Are We Aligned? A Preliminary Investigation of the Alignment of Responsible AI Values between LLMs and Human Judgment

Large Language Models (LLMs) are increasingly employed in software engineering tasks such as requirements elicitation, design, and evaluation, raising critical questions regarding their alignment with human judgments on responsible AI values. This study investigates how closely LLMs' value preferences align with those of two human groups: a US-representative sample and AI practitioners. We evaluate 23 LLMs across four tasks: (T1) selecting key responsible AI values, (T2) rating their importance in specific contexts, (T3) resolving trade-offs between competing values, and (T4) prioritizing software requirements that embody those values. The results show that LLMs generally align more closely with AI practitioners than with the US-representative sample, emphasizing fairness, privacy, transparency, safety, and accountability. However, inconsistencies appear between the values that LLMs claim to uphold (Tasks 1-3) and the way they prioritize requirements (Task 4), revealing gaps in faithfulness between stated and applied behavior. These findings highlight the practical risk of relying on LLMs in requirements engineering without human oversight and motivate the need for systematic approaches to benchmark, interpret, and monitor value alignment in AI-assisted software development.

Data Requirement Goal Modeling for Machine Learning Systems

Machine Learning (ML) has been integrated into various software and systems. Two main components are essential for training an ML model: the training data and the ML algorithm. Given the critical role of data in ML system development, it has become increasingly important to assess the quality of data attributes and ensure that the data meets specific requirements before its utilization. This work proposes an approach to guide non-experts in identifying data requirements for ML systems using goal modeling. In this approach, we first develop the Data Requirement Goal Model (DRGM) by surveying the white literature to identify and categorize the issues and challenges faced by data scientists and requirement engineers working on ML-related projects. An initial DRGM was built to accommodate common tasks that would generalize across projects. Then, based on insights from both white and gray literature, a customization mechanism is built to help adjust the tasks, KPIs, and goals' importance of different elements within the DRGM. The generated model can aid its users in evaluating different datasets using GRL evaluation strategies. We then validate the approach through two illustrative examples based on real-world projects. The results from the illustrative examples demonstrate that the data requirements identified by the proposed approach align with the requirements of real-world projects, demonstrating the practicality and effectiveness of the proposed framework. The proposed dataset selection customization mechanism and the proposed DRGM are helpful in guiding non-experts in identifying the data requirements for machine learning systems tailored to a specific ML problem. This approach also aids in evaluating different dataset alternatives to choose the optimum dataset for the problem. For future work, we recommend implementing tool support to generate the DRGM based on a chatbot interface.

Text-to-Image Representativity Fairness Evaluation Framework

Text-to-Image generative systems are progressing rapidly to be a source of advertisement and media and could soon serve as image searches or artists. However, there is a significant concern about the representativity bias these models embody and how these biases can propagate in the social fabric after fine-tuning them. Therefore, continuously monitoring and evaluating these models for fairness is important. To address this issue, we propose Text-to-Image (TTI) Representativity Fairness Evaluation Framework. In this framework, we evaluate three aspects of a TTI system; diversity, inclusion, and quality. For each aspect, human-based and model-based approaches are proposed and evaluated for their ability to capture the bias and whether they can substitute each other. The framework starts by suggesting the prompts for generating the images for the evaluation based on the context and the sensitive attributes under study. Then the three aspects are evaluated using the proposed approaches. Based on the evaluation, a decision is made regarding the representativity bias within the TTI system. The evaluation of our framework on Stable Diffusion shows that the framework can effectively capture the bias in TTI systems. The results also confirm that our proposed model based-approaches can substitute human-based approaches in three out of four components with high correlation, which could potentially reduce costs and automate the process. The study suggests that continual learning of the model on more inclusive data across disadvantaged minorities such as Indians and Middle Easterners is essential to mitigate current stereotyping and lack of inclusiveness.

You Only Look Twice! for Failure Causes Identification of Drill Bits

Efficient identification of the root causes of drill bit failure is crucial due to potential impacts such as operational losses, safety threats, and delays. Early recognition of these failures enables proactive maintenance, reducing risks and financial losses associated with unforeseen breakdowns and prolonged downtime. Thus, our study investigates various causes of drill bit failure using images of different blades. The process involves annotating cutters with their respective locations and damage types, followed by the development of two YOLO Location and Damage Cutter Detection models, as well as multi-class multi-label Decision Tree and Random Forests models to identify the causes of failure by assessing the cutters' location and damage type. Additionally, RRFCI is proposed for the classification of failure causes. Notably, the cutter location detection model achieved a high score of 0.97 mPA, and the cutter damage detection model yielded a 0.49 mPA. The rule-based approach over-performed both DT and RF in failure cause identification, achieving a macro-average F1-score of 0.94 across all damage causes. The integration of the complete automated pipeline successfully identified 100\% of the 24 failure causes when tested on independent sets of ten drill bits, showcasing its potential to efficiently assist experts in identifying the root causes of drill bit damages.