Decreasing Error in a Machine Learning Model Based on Identifying Reference and Monitored Groups of the Machine Learning Model

A machine learning model data quality improvement detection tool is provided for identifying an accurate reference group and an accurate monitored group of a machine learning model. The tool monitors a behavior of the machine learning model for a predetermined time frame. The tool compares a determined fairness metric a pre-defined fairness threshold. Responsive to the fairness metric failing to meet the pre-defined fairness threshold, the tool modifies the monitored group to include a first portion of the reference group. The tool compares a newly determined fairness metric to the pre-defined fairness threshold. Responsive to the newly determined fairness metric meeting the pre-defined fairness threshold, the tool identifies the modified monitored group including the first portion of the user-defined reference group as a new monitored group and the modified reference group without the first portion of the user-defined reference group as a new reference group.

BACKGROUND

The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for decreasing error in a machine learning model based on an identification of monitored and reference groups of the machine learning model.

Decision support computing systems rely on artificial intelligence and complex analytics to be able to perform analysis of large datasets, typically to identify patterns of data within the large datasets, to thereby generate insights into the data and provide such insights as a basis for making decisions. IBM Watson® (IBM Watson and all IBM Watson-based trademarks and logos are trademarks or registered trademarks of International Business Machines Corporation and/or its affiliates) cognitive system available from International Business Machines (IBM®) (IBM and all IBM-based trademarks and logos are trademarks or registered trademarks of International Business Machines Corporation and/or its affiliates) corporation of Armonk, N.Y. is one example of a supercomputing system that combines artificial intelligence and complex analytics operating on large datasets to be able to make accurate predictions, automate aspects of decision support computing systems, and perform various recognition and classification operations that generate results upon which downstream computing systems base their operations. IBM Watson® has been used to implement various types of recognition, classification, decision support, and prediction computer operations including visual recognition, language translation, natural language classification, personality insights, tone analyzer, question answering, and even automated music composition and recipe generation.

The underlying artificial intelligence and analytics computing systems used in such decision support computing systems is dependent upon a machine learning process using a set of training data. If this training data comprises erroneous inclinations, i.e. erroneous inclination towards a group defined by a set of one or more attributes (often referred to as protected attributes), the erroneous inclinations will influence the training of the artificial intelligence computing system causing potential erroneous inclinations in operations of the computing systems that rely on the results of the artificial intelligence computing system to perform their operations, such as decision support operations. Such erroneous inclinations may result in incorrect results being generated by the artificial intelligence computing system and any downstream computing systems that rely on the results generated by the artificial intelligence computing system. Moreover, such erroneous inclinations may exist in the data upon which such artificial intelligence computer systems operate after training of the artificial intelligence computer system.

SUMMARY

In one illustrative embodiment, a method is provided, in a data processing system comprising a processor and memory, the memory comprising instructions executed by the processor to cause the processor to implement a machine learning model data quality improvement detection tool that identifies an accurate reference group and an accurate monitored group of a machine learning model. The illustrative embodiment monitors a behavior of the machine learning model for a predetermined time frame. The illustrative embodiment compares a determined fairness metric that utilizes a percentage of favorable outcomes of a user-defined monitored group and a percentage of favorable outcomes of a user-defined reference group to a pre-defined fairness threshold. The illustrative embodiment modifies the monitored group to include a first portion of the reference group thereby forming a modified monitored group and a modified reference group in response to the fairness metric failing to meet the pre-defined fairness threshold. The illustrative embodiment compares a newly determined fairness metric that utilizes a percentage of favorable outcomes of the modified monitored group and a percentage of favorable outcomes of the modified reference group to the pre-defined fairness threshold. The illustrative embodiment identifies the modified monitored group including the first portion of the user-defined reference group as a new monitored group and the modified reference group without the first portion of the user-defined reference group as a new reference group in response to the newly determined fairness metric meeting the pre-defined fairness threshold.

DETAILED DESCRIPTION

As noted above, erroneous inclinations may cause incorrect operation of artificial intelligence computing systems and/or the downstream computing systems that operate on the results generated by such artificial intelligence computing systems. That is, artificial intelligence is a key element for modern-day applications and these modern-day applications are only effective when they are making accurate predictions. Any wrong prediction or a generalized insight about certain population may lead to higher error rates and more so may even lead to negative press publicity to enterprises. For example, a recruiting tool showing erroneous inclinations against a particular gender, a job recruitment tool showing erroneous inclinations towards applicants who are known to speak specific spoken languages, or an insurance tool making an insight that group A is prone to make more fraudulent insurance claims when compared to their counterpart group B, or to say the otherwise—group B makes valid claims when compared to group A, may have an adverse impact to the enterprise either directly or indirectly.

