A data statement, according to the authors, is … a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize,how software might be appropriately deployed,and what biases might be reflected in systems built on the software. (587) This paper specifically focuses on ethically responsive NLP technology. The authors argue that a data statement should be an integral part of work and writing on NLP. Read more...

Word embeddings are a widely used representation for text data. A well-known example in natural language processing (NLP) is Word2vec, which uses a neural network to learn latent vector representations of words. It turns out that relations in this latent vector space capture semantic relations quite well. For example, by finding similar vectors you typically end up with highly related or synonymous words. Another typical example is that when you add up the vectors of “king” and “woman”, you end up with the vector corresponding to “queen”, so even a form of conceptual calculus is possible. Read more...

ELI5 (“Explain Like I’m 5”) provides model-specific support for models from scikit-learn, lightning, decision tree ensembles using the xgboost, LightGBM, CatBoost libraries. ELI5 mainly provides convenient wrappers to couple the feature importance coefficients that these libraries already provide with feature names, as well as convenient ways to visualize importances, e.g. by highlighting words in a text. For Keras image classifiers an implementation of the gradient-based Grad-CAM visualizations is offered, but the TensorFlow V2 backend is not supported. Read more...

This handcrafted dataset can be used to evaluate bias in AI using text data for NLP tasks. Dataset description: Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems and resources. Further, there is a lack of benchmark datasets for examining inappropriate biases in system predictions. Here, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. Read more...

The OpenMined community is a collaboration of several organizations, including TensorFlow, PyTorch and Keras, to create an open-source ecosystem of privacy tools that extend libraries such as PyTorch with cryptographic techniques and differential privacy. The aim is to contribute to the adaptation of privacy-preserving AI. To this end, OpenMined offers several privacy-preserving tools on their github. A main tool is PySyft, which allows “computing on data you do not own and cannot see”. Read more...

TensorFlow Privacy is a library that allows you to replace default TensorFlow optimizers with optimizers that allow training with differential privacy, i.e. they implement forms of stochastic gradient descent (SGD) with differential privacy. Because large neural networks or other differentiable models have a very large learning capacity, it can happen that the model achieves high performance on uncommon training input by simply “memorizing” the training input. If the training data is sensitive, for example information about a specific user, this is undesired behavior that may leak private information. Read more...