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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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. ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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Towards a roadmap on software engineering for responsible AI
CAIN '22: Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AIAlthough AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and frameworks for responsible AI have been issued recently. However, they are high level ...
First International Workshop on Software Engineering for Computational Science & Engineering
In recognition of the general lack of exposure scientists have to software engineering and vice versa, a workshop was held during the 2008 International Conference on Software Engineering in Leipzig, Germany. The workshop's goal was to bring together ...