The 2nd International Workshop on Data Quality Assessment for Machine Learning (DQAML'21) is organized in conjunction with the Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). This workshop aims to serve as a forum for the presentation of research related to data quality assessment and remediation in AI/ML pipeline. Data quality is a critical issue in the data preparation phase and involves numerous challenging problems related to detection, remediation, visualization and evaluation of data issues. The workshop aims to provide a platform to researchers and practitioners to discuss such challenges across different modalities of data like structured, time series, text and graphical. The aim is to attract perspectives from both industrial and academic circles.

Significant effort is being put into developing industrial applications for artificial intelligence (AI), especially those using machine learning (ML) techniques. Despite the intensive support for building ML applications, there are still challenges when it comes to evaluating, assuring, and improving the quality or dependability. The difficulty stems from the unique nature of ML, namely, system behavior is derived from training data not from logical design by human engineers. This leads to black-box and intrinsically imperfect implementations that invalidate many principles and techniques in traditional software engineering. In light of this situation, the Japanese industry has jointly worked on a set of guidelines for the quality assurance of AI systems (in the Consortium of Quality Assurance for AI-based Products and Services) from the viewpoint of traditional quality-assurance engineers and test engineers. We report on the second version of these guidelines, which cover a list of quality evaluation aspects, catalogue of current state-of-the-art techniques, and domain-specific discussions in five representative domains. The guidelines provide significant insights for engineers in terms of methodologies and designs for tests driven by application-specific requirements.