A key point is that it is not always true that the machine learning model is showing an error or inclination against one group as one sub-group of the group may as well get favorable outcomes. And, likewise, it is not always that the one group is making valid insurance claims as one sub-group of the group might be making fraudulent insurance claims as well. Similar to the way of detecting erroneous inclinations in the machine learning models is essential to build trust for any application or enterprise, it is equally important to precisely identify to which population group(s) that the machine learning model is making erroneous predictions so as to remove erroneous inclinations of the machine learning model by utilizing the refined the reference and monitored values.

There are multiple techniques that exist to detect erroneous inclinations in machine learning models for both regression and classification type models. These techniques compute whether the machine learning model is exhibiting erroneous inclination against a monitored group as compared its counterpart a reference group. For example, a loan machine learning model may be erroneously inclined to give 90% of favorable outcomes to loan applicants within group B as compared to only 75% of favorable outcomes to group A applicants. If a fairness calculation is determined using a disparate impact ratio, then the metric would turn out to be 75/90=83%. If a machine learning model validator has a fairness threshold as 90% (meaning at least 90% of the monitored population should get 90% of favorable outcomes) then an inference may be made that the machine learning model is erroneous inclined against the group A.

In order to detect such erroneous inclinations, a machine learning model data quality improvement detection engine receives specified input from a user as to a reference value and a monitored value. In the above example, the monitored value is associated with group A and the reference value is associated with group B. Many times, clients face a problem that they are not accurately aware of the monitored and reference ranges that need to be utilized, e.g., in the above use case, the machine learning model may also be acting in an erroneous manner for group B. The client may not be aware of the inaccurate specified reference value and monitored value, which may lead to lawsuits, negative publicity, or the like. Hence, the illustrative embodiments provide mechanisms for identifying accurate reference and monitored values utilized by a machine learning model so as to provide an accurate picture of the machine learning model behavior to the client. In addition, the illustrative embodiments provide mechanisms for removing erroneous inclinations of the machine learning model by utilizing the refining the reference and monitored values. In cases where the user has specified reference and monitored values that detect erroneous inclinations so as to accurately train an artificial intelligence (AI) computing system, such as a cognitive computing system, neural network computer model, deep learning network computer model, ensemble learning computer model, or any other machine learning computer model or computing system, the AI computing system and/or computer model may be retrained based on the identified accurate reference and monitored values so as to remove any erroneous inclinations in the training of the AI computing system or computer model.

It should be appreciated that the present invention provides an improved computer tool and improved computer tool operations that identify reference and monitored values utilized by a machine learning model so as to provide an accurate picture of the machine learning model behavior to the client, reduce erroneous inclinations, and perform a corrective operation for correcting the AI computing system and/or computer model after having reduced erroneous inclinations, e.g., a retraining operation based on the accurate reference and monitored values as a basis for the machine learning processes. The improved computing tool of the illustrative embodiments is specifically concerned with solving the issues, arising from computer technology, in the erroneous inclinations present in artificial intelligence computing systems or computer models that are trained by a machine learning process based on such reference and monitored values. Thus, the present invention is specifically directed to an improved computer tool solving a problem arising from computer technology.

As noted above, the illustrative embodiments identify erroneous inclinations within a machine learning model and, more specifically, accurately identify reference and monitored values identifying which population group(s) for which the machine learning model is making erroneous inclinations. Erroneous inclinations, in general, occurs when the count of favorable results for one group, also referred to as a “monitored”, is much less than that of another, also referred to as a “reference,” or that the count of favorable results for one group (reference) is much greater than that of another group (monitored), where “much less” or “much greater” are determined relative to established fairness threshold values, e.g., at least 90% of the monitored group obtain favorable outcomes as compared to the reference group. The reference or monitored groupings, in the context of the illustrative embodiments, are general designations of a range of values or categories of one or more protected attributes where erroneous inclinations are believed to potentially be present, however without a specific source of the erroneous inclinations being identified. The sources of erroneous inclinations may be specific instances or sub-ranges of protected attributes within the reference or monitored groupings.

The reference and monitored groupings indicate areas where there is potential erroneous inclination, but again, since these reference and monitored values are provided by the user, the provided reference and monitored values themselves may be inaccurate. For example, in a home loan decision making computing system, it may be determined that the home loan decision making computing system seems to be favoring a certain group of applicants (recommending loan approval) over one or more other groups of applicants (recommending denying the loan), such that a reference grouping and monitored grouping may be defined. However, the values for each or the monitored value and the reference value may themselves be inaccurate and thus, inaccurately identifying erroneous inclinations to be present in the results generated by the home loan decision making computing system.

