Abstract: A computing platform installed with executable software for producing a fairer version of a machine learning model comprising a perturbed version of additive explanations for the model's features in which the additive explanations are perturbed by corresponding perturbation coefficients, where the computing platform is programmed to (i) produce candidates for the perturbation coefficients by carrying out a training process for a trainable object comprising the perturbation coefficients as trainable parameters, where the training process uses an objective function having an error term, a bias term, and a bias coefficient and involves multiple training rounds that are carried out using different bias-coefficient values and produce different candidates for the perturbation coefficients, (ii) evaluate the performance and bias achieved by the different candidates for the perturbation coefficients, and (iii) based on the evaluation, determine a given candidate for the coefficient values to use in the fairer version

Abstract: A computing platform is installed with executable software for producing a fairer version of a trained ensemble-based model in which the ensemble-based model's sub-models are adjusted by respective sub-model coefficients, where the computing platform is programmed to (i) produce multiple sets of candidate sub-model coefficient values by carrying out a training process for a trainable object comprising the sub-model coefficients as trainable parameters, where the training process uses an objective function having an error term, a bias term, and a bias coefficient and involves multiple training rounds that are each carried out using a different bias-coefficient value and each produces a respective set of candidate sub-model coefficient values, (ii) evaluate the performance and bias achieved by the multiple sets of candidate sub-model coefficient values, and (iii) based on the evaluation, determine a given set of sub-model coefficient values to use in the fairer version of the ensemble-based model.

Abstract: A computing platform installed with executable software for producing a fairer version of a machine learning (ML) model that is configured to output a predicted value for a numerical variable, where the computing platform is programmed to (i) initialize a subpopulation-dependent fairer version of the ML model, (ii) train a subpopulation-independent projection of the subpopulation-dependent fairer ML model, (iii) evaluate multiple candidate instances of a subpopulation-independent fairer version of the ML model comprising a linear combination of (a) an unadjusted predicted value for the numerical variable that is output by the ML model and (b) a fairness-adjusted predicted value for the numerical variable that is output by the subpopulation-independent projection, and (iv) based on evaluating the multiple candidate instances of the subpopulation-independent fairer ML model, select a given candidate instance of the subpopulation-independent fairer ML model that is thereafter deployed.

Abstract: A computing platform may be configured to (i) train an initial model object for a data science model using a machine learning process, (ii) determine that the initial model object exhibits a threshold level of bias, and (iii) thereafter produce an updated version of the initial model object having mitigated bias by (a) identifying a subset of the initial model object's set of input variables that are to be replaced by transformations, (b) producing a post-processed model object by replacing each respective input variable in the identified subset with a respective transformation of the respective input variable that has one or more unknown parameters, (c) producing a parameterized family of the post-processed model object, and (d) selecting, from the parameterized family of the post-processed model object, one given version of the post-processed model object to use as the updated version of the initial model object for the data science model.

Abstract: An example computing platform is configured to receive configuration data that defines a pipeline for building a deep learning model, the configuration data including data defining an input dataset, data type assignments for a set of input data variables included within the dataset, data transformations that are to be applied to the dataset, and a machine learning process that is to be utilized to train the deep learning model. Based on the received configuration data, the computing platform functions to build the deep learning model by obtaining the input dataset, assigning a data type to data in the dataset, selecting transformation operations for the data in the dataset, splitting the dataset into a sequence of data blocks, applying the transformation operations to each data block to produce a transformed dataset, generating a compressed data structure that includes the transformed datasets, and applying the machine learning process to the transformed datasets.

Abstract: An example computing platform is configured to receive configuration data that defines a pipeline for building a deep learning model, the configuration data including data defining an input dataset, data type assignments for a set of input data variables included within the dataset, data transformations that are to be applied to the dataset, and a machine learning process that is to be utilized to train the deep learning model. Based on the received configuration data, the computing platform functions to build the deep learning model by obtaining the input dataset, assigning a data type to data in the dataset, selecting transformation operations for the data in the dataset, splitting the dataset into a sequence of data blocks, applying the transformation operations to each data block to produce a transformed dataset, generating a compressed data structure that includes the transformed datasets, and applying the machine learning process to the transformed datasets.

