In certain embodiments, the adjustment module 206, in one embodiment, may decide which accessibility features to enable and/or disable using machine learning, artificial intelligence, or the like. For example, the adjustment module 206 may provide information identifying one or more accessibility capabilities of a mobile device, active accessibility features, or the like as one or more inputs into machine learning, artificial intelligence, or the like, and receive a selection, prediction, and/or recommendation of one or more accessibility features to replace, substitute, provide, limit, or the like as one or more outputs. In a further embodiment, the adjustment module 206 may use a decision tree, a predetermined ruleset, a look up table (LUT), or the like to determine one or more accessibility features of a mobile device to replace, substitute, provide, and/or to limit, based on one or more detected accessibility features, capabilities, or the like of the mobile device.

FIG. 4 depicts a flow-chart diagram illustrating one embodiment of a method 400 for on-device feature and performance testing and/or adjustment. In one embodiment, the method 400 begins and the audit module 202 determines 402 one or more capabilities of a mobile device. In certain embodiments, the feature module 204 determines 404 one or more potential features that are executable on the mobile device. In further embodiments, the adjustment module 206 selectively configures 406, during runtime, the one or more potential features that are executable on the mobile device in response to execution of the one or more potential features being affected by the one or more capabilities of the mobile device, and the method 400 ends.