The illustrative embodiments provide mechanisms for identifying accurate reference and monitored values for which the machine learning model is making decisions. For example, in one embodiment, the machine learning model data quality improvement detection mechanism monitors a behavior of the machine learning model every predetermined time frame, such as 1, hour 2 hours, or the like. Looking at the monitored behavior data, the machine learning model data quality improvement detection mechanism computes the percentage of favorable outcomes, e.g. 90%, for the predefined reference group, e.g. 26-80, and the percentage of favorable outcomes, e.g. 75%, for the predefined monitored group, e.g. 18-25. The machine learning model data quality improvement detection mechanism then determines a fairness metric using a disparate impact ratio. Put another way, assume that RMAJis the rate of favorable outcomes for the reference group and RMINis the rate of favorable outcomes for the monitored group. The disparate impact is the ratio of RMINto RMAJ, or DI=RMIN/RMAJ, DI=75/90=83%. The machine learning model data quality improvement detection mechanism then compares the fairness metric (disparate impact DI) to a fairness threshold, e.g. 90%. If the fairness metric fails to meet the fairness threshold, the machine learning model data quality improvement detection mechanism splits the reference group into smaller buckets, e.g. split the reference group into buckets of size 2, such as 26-27, 28-29, 30-31, etc.

The machine learning model data quality improvement detection mechanism then moves the smaller bucket, e.g. 26-27, of the reference group bordering the monitored group to the monitored group and checks for fairness metric. The machine learning model data quality improvement detection mechanism then determines a fairness metric using a disparate impact ratio and compares the fairness metric (disparate impact DI) to a fairness threshold, e.g. 90%. If machine learning model data quality improvement detection mechanism determines that the newly calculated fairness metric fails to meet the fairness threshold, then the utilized smaller bucket, e.g. 26-27, is added to the monitored group and the machine learning model data quality improvement detection mechanism repeats the process analyzing and adding, if necessary, the smaller bucket bordering the monitored group until the fairness metric meets the fairness threshold. Once the fairness threshold is met, the last analyzed smaller bucket is added to the monitored group and the new ranges for the monitored group and reference group are output to the data quality improvement engine for reducing erroneous inclinations of the machine learning model. That is, the newly identified ranges for the monitored and reference groups may be utilized in retraining the machine learning model using data that accurately reflects the monitored and reference grouping that the client wants.

Even though the reference and monitored groupings are identified based on results of the machine learning model data quality improvement detection mechanism, it should be appreciated that not all entities within the reference or monitored groupings contribute to erroneous inclinations. It is important to be able to localize or pinpoint the particular sub-groups of entities which actually are a cause or source of erroneous inclinations in the reference and monitored groupings.

Therefore, in a second embodiment, the machine learning model data quality improvement detection mechanism monitors a behavior of the machine learning model every predetermined time frame, such as 1, hour 2 hours, or the like. Looking at the monitored behavior data, the machine learning model data quality improvement detection mechanism computes the percentage of favorable outcomes, e.g. 90%, for the predefined reference group, e.g. 26-80, and the percentage of favorable outcomes, e.g. 75%, for the predefined monitored group, e.g. 18-25. The machine learning model data quality improvement detection mechanism then determines a fairness metric using a disparate impact ratio. Put another way, assume that RMAJis the rate of favorable outcomes for the reference group and RMINis the rate of favorable outcomes for the monitored group. The disparate impact is the ratio of RMINto RMAJ, or DI=RMIN/RMAJ, DI=75/90=83%. The machine learning model data quality improvement detection mechanism then compares the fairness metric (disparate impact DI) to a fairness threshold, e.g. 90%. If the fairness metric fails to meet the fairness threshold, the machine learning model data quality improvement detection mechanism identifies the boundaries of the reference group. Continuing with the example, the boundaries of the reference group may be 26 and 80. The machine learning model data quality improvement detection mechanism then divides the reference group into buckets where the bucket size is 10% of the size of reference thus, the buckets would be: (80−26)/10=5.4 which would be rounded off to 5. So the first bucket that the machine learning model data quality improvement detection mechanism would consider would be 26-30. That is, the machine learning model data quality improvement detection mechanism considers that there is an additional monitored group of 26-30 and the reference group will be 31-80.

