A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineering

Today, with the growing obsession with applying Artificial Intelligence (AI), particularly Machine Learning (ML), to software across various contexts, much of the focus has been on the effectiveness of AI models, often measured through common metrics such as F1- score, while fairness receives relatively little attention. This paper presents a review of existing gray literature, examining fairness requirements in AI context, with a focus on how they are defined across various application domains, managed throughout the Software Development Life Cycle (SDLC), and the causes, as well as the corresponding consequences of their violation by AI models. Our gray literature investigation shows various definitions of fairness requirements in AI systems, commonly emphasizing non-discrimination and equal treatment across different demographic and social attributes. Fairness requirement management practices vary across the SDLC, particularly in model training and bias mitigation, fairness monitoring and evaluation, and data handling practices. Fairness requirement violations are frequently linked, but not limited, to data representation bias, algorithmic and model design bias, human judgment, and evaluation and transparency gaps. The corresponding consequences include harm in a broad sense, encompassing specific professional and societal impacts as key examples, stereotype reinforcement, data and privacy risks, and loss of trust and legitimacy in AI-supported decisions. These findings emphasize the need for consistent frameworks and practices to integrate fairness into AI software, paying as much attention to fairness as to effectiveness.

An Empirical Study on Log-based Anomaly Detection Using Machine Learning

The growth of systems complexity increases the need of automated techniques dedicated to different log analysis tasks such as Log-based Anomaly Detection (LAD). The latter has been widely addressed in the literature, mostly by means of different deep learning techniques. Nevertheless, the focus on deep learning techniques results in less attention being paid to traditional Machine Learning (ML) techniques, which may perform well in many cases, depending on the context and the used datasets. Further, the evaluation of different ML techniques is mostly based on the assessment of their detection accuracy. However, this is is not enough to decide whether or not a specific ML technique is suitable to address the LAD problem. Other aspects to consider include the training and prediction time as well as the sensitivity to hyperparameter tuning. In this paper, we present a comprehensive empirical study, in which we evaluate different supervised and semi-supervised, traditional and deep ML techniques w.r.t. four evaluation criteria: detection accuracy, time performance, sensitivity of detection accuracy as well as time performance to hyperparameter tuning. The experimental results show that supervised traditional and deep ML techniques perform very closely in terms of their detection accuracy and prediction time. Moreover, the overall evaluation of the sensitivity of the detection accuracy of the different ML techniques to hyperparameter tuning shows that supervised traditional ML techniques are less sensitive to hyperparameter tuning than deep learning techniques. Further, semi-supervised techniques yield significantly worse detection accuracy than supervised techniques.