Abstract: Provided are methods, systems, and computer-storage media for developing machine learning technology that is less susceptible to bias problems. A machine learning model may be developed with reduced error attributed to one or more sensitive features by utilizing a loss adjustment weight to determine an adjusted loss function used to train the model. The loss adjustment weight may be determined based on a count of a feature-label combination of a sensitive feature. The adjusted loss function is determined and configured to use the loss adjustment weight when determining loss during model training, and the output of the adjusted loss function is an adjusted loss. The machine learning model may be trained until the adjusted loss satisfies a loss threshold, indicative of an acceptable level of model inaccuracy. Accordingly, present embodiments can provide use case specific tailoring to improve machine learning systems by removing biases associated with certain data features.

Abstract: A client computing architecture is provided that utilized minimal download and simulated page navigation features. A client computer may receive a payload comprising delta data in response to a navigation request for an electronic page. The client computer may further parse the delta data for the electronic page, update page contents for the electronic page based on the received delta data, determine whether one or more errors are associated with a page load process for the electronic page, manage a fragment identifier portion of a uniform resource locator (URL) to track a current location in a navigation of the electronic page, and intercept one or more navigation click events to ensure a minimal download strategy navigation.

Abstract: A client computing architecture is provided that utilized minimal download and simulated page navigation features. A client computer may receive a payload comprising delta data in response to a navigation request for an electronic page. The client computer may further parse the delta data for the electronic page, update page contents for the electronic page based on the received delta data, determine whether one or more errors are associated with a page load process for the electronic page, manage a fragment identifier portion of a uniform resource locator (URL) to track a current location in a navigation of the electronic page, and intercept one or more navigation click events to ensure a minimal download strategy navigation.

Abstract: A client computing architecture is provided that utilized minimal download and simulated page navigation features. A client computer may receive a payload comprising delta data in response to a navigation request for an electronic page. The client computer may further parse the delta data for the electronic page, update page contents for the electronic page based on the received delta data, determine whether one or more errors are associated with a page load process for the electronic page, manage a fragment identifier portion of a uniform resource locator (URL) to track a current location in a navigation of the electronic page, and intercept one or more navigation click events to ensure a minimal download strategy navigation.

Abstract: A server computing architecture is provided that uses minimal download features and includes error failover functionality. A server computer may receive an electronic page request and calculate a delta between a known static baseline electronic page and the requested electronic page. The static baseline electronic page may include an electronic page without content that utilizes a master electronic page. The master electronic page may include shared content for electronic pages associated with a website. The server computer may further determine whether one more errors are associated with rendering the calculated delta, render a payload comprising only the calculated delta upon determining an absence of the one or more errors and failover to one or more options upon determining that the one or more errors are associated with rendering the calculated delta.

Abstract: A collaboration icon system displays collaboration icons representing a current collaboration state of a resource. The collaboration icon system determines the collaboration state of the resource. When the collaboration state indicates that the resource does not have multiple editors, the collaboration icon system displays a share icon that is selectable to support sharing the resource. When the collaboration state indicates that the resource has multiple editors, the collaboration icon system displays an editors icon to support sharing the resource and accessing information relating to the editors. The share icon and the editors icon provide an indication of the collaboration state while the resource is being accessed.