For the new monitored group of 26-30, the machine learning model data quality improvement detection mechanism uses data perturbation. Data perturbation is a data security technique that adds ‘noise’ to databases allowing individual record confidentiality. This technique allows users to ascertain key summary information about the data that is not distorted and does not lead to a security breach. Therefore, for all the records that the machine learning model data quality improvement detection mechanism received in the last predetermined time frame, if the data value is greater than 30, the machine learning model data quality improvement detection mechanism flips the data value to a value between 26-30 and sends the data value back to the machine learning model to understand how the machine learning model handles the perturbed record. For example, if a claim has a data value of 40, the machine learning model data quality improvement detection mechanism changes the data value to say 27 and keeps all the other features constant. The machine learning model data quality improvement detection mechanism sends the new (changed) record to the machine learning model to determine whether the machine learning model predicts that claim is fraudulent or not. Similarly, for a record where the data value is less than or equal to 30, e.g. data value of 27, the machine learning model data quality improvement detection mechanism changes the data value to say 40 and keeps all the other features constant. The machine learning model data quality improvement detection mechanism sends the new (changed) record to the machine learning model to determine whether the machine learning model predicts that claim is fraudulent or not.

The machine learning model data quality improvement detection mechanism then determines a fairness metric using a disparate impact ratio based on the original record data and the newly considered perturbed record data to determine whether the machine learning model is truly acting in an erroneously inclined manner in the group 26-30. If the newly determined fairness metric meets the fairness threshold, indicating that the machine learning model is not acting in an erroneously inclined manner, then the machine learning model data quality improvement detection mechanism splits the new monitored group of 26-30 into half and repeat the process, e.g., the machine learning model data quality improvement detection mechanism considers group of 26-28 as the new monitored group repeats the process. If, on the other hand, the newly determined fairness metric fails to meet the fairness threshold, indicating that the machine learning model is acting in an erroneously inclined manner, the machine learning model data quality improvement detection mechanism considers the next group of 31-35 as a new monitored group and repeats the process. The machine learning model data quality improvement detection mechanism may repeat the process for a predetermined number of iterations, e.g. 5 iterations, and, if at the end of the predetermined number of iterations, the machine learning model is consistently exhibiting erroneous inclinations for the new monitored group, then a new monitored group range of the original plus any new monitored group(s) and the modified reference group minus the new monitored group(s) is recommended to the user as a new monitored/reference group ranges.

Evaluating the fairness metric on the reference/monitored group as a whole provides an indication of whether erroneous inclinations exists or not based on the accepted ranges of fairness metrics indicating fairness or erroneous inclination. e.g., for a disparate impact fairness metric, which is the ratio of rate of favorable outcome for the monitored group to that of the reference group, it has been determined that an ideal value is 1.0, i.e. the rates are the same, however an AI computing system or computer model is considered to be fair and not erroneously inclined if this fairness metric is anywhere in the range from 0.8 to 1.0 (this range may be user specified or otherwise configurable in the inputs provided to the tool). Thus, if the disparate impact fairness metric has a value less than 1.0 this implies a higher benefit for the reference group, with a value less than 0.8 indicating erroneously inclined in favor of the reference group, and if it has a value greater than 1.0 this implies a higher benefit for the monitored group, i.e. an erroneously inclined in favor of the monitored group.

Evaluating the fairness metric of each sub-group relative the fairness metric of the group as a whole provides insights into which sub-group(s) are the source of any detected erroneous inclinations. The fairness metric for a sub-group is calculated with the sub-group standing in for the corresponding reference/monitored group in the particular fairness metric. For example, in a disparate impact fairness metric, where the rate of favorable outcome of the reference group as a whole would be used in the denominator, for a sub-group in the reference, only the rate of favorable outcomes within that sub-group would be used in the fairness metric for that sub-group while the monitored group ratio of favorable outcomes would remain the same as for the entire monitored group.

This same process may be performed for cases where the protected attribute is a categorical value rather than a numeric value or range of values, such as in the case of ethnicity, for example. In such a case the reference grouping may be a particular set of ethnicities and the monitored grouping may be a different set of ethnicities. For each, the groups are divided into individual sub-groups corresponding to each individual category within that group. Thus, if a reference group comprises group A for example, then two sub-groups, one for group A1 and one for group A2, would be generated. Similarly, if the monitored group comprises group B, group C, and group D, then 3 sub-groups may be generated, one for each of group B, group C, and group D. A similar evaluation may then be performed for the reference/monitored groups as a whole and then for each sub-group so as to determine the fairness metrics for each and then compare them to identify potential sources of improving data quality.

Thus, potential sources of improving data quality are specifically identified or localized within the reference and/or monitored groupings developed from the erroneous inclination detection tool results. These potential sources of improving data quality pinpoint the particular sub-groups within the reference and/or monitored groupings. The granularity of such groupings may be configured to any desirable range of values and/or categories.

