Session-based recommendation (SBR) targets a core scenario in e-Commerce: Given a sequence of interactions of a visitor with a selection of items, we want to recommend the next item(s) of interest to interact with. Unfortunately, SBR models are difficult to deploy in practice, as ($i$) session-based recommendations cannot be precomputed offline, but must be inferred online for ongoing user sessions with low latency, and (ii) there is a huge variety of SBR models available, typically designed by academic researchers, whose inference performance and deployment cost is unclear. As a result, data scientists must typically prototype and evaluate different deployment options in collaboration with devops teams - a tedious and costly process, which does not scale to multiple use cases. To alleviate this, we present Etude, an end-to-end bench-marking framework, which enables data scientists to automati-cally evaluate the inference performance of SBR models under different deployment options. With Etude, data scientists can declaratively specify workload statistics, hardware options, as well as latency and throughput constraints. Based on these, Etude automatically deploys and runs an inference benchmark in Kubernetes with a synthetically generated click workload. Sub-sequently, Etude provides the data scientists with measurements on the achieved throughput and latency, as a basis for deciding on feasible and cost-efficient deployment options. We detail the design of Etude and present an experimental study for ten different SBR models in challenging settings resembling real-world workloads encountered at the large Euro-pean e-Commerce platform bol.com. We determine performant and cost-efficient deployment options in terms of models and cloud instance types for a variety of online shopping use cases (ranging from grocery shopping to large e-Commerce platforms). Moreover, we identify severe performance bottlenecks in the open source TorchServe inference server from the PyTorch ecosystem and in the implementation of four SBR models from the open source RecBole library. We make the source code of our framework and experimental results publicly available.

In this paper, we interrogate whether data quality issues track demographic group membership (based on sex, race and age) and whether automated data cleaning — of the kind commonly used in production ML systems — impacts the fairness of predictions made by these systems. To the best of our knowledge, the impact of data cleaning on fairness in downstream tasks has not been investigated in the literature. We first analyse the tuples flagged by common error detection strategies in five research datasets. We find that, while specific data quality issues, such as higher rates of missing values, are associated with membership in historically disadvantaged groups, poor data quality does not generally track demographic group membership. As a follow-up, we conduct a large-scale empirical study on the impact of automated data cleaning on fairness, involving more than 26,000 model evaluations. We observe that, while automated data cleaning is unlikely to worsen accuracy, it is more likely to worsen fairness than to improve it, especially when the cleaning techniques are not carefully chosen. Furthermore, we find that the positive or negative impact of a particular cleaning technique often depends on the choice of fairness metric and group definition (single-attribute or intersectional). We make our code and experimental results publicly available. The analysis we conducted in this paper is difficult, primarily because it requires that we think holistically about disparities in data quality, disparities in the effectiveness of data cleaning methods, and impacts of such disparities on ML model performance for different demographic groups. Such holistic analysis can and should be supported by data engineering tools, and requires substantial data engineering research. Towards this goal, we discuss open research questions, envision the development of fairness-aware data cleaning methods, and their integration into complex pipelines for ML-based decision making.

In this paper, we interrogate whether data quality issues track demographic characteristics such as sex, race and age, and whether automated data cleaning — of the kind commonly used in production ML systems — impacts the fairness of predictions made by these systems. To the best of our knowledge, the impact of data cleaning on fairness in downstream tasks has not been investigated in the literature.We first analyze the tuples flagged by common error detection strategies in five research datasets. We find that, while specific data quality issues, such as higher rates of missing values, are associated with membership in historically disadvantaged groups, poor data quality does not generally track demographic group membership. As a follow-up, we conduct a large-scale empirical study on the impact of automated data cleaning on fairness, involving more than 26,000 model evaluations on five datasets. We observe that, while automated data cleaning has an insignificant impact on both accuracy and fairness in the majority of cases, it is more likely to worsen fairness than to improve it, especially when the cleaning techniques are not carefully chosen. This finding is both significant and worrying, given that it potentially implicates many production ML systems. We make our code and experimental results publicly available.The analysis we conducted in this paper is difficult, primarily because it requires that we think holistically about disparities in data quality, disparities in the effectiveness of data cleaning methods, and impacts of such disparities on ML model performance for different demographic groups. Such holistic analysis can and should be supported with the help of data engineering research. Towards this goal, we envision the development of fairness-aware data cleaning methods, and their integration into complex pipelines for ML-based decision making.

Software systems that learn from data with machine learning (ML) are used in critical decision-making processes. Unfortunately, real-world experience shows that the pipelines for data preparation, feature encoding and model training in ML systems are often brittle with respect to their input data. As a consequence, data scientists have to run different kinds of data centric what-if analyses to evaluate the robustness and reliability of such pipelines, e.g., with respect to data errors or preprocessing techniques. These what-if analyses follow a common pattern: they take an existing ML pipeline, create a pipeline variant by introducing a small change, and execute this variant to see how the change impacts the pipeline's output score. We recently proposed mlwhatif, a library that enables data scientists to declaratively specify what-if analyses for an ML pipeline, and to automatically generate, optimize and execute the required pipeline variants. We demonstrate how data scientists can leverage mlwhatif for a variety of pipelines and three different what-if analyses focusing on the robustness of a pipeline against data errors, the impact of data cleaning operations, and the impact of data preprocessing operations on fairness. In particular, we demonstrate step-by-step how mlwhatif generates and optimizes the required execution plans for the pipeline analyses. Our library is publicly available at https://github.com/stefan-grafberger/mlwhatif.

Machine Learning (ML) is increasingly used to automate impactful decisions, and the risks arising from this wide-spread use are garnering attention from policymakers, scientists, and the media. ML applications are often very brittle with respect to their input data, which leads to concerns about their reliability, accountability, and fairness. While bias detection cannot be fully automated, computational tools can help pinpoint particular types of data issues.