the same level of rigor and automation as traditional software systems has shown itself to be a non-trivial task, requiring extra care and infrastructure to deal with the additional challenges. ...

short-paper
Robustness testing of a machine learning-based road object detection system: an industrial case

With the increasing development of critical systems based on artificial intelligence (AI), methods have been proposed and evaluated in academia to assess the reliability of these systems. In the context of computer vision, some approaches use the ...

short-paper
Open Access
Towards trusting the ethical evolution of autonomous dynamic ecosystems

Until recently, systems and networks have been designed to implement established actions within known contexts. However, gaining the human trust in system behavior requires development of artificial ethical agents proactively acting outside fixed ...

short-paper
Open Access
The concept of ethical digital identities

Dynamic changes within the cyberspace are greatly impacting human lives and our societies. Emerging evidence indicates that without an ethical overlook on technological progress, intelligent solutions created to improve and enhance our lives can easily ...

research-article
Challenges in machine learning application development: an industrial experience report

SAP is the market leader in enterprise application software offering an end-to-end suite of applications and services to enable their customers worldwide to operate their business. Especially, retail customers of SAP deal with millions of sales ...

research-article
Open Access
Non-functional requirements for machine learning: an exploration of system scope and interest

Systems that rely on Machine Learning (ML systems) have differing demands on quality---non-functional requirements (NFRs)---compared to traditional systems. NFRs for ML systems may differ in their definition, scope, and importance. Despite the ...

research-article
Open Access
Augur: a step towards realistic drift detection in production ML systems

The inference quality of deployed machine learning (ML) models degrades over time due to differences between training and production data, typically referred to as drift. While large organizations rely on periodic training to evade drift, the reality is ...

research-article
MLOps: a guide to its adoption in the context of responsible AI

DevOps practices have increasingly been applied to software development as well as the machine learning lifecycle, in a process known as MLOps. Currently, many professionals have written about this topic, but still few results can be found in the ...

Contributors
  • Commonwealth Scientific and Industrial Research Organisation
  • Commonwealth Scientific and Industrial Research Organisation
  • Commonwealth Scientific and Industrial Research Organisation
  • Monash University

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