Thus, the mechanisms of the illustrative embodiments provide an improved computing tool and computing tool operation that identifies reference and monitored values utilized by a machine learning model so as to provide an accurate picture of the machine learning model behavior to the client, reduce erroneous inclination, and perform a corrective operation for correcting the AI computing system and/or computer model after having reduced erroneous inclinations, e.g., a retraining operation based on the accurate reference and monitored values as a basis for the machine learning processes. The data quality improvement may be determined based on fairness metric evaluations and/or explanation based determinations. Based on the identified data quality improvement, improvement of data quality operations may be performed to remove the erroneous inclination based on the accurate reference and monitored values as a basis for the machine learning processes. In cases where an AI computing system or computer model has been trained using the original reference and monitored groupings, corrective operations may be performed to retrain the AI computing system or computer model based on the accurate reference and monitored groupings to thereby reduce any erroneous inclination present in the operation of the trained AI computing system or computer model.

As noted above, the present invention provides mechanisms in an improved computer tool and computer tool operation for identifying accurate reference and monitored values utilized by a machine learning model so as to provide an accurate picture of the machine learning model behavior to the client, reduce erroneous inclination, and perform a corrective operation for correcting the AI computing system and/or computer model after having reduced erroneous inclinations, e.g., a retraining operation based on the accurate reference and monitored values as a basis for the machine learning processes. The trained artificial intelligence based computing system or computer model may be implemented as part of or utilized by a cognitive computing system that employs the trained artificial intelligence (AI) based computing system or computer model to generate results upon which the cognitive computing system operates to generate cognitive computing responses to user requests, for example, e.g., answering natural language questions, performing image recognition, generating recommendations, decision support operations, or any other cognitive computing operation. The cognitive computing system may comprise any artificial intelligence based computing system that is trained through a machine learning process so as to generate results from given inputs, where the results have an acceptable level of error or loss after such training. For example, the cognitive computing system may be comprised of a single neural network, multiple neural networks, one or more rules based engines, a deep learning computing system such as the IBM Watson™ cognitive computing system, or the like. For purposes of illustration in the description of the illustrative embodiments hereafter, the cognitive computing system will be assumed to be the IBM Watson™ cognitive computing system, and in particular an implementation of the IBM Watson™ cognitive computing system in which one or more deep learning neural networks trained through a machine learning process, supervised or unsupervised, is implemented.

The AI computing system or computer model of the cognitive computing system is trained through a machine learning process that involves an iterative adjustment of operational parameters of the machine learning computer models employed by the cognitive computing system so as to minimize an error or loss in the outputs or results generated by the cognitive computing system. For example, in the case of a neural network, the weights of nodes in the neural network may be iteratively adjusted based on the input of training data and the comparison of outputs or results to expected outputs/results (ground truth) which indicates an error or loss. The iterative adjustment may be based on an identification of features that were most influential in the generation of the output such that the weights associated with nodes processing such features may be adjusted to minimize the influence of those features on the output and thus, reduce the loss or error in the output generated. This machine learning process is referred to as training the machine learning computer model or training the cognitive computing system herein.

Through the training of an AI computing system or computer model of the cognitive computing system, erroneous inclinations may be inadvertently introduced into the operation of the cognitive computing system due to such erroneous inclinations being present in training datasets. For example, in the case of certain erroneous inclinations, training datasets may associate one group over another, traditional or stereotypical associations of characteristics, objects, events, etc. which reflect an erroneous inclination (whether it be a positive or negative erroneous inclination), e.g., likes, dislikes, limitations, strengths, etc. For example, an erroneous inclination may be that group X prefers the color “pink” and group Y does not prefer the color “pink” or that group X likes to play with “dolls” and group Y does not like to play with dolls. Such erroneous inclinations may be present in the training datasets in various ways, e.g., a relative number of training data instances having correct results being “pink” or “dolls” for corresponding features of “group X” being substantially greater than other possible results. Erroneous inclinations may be associated with different types of protected attributes including gender, ethnicity, age, or any other protected attribute specific to the types of entities for which erroneous inclinations are being evaluated.

The problem with erroneous inclinations embedded into cognitive computing systems, or trained AI computing systems or computer models employed by these cognitive computing systems, is that the results generated by these systems/models may be incorrect. The reference of the time, the output of a trained cognitive computing system or trained computer model is processed through additional computer logic within a calling application. Depending on the calling application, various incorrect outcomes could result. For example, trained cognitive computing systems or trained computer models with erroneous inclinations “trained in” or embedded in the cognitive computing system and/or computer models could possibly cause erroneously inclined financial decisions, erroneously inclined decisions about the incarcerated, erroneously inclined decisions about educational needs and projects, etc. Practically any current system in use today that utilizes the operation of a trained AI computing system or computer model component has a possibility of erroneous inclinations being “trained in” and used indirectly to make decisions based on these erroneous inclinations. The entities using such erroneous inclination cognitive computing systems and/or computer models, e.g., companies, governmental agencies, or other individuals or organizations, may experience legal or public dissatisfaction issues.