Abstract: A computing platform may be configured to (i) train an initial model object for a data science model using a machine learning process, (ii) determine that the initial model object exhibits a threshold level of bias, and (iii) thereafter produce an updated version of the initial model object having mitigated bias by (a) identifying a subset of the initial model object's set of input variables that are to be replaced by transformations, (b) producing a post-processed model object by replacing each respective input variable in the identified subset with a respective transformation of the respective input variable that has one or more unknown parameters, (c) producing a parameterized family of the post-processed model object, and (d) selecting, from the parameterized family of the post-processed model object, one given version of the post-processed model object to use as the updated version of the initial model object for the data science model.

Abstract: An example computing platform is configured to receive configuration data that defines a pipeline for building a deep learning model, the configuration data including data defining an input dataset, data type assignments for a set of input data variables included within the dataset, data transformations that are to be applied to the dataset, and a machine learning process that is to be utilized to train the deep learning model. Based on the received configuration data, the computing platform functions to build the deep learning model by obtaining the input dataset, assigning a data type to data in the dataset, selecting transformation operations for the data in the dataset, splitting the dataset into a sequence of data blocks, applying the transformation operations to each data block to produce a transformed dataset, generating a compressed data structure that includes the transformed datasets, and applying the machine learning process to the transformed datasets.

Abstract: A computing platform may be configured to (i) train an initial model object for a data science model using a machine learning process, (ii) determine that the initial model object exhibits a threshold level of bias, and (iii) thereafter produce an updated version of the initial model object having mitigated bias by (a) identifying a subset of the initial model object's set of input variables that are to be replaced by transformations, (b) producing a post-processed model object by replacing each respective input variable in the identified subset with a respective transformation of the respective input variable that has one or more unknown parameters, (c) producing a parameterized family of the post-processed model object, and (d) selecting, from the parameterized family of the post-processed model object, one given version of the post-processed model object to use as the updated version of the initial model object for the data science model.

Abstract: A method for using a reusable sample-holding device for readily loading very small wet samples for observation of the samples by microscopic equipment, in particular in a vacuum environment. The method may be used with a scanning electron microscope (SEM), a transmission electron microscope (TEM), an X-ray microscope, optical microscope, and the like. For observation of the sample, the method provides a thin-membrane window etched in the center of each of two silicon wafers abutting to contain the sample in a small uniform gap formed between the windows. This gap may be adjusted by employing spacers. Alternatively, the thickness of a film established by the fluid in which the sample is incorporated determines the gap without need of a spacer. To optimize resolution each window may have a thickness on the order of 50 nm and the gap may be on the order of 50 nm.

Abstract: A method for using a reusable sample-holding device for readily loading very small wet samples for observation of the samples by microscopic equipment, in particular in a vacuum environment. The method may be used with a scanning electron microscope (SEM), a transmission electron microscope (TEM), an X-ray microscope, optical microscope, and the like. For observation of the sample, the method provides a thin-membrane window etched in the center of each of two silicon wafers abutting to contain the sample in a small uniform gap formed between the windows. This gap may be adjusted by employing spacers. Alternatively, the thickness of a film established by the fluid in which the sample is incorporated determines the gap without need of a spacer. To optimize resolution each window may have a thickness on the order of 50 nm and the gap may be on the order of 50 nm.

Abstract: A reusable sample-holding device for readily loading very small wet samples for observation of the samples by microscopic equipment, in particular in a vacuum environment. Embodiments may be used with a scanning electron microscope (SEM), a transmission electron microscope (TEM), an X-ray microscope, optical microscope, and the like. For observation of the sample, embodiments provide a thin-membrane window etched in the center of each of two silicon wafers abutting to contain the sample in a small uniform gap formed between the windows. This gap may be adjusted by employing spacers. Alternatively, the thickness of a film established by the fluid in which the sample is incorporated determines the gap without need of a spacer. To optimize resolution each window may have a thickness on the order of 50 nm and the gap may be on the order of 50 nm.

Abstract: A reusable sample-holding device for readily loading very small wet samples for observation of the samples by microscopic equipment, in particular in a vacuum environment. Embodiments may be used with a scanning electron microscope (SEM), a transmission electron microscope (TEM), an X-ray microscope, optical microscope, and the like. For observation of the sample, embodiments provide a thin-membrane window etched in the center of each of two silicon wafers abutting to contain the sample in a small uniform gap formed between the windows. This gap may be adjusted by employing spacers. Alternatively, the thickness of a film established by the fluid in which the sample is incorporated determines the gap without need of a spacer. To optimize resolution each window may have a thickness on the order of 50 nm and the gap may be on the order of 50 nm.