The use of machine learning (ML) based systems has become ubiquitous including their usage in critical applications like medicine and assistive technologies. Therefore, it is important to determine the trustworthiness of these ML models and tasks. A key component in this determination is the development of task specific datasets, metrics, and best practices which are able to measure the various aspects of responsible model development and deployment including robustness, interpretability and fairness. Further, datasets are also key when training for a given task, be it coreference resolution in language modeling or facial recognition in computer vision. Imbalances and inadequate representation in datasets can have repercussions of an undesirable nature. Some common examples include how coreference resolution systems in NLU are often not all gender inclusive, discrepancies in the measurement of how robust and trustworthy machine predictions are in domains where the selective labels problem is prevalent, and discriminatory determination of pain or care levels of people belonging to different demographics in health science applications. Development of task specific datasets which do better in this regard is also extremely vital. In this workshop, we invite contributions towards different (i) datasets which help enhance task performance and inclusivity, (ii) measures and metrics which help in determining the trustworthiness of a model/dataset, (iii) assessment or remediation tools for fairer, more transparent, robust, and reliable models, and (iv) case studies describing responsible development and deployment of AI systems across fields such as healthcare, financial services, insurance, etc. The datasets, measures, mitigation techniques, and best practices could focus on different areas including (but not restricted to) the following: Fairness and Bias Robustness Reliability and Safety Interpretability Explainability Ethical AI Causal Inference Counterfactual Example Analysis They could also be focussed on the applications in diverse fields such as industry, finance, healthcare and beyond. Text based datasets can be in languages other than English as well.

Word vector embeddings have been shown to contain and amplify biases in data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this tutorial, we will review a collection of state-of-the-art debiasing techniques. To aid this, we provide an open source web-based visualization tool and offer hands-on experience in exploring the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, we decompose each technique into interpretable sequences of primitive operations, and study their effect on the word vectors using dimensionality reduction and interactive visual exploration.

We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional space. Our methods extend approaches known as Absolute Orientation, which are popular for aligning objects in three-dimensions, and generalize an approach by Smith et al. (ICLR 2017). We prove new results for optimal scaling and for maximizing cosine similarity. Then we demonstrate how to evaluate the similarity of embeddings from different sources or mechanisms, and that certain properties like synonyms and analogies are preserved across the embeddings and can be enhanced by simply aligning and averaging ensembles of embeddings.