Towards Requirements Engineering for GenAI-Enabled Software: Bridging Responsibility Gaps through Human Oversight Requirements

TL;DR

This paper proposes a systematic approach to address responsibility gaps in GenAI-enabled software by integrating human oversight requirements into requirements engineering, enhancing accountability and traceability.

Contribution

It introduces a multi-layered methodology for conceptualizing, identifying, and formalizing responsibility gaps and oversight requirements in GenAI systems.

Findings

The methodology improves gap identification accuracy.

It enhances traceability of responsibility in GenAI systems.

User study confirms effectiveness over baseline approaches.

Abstract

Context: Responsibility gaps, long-recognized challenges in socio-technical systems where accountability becomes diffuse or ambiguous, have become increasingly pronounced in GenAI-enabled software. The generative and adaptive nature complicates how human oversight and responsibility are specified, delegated, and traced. Existing requirements engineering (RE) approaches remain limited in addressing these phenomena, revealing conceptual, methodological, and artifact-level research gaps.. Objective: This study aims to analyze these research gaps in the context of GenAI-enabled software systems. It seeks to establish a coherent perspective for a systematic analysis of responsibility gaps from a human oversight requirements standpoint, encompassing how these responsibility gaps should be conceptualized, identified, and represented throughout the RE process. Methods: The proposed design methodology is structured across three analytical layers. At the conceptualization layer, it establishes a conceptual framing that defines the key elements of responsibility across the human and system dimensions and explains how potential responsibility gaps emerge from their interactions. At the methodological layer, it introduces a deductive pipeline for identifying responsibility gaps by analyzing interactions between these dimensions and deriving corresponding oversight requirements within established RE frameworks. At the artifact layer, it formalizes the results in a Deductive Backbone Table, a reusable representation that traces the reasoning path from responsibility gaps identification to human oversight requirements derivation. Results: A user study compared the proposed methodology with a baseline goal-oriented RE across two scenarios. Evaluation across six dimensions indicated clear improvements of the proposed methodology, confirming its effectiveness in addressing three research gaps.