The illustrative embodiments provide mechanisms for identifying reference and monitored values utilized by a machine learning model so as to provide an accurate picture of the machine learning model behavior to the client, reduce erroneous inclinations, and perform a corrective operation for correcting the AI computing system and/or computer model after having reduce erroneous inclinations, e.g., a retraining operation based on the accurate reference and monitored values as a basis for the machine learning processes. While numerical data value specific erroneous inclinations are used as a primary example throughout this description, it should be appreciated that the mechanisms of the illustrative embodiments may be implemented to identify any type of erroneous inclination that may be present in the operation of an AI computing system or computer model, such as erroneous inclinations for/against particular parties, organizations, objects, etc. for various reasons, e.g., erroneous inclinations toward/against a particular political party, a particular special interest group, etc. Moreover, the erroneous inclination that is identifiable may be either positive or negative erroneous inclination, as the mechanisms are configured to identify erroneous inclinations itself. Whether or not the erroneous inclination is “negative” or “positive” is a human judgement and is not relevant to the operation of the mechanisms of the illustrative embodiment.

The mechanisms of the illustrative embodiments may be configured to operate on a dataset associated with a detected erroneous inclination to identify accurate reference and monitored values utilized by a machine learning model so as to provide an accurate picture of the machine learning model behavior to the client. This detected erroneous inclination may be detected in the operation of an already trained AI computing system or computer model (assumed hereafter to be a computer model, such as a neural network, deep learning network, or the like, for ease of explanation) which may or may not have a configuration, due to training, which introduces erroneous inclinations into the results generated by the trained computer model. It should be appreciated that when reference is made to the trained computer model herein, such references may also be considered directed to a trained cognitive computing system in which such trained AI computing systems or computer models are implemented. That is, a trained cognitive computing system may use one or more trained computer models to perform cognitive computing operations, however the mechanisms of the claimed invention may be applied to a single trained computer model as well. Thus, the description of mechanisms of the illustrative embodiments with references to a trained computer models may also be applied to trained cognitive computing system as well.

As an overview, a cognitive computing system (or more simply a “cognitive system”) is a specialized computer system, or set of computer systems, configured with hardware and/or software logic (in combination with hardware logic upon which the software executes) to emulate human cognitive functions. These cognitive systems apply human-like characteristics to conveying and manipulating ideas which, when combined with the inherent strengths of digital computing, can solve problems with high accuracy and resilience on a large scale. A cognitive system performs one or more computer-implemented cognitive operations that approximate a human thought process, but within the limitations of a computer architecture, as well as enable people and machines to interact in a more natural manner so as to extend and magnify human expertise and cognition. A cognitive system comprises artificial intelligence logic, such as natural language processing (NLP) based logic, for example, and machine learning logic, which may be provided as specialized hardware, software executed on hardware, or any combination of specialized hardware and software executed on hardware.

The logic of the cognitive system implements the cognitive operation(s), examples of which include, but are not limited to, question answering, identification of related concepts within different portions of content in a corpus, intelligent search algorithms, such as Internet web page searches, for example, medical diagnostic and treatment recommendations, financial trend analysis, financial investment recommendations, credit scoring and credit/loan approval recommendations, and other types of recommendation generation, e.g., items of interest to a particular user, potential new contact recommendations, or the like. IBM Watson™ is an example of one such cognitive system which can process human readable language and identify inferences between text passages with human-like high accuracy at speeds far faster than human beings and on a larger scale. The IBM Watson® cognitive system has many different implementations in which the IBM Watson® cognitive system has been configured for different cognitive functions, e.g., IBM Chef Watson® (IBM Chef Watson and all IBM Chef Watson-based trademarks and logos are trademarks or registered trademarks of International Business Machines Corporation and/or its affiliates) generates recipes for users, IBM Watson Ads™ provides an artificial intelligence (AI) solution for advertising, and IBM Watson Health™ provides a number of different tools for implementing AI solutions to perform various patient health related cognitive computing functions, etc.

