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New framework REAL aims to improve ML system trustworthiness and alignment

By PulseAugur Editorial · [2 sources] ·

A new framework called REAL (Requirements Engineering for mAchines that Learn - and Fail) has been proposed to enhance the trustworthiness and stakeholder alignment of machine learning systems. This model-based framework integrates requirements for data, models, and the overall system, using system failures to explore alternative requirements. The approach emphasizes iterative and traceable refinement, demonstrated with an autonomous driving example to show improved alignment with stakeholder needs. AI

IMPACT This framework could lead to more trustworthy and aligned AI systems by systematically addressing requirements and failures.

RANK_REASON The cluster contains a research paper detailing a new framework for machine learning systems.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework REAL aims to improve ML system trustworthiness and alignment

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Amel Bennaceur, Gopi Krishnan Rajbahadur, Prince Mercy, Bashar Nuseibeh, Faeq Alrimawi ·

    From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems

    arXiv:2606.31589v1 Announce Type: cross Abstract: Organisations designing, developing, and deploying machine learning systems (MLS) need to be able to check that these systems are trustworthy, and communicate this clearly to their stakeholders, be they different categories of use…

  2. arXiv cs.LG TIER_1 English(EN) · Faeq Alrimawi ·

    From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems

    Organisations designing, developing, and deploying machine learning systems (MLS) need to be able to check that these systems are trustworthy, and communicate this clearly to their stakeholders, be they different categories of users, engineers, or wider society. By focusing on st…

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