Method and system for determining local fairness of ml model with degree of fairness

A system for local fairness detection, the system comprising:

• a memory storing instructions;
• one or more Input/Output (I/O) interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
• determine a first set of optimum clusters within a tabular base data using Gaussian Mixture Model (GMM) technique, wherein the tabular base data associated with a Machine Learning (ML) model comprises a protected attribute, a plurality of categorical features and a plurality of continuous features;
• determine a first cluster among the first set of optimum clusters to which a local instance of the ML model belongs to, based on the plurality of categorical features and the plurality of continuous features associated with the local instance;
• generate a subset of the tabular base data comprising i) the local instance, and ii) a perturbed dataset obtained by perturbing the tabular base data associated with the first cluster, wherein boundaries of perturbation are obtained using a constrained perturbation technique, wherein the protected attribute in the subset is flipped from an unprivileged group to a privileged group;
• determine a second set of optimum clusters by clustering the subset using the GMM technique to identify a second cluster among the second set of optimum of clusters comprising the local instance;
• select data points within the perturbed dataset that fall within the second cluster, wherein the local instance is excluded from the selected datapoints of the second cluster;
• obtain a class, and a probability of i) the selected datapoints, and ii) the local instance;
• and
• determine a local fairness of the ML model with a degree of fairness by:
• computing an individual similarity score for each of the selected datapoints by determining a cosine similarity between the probability of the local instance with the probability of each of the selected datapoints, wherein a value of the individual similarity score is i) positive if the class of the local instance maps to the class of a selected datapoint from among the selected datapoints, and ii) negative if the class of the local instance varies from the class of the selected datapoint;
• and
• obtaining an average similarity score by averaging the individual similarity score of each of the selected datapoints, wherein the ML model decision is:
• fair if the average similarity score is greater than zero, wherein a value of the average similarity score indicates the degree of fairness, and
• unfair if the average similarity score is equal to or less than zero, wherein the value of the average similarity score indicates a degree of unfairness.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• determining, a first set of optimum clusters within a tabular base data using Gaussian Mixture Model (GMM) technique, wherein the tabular base data associated with a Machine Learning (ML) model comprises a protected attribute, a plurality of categorical features and a plurality of continuous features;
• determining, a first cluster among the first set of optimum clusters to which a local instance of the ML model belongs to, based on the plurality of categorical features and the plurality of continuous features associated with the local instance;
• generating, a subset of the tabular base data comprising i) the local instance, and ii) a perturbed dataset obtained by perturbing the tabular base data associated with the first cluster, wherein boundaries of perturbation are obtained using a constrained perturbation technique, wherein the protected attribute in the subset is flipped from an unprivileged group to a privileged group;
• determining, a second set of optimum clusters by clustering the subset using the GMM technique to identify a second cluster among the second set of optimum of clusters comprising the local instance;
• selecting, data points within the perturbed dataset that fall within the second cluster, wherein the local instance is excluded from the selected datapoints of the second cluster;
• obtaining, a class, and a probability of i) the selected datapoints, and ii) the local instance;
• and
• determining, a local fairness of the ML model with a degree of fairness by:
• computing, an individual similarity score for each of the selected datapoints by determining a cosine similarity between the probability of the local instance with the probability of each of the selected datapoints, wherein a value of the individual similarity score is i) positive if the class of the local instance maps to the class of a selected datapoint from among the selected datapoints, and ii) negative if the class of the local instance varies from the class of the selected datapoint;
• and
• obtaining, an average similarity score by averaging the individual similarity score of each of the selected datapoints, wherein the ML model decision is:
• fair if the average similarity score is greater than zero, wherein a value of the average similarity score indicates the degree of fairness, and
• unfair if the average similarity score is equal to or less than zero, wherein the value of the average similarity score indicates a degree of unfairness.