FIG. 1is an example block diagram illustrating the primary operational components of an improved computing tool that performs erroneous inclination identification, dataset data quality improvement, and/or corrective actions in accordance with one illustrative embodiment. As shown inFIG. 1, the primary operational components include a machine learning model data quality improvement detection mechanism100that operates to detect the presence of erroneous inclinations, via an accurate identification of a monitored group and a reference group, in the operation of a trained cognitive computing system102and/or trained computer machine learning model104and reports these accurate monitored/reference groups to an administrator computing device106in a monitored/reference groups notifications108. In some illustrative embodiments, the identification of the erroneous inclination, i.e. initial monitored and reference groupings, is also a basis upon which the machine learning model data quality improvement detection mechanism100performs a data quality improvement operation to address the erroneous inclination. Accurate identification of a monitored group and a reference group may be used as a basis for performing corrective operations with regard to the trained cognitive computing system102and/or trained computer machine learning model104, such as retraining the trained computer machine learning model104via a machine learning training engine110that uses the accurate identification of a monitored group and a reference group to perform the retraining.

The machine learning model data quality improvement detection engine100may include an erroneous inclination detection tool112, a reference/monitored group modification tool114, an accurate reference/monitored grouping reporting engine116, and a data quality improvement engine118. It should be appreciated that these are the primary operational elements of the machine learning model data quality improvement detection mechanism100and other elements may also be present for facilitating the operations of these primary operational elements. For example, various communication elements, control elements, and the like, which facilitate the interaction of the primary operational elements with each other and with components of other computing systems, may be provided, but are not depicted for ease of explanation of the improvements provided by the improved computing tool and improved computing operations of the illustrative embodiments.

The data quality improvement detection tool112provides the computer executed logic and data structures used to perform erroneous inclination detection in the operation of an AI computing system or computer model, such as trained cognitive computing system102and/or trained computer machine learning model104. The data quality improvement detection tool112only detects the presence of erroneous inclinations in the operation of the computing system/computer model. Examples of data quality improvement detection tools112which may be utilized to detect the presence of erroneous inclinations may include the data quality improvement detection tool such as that described in the co-pending U.S. patent application Ser. No. 16/589,314, the AI Fairness 360 tool available from IBM® Corporation, or any other erroneous inclination detection tool currently available or later developed.

Responsive to receiving a request to identify accurate reference/monitored groupings, the data quality improvement detection tool112operates on results generated by the trained cognitive computing system102and/or trained computer machine learning model104(which in some cases may be independent of the trained cognitive computing system102and not necessarily integrated into the computing system102) based on a processing of an input dataset. That is, the data quality improvement detection tool112monitors a behavior of the trained cognitive computing system102and/or trained computer machine learning model104every predetermined time frame, such as 1, hour 2 hours, or the like.

Utilizing a set of user-defined monitored and reference groupings120, in one embodiment, the reference/monitored group modification tool114looks at the monitored behavior data detected by the data quality improvement detection tool112and computes the percentage of favorable outcomes for the set of user-defined monitored and reference groupings120, e.g. 90%, for the user-defined reference group, e.g. 26-80, and the percentage of favorable outcomes, e.g. 75%, for the user defined monitored group, e.g. 18-25. A fairness metric erroneous inclination source identifier122of the reference/monitored group modification tool114then determines a fairness metric using a disparate impact ratio. Put another way, assuming that RMAJis the rate of favorable outcomes for the reference group and RMINis the rate of favorable outcomes for the monitored group, the fairness metric erroneous inclination source identifier122determines a disparate impact that is the ratio of RMINto RMAJ, or DI=RMIN/RMAJ, DI=75/90=83%. The fairness metric erroneous inclination source identifier122then compares the fairness metric (disparate impact DI) to a fairness threshold, e.g. 90%. If the fairness metric erroneous inclination source identifier122determines that the fairness metric fails to meet the fairness threshold, the reference/monitored group modification tool114splits the reference group into smaller buckets, e.g. split the reference group into buckets of size 2, such as 26-27, 28-29, 30-31, etc.

The reference/monitored group modification tool114then moves the smaller bucket, e.g. 26-27, of the reference group bordering the monitored group to the monitored group and then rechecks for fairness metric. That is, the fairness metric erroneous inclination source identifier122determines a fairness metric using a disparate impact ratio and compares the fairness metric (disparate impact DI) to the fairness threshold, e.g. 90%. If the fairness metric erroneous inclination source identifier122determines that the newly calculated fairness metric fails to meet the fairness threshold, then the reference/monitored group modification tool114adds the utilized smaller bucket, e.g. 26-27, to the monitored group and repeats the process analyzing and adding, if necessary, each smaller bucket bordering the monitored group until the fairness metric meets the fairness threshold. Once the reference/monitored group modification tool114determines that the fairness threshold is met, the reference/monitored group modification tool114adds the last analyzed smaller bucket to the monitored group. Having identified a new monitored group and new reference group, the accurate identification of the monitored group and the reference group may be transmitted as a monitored/reference groups notifications108by the accurate reference/monitored grouping reporting engine116to an authorized computing system, such as administrator computing device106. Moreover, such notifications may be logged or otherwise stored for later retrieval and use in evaluating the operation of the trained cognitive computing system102and/or trained computer machine learning model104. In response to receiving the monitored/reference groups notifications108, a user of the administrator computing device106may provide a request to the machine learning model data quality improvement detection mechanism100to reduce erroneous inclinations of the trained cognitive computing system102and/or trained computer machine learning model104, e.g., re-training of the trained computer machine learning model104. In some illustrative embodiments, data quality improvement actions may be requested with an original request to identify accurate reference/monitored groupings124or may be performed automatically in response to identifying the accurate reference/monitored groupings.

