generating an action recommendation that recommends mitigating or leveraging from a parameter forecast to a client device associated with a bias management service, wherein the parameter forecast is a forecast of future values of a parameter associated with a factor group of the bias management service;

obtaining an adjusted forecast from the client device, wherein the adjusted forecast is a modification in the parameter forecast based on an adjustment made by a cognitive system in the parameter forecast;

using a bias discovery and modelling module in a bias management server, classifying the adjusted forecast as a negative forecast bias or a positive forecast bias based on a deviation of the adjusted forecast from an actual value of the parameter associated with forecast;

using a bias quantification module in the bias management server, automatically determining a cognitive attribute of the cognitive system by generating a systematic pattern of the deviation of the adjusted forecast from an actual value of the parameter associated with the parameter forecast;

using the bias quantification module, determining a set of factors that are associated with the negative forecast bias or the positive forecast bias, wherein said set of factors is a combination of factors of an external factor group and a plurality of internal factor groups;

using a forecast skill scoring module in the bias management server, generating a skill score of the cognitive system that corresponds to a skill factor group using a machine learning engine, the machine learning engine comprising a machine learning model based on a tracking signal that detects trends in a forecast adjustment utilizing data from a repository, wherein the skill factor group is a combination of an external factor group and a plurality of internal factor groups; and

using a forecast adjustment recommendation module in the bias management server, generating a recommendation of improved adjusted forecast based on the cognitive attribute of the cognitive system and skill score of the cognitive system, wherein the improved adjusted forecast comprises an updated value of the adjusted forecast,

wherein the bias management server further comprises a bias management platform and a bias management environment, the bias management platform communicatively connected to the bias management environment to provide the bias management service to the client device through a network, the bias management environment including a machine learning based forecasting engine that continuously learns from forecast error scenarios by automatically addressing required changes to the machine learning based forecasting engine, the bias management platform comprising:

a use-case configurer module that captures a specification information of a use-case for the bias management service to be performed, the specification information including internal data of the use-case and a meta-data model of metrics which are applicable for the use-case, wherein the use-case configurer module enables the client device to select metrics of interest and models of other bias management services available in the bias management server to determine infrastructure of the bias management environment which may be required;

a service builder module configured to receive the specification information of the use-case from the use-case configurer module and assemble, validate and publish the bias management service to the service consumption module for consumption of the bias management service by the client device associated with the service;

a service consumption module connected to network to facilitate usage of the bias management service to the client device, wherein the service consumption module receives the bias management service published by the service builder module to be consumed by the client device;

a service manager module that manages the bias management service by communicating with the bias management environment; and

the repository that includes historical data of forecasts, actual value of the parameter, cognitive system adjustments, and data from the use-case configurer module, the service builder module, the service consumption module, and the service manager module.