The data quality improvement engine118is provided by the machine learning model data quality improvement detection mechanism100to address the newly identified accurate reference/monitored groupings identified in the monitored/reference groups notifications108. That is, utilizing the newly identified ranges for the monitored and reference groups, data quality improvement engine118may initiate a retraining of the trained cognitive computing system102and/or trained computer machine learning model104via the machine learning training engine110utilizing data that more accurate reflects the set of user-defined monitored and reference groupings120or another set of monitored and reference groupings based on the needs of the client.

In a second embodiment, responsive to receiving a request to identify accurate reference/monitored groupings, the erroneous inclination detection tool112again operates on results generated by the trained cognitive computing system102and/or trained computer machine learning model104(which in some cases may be independent of the trained cognitive computing system102and not necessarily integrated into the computing system102) based on a processing of an input dataset. That is, the erroneous inclination detection tool112monitors a behavior of the trained cognitive computing system102and/or trained computer machine learning model104every predetermined time frame, such as 1, hour 2 hours, or the like.

Utilizing a set of user-defined monitored and reference groupings120, the reference/monitored group modification tool114looks at the monitored behavior data detected by the erroneous inclination detection tool112and computes the percentage of favorable outcomes for the set of user-defined monitored and reference groupings120, e.g. 90%, for the user-defined reference group, e.g. 26-80, and the percentage of favorable outcomes, e.g. 75%, for the user defined monitored group, e.g. 18-25. A fairness metric erroneous inclination source identifier122of the reference/monitored group modification tool114then determines a fairness metric using a disparate impact ratio. Put another way, assuming that RMAJis the rate of favorable outcomes for the reference group and RMINis the rate of favorable outcomes for the monitored group, the fairness metric erroneous inclination source identifier122determines a disparate impact that is the ratio of RMINto RMAJ, or DI=RMIN/RMAJ, DI=75/90=83%. The fairness metric erroneous inclination source identifier122then compares the fairness metric (disparate impact DI) to a fairness threshold, e.g. 90%.

If the fairness metric fails to meet the fairness threshold, the reference/monitored group modification tool114identifies the boundaries of the reference group. Continuing with the example, the boundaries of the reference group would be 26 and 80. The reference/monitored group modification tool114then divides the reference group into buckets where the bucket size is 10% of the size of reference thus, the buckets would be: (80−26)/10=5.4 which would be rounded off to 5. So the first bucket that reference/monitored group modification tool114would consider would be 26-30. That is, the reference/monitored group modification tool114considers that there is an additional monitored group of 26-30 and the reference group will be 31-80.

For the new monitored group of 26-30, reference/monitored group modification tool114uses data perturbation. Data perturbation is a data security technique that adds ‘noise’ to databases allowing individual record confidentiality. This technique allows users to ascertain key summary information about the data that is not distorted and does not lead to a security breach. Therefore, for all the records that the machine learning model data quality improvement detection engine100received in the last predetermined time frame, if the data value is greater than 30, reference/monitored group modification tool114flips the data value to a value between 26-30 and send it back to the trained cognitive computing system102and/or trained computer machine learning model104to understand how the trained cognitive computing system102and/or trained computer machine learning model104handles the perturbed record. For example, if a claim is made with a data value of 40, the reference/monitored group modification tool114changes the data value to say 27 and keeps all the other features constant. The reference/monitored group modification tool114sends the new (changed) record to the trained cognitive computing system102and/or trained computer machine learning model104to determine whether the trained cognitive computing system102and/or trained computer machine learning model104predicts that claim is fraudulent or not. Similarly, for a record where the data value is less than or equal to 30, e.g. data value of 27, the reference/monitored group modification tool114changes the data value to say 40 and keeps all the other features constant. The reference/monitored group modification tool114sends the new (changed) record to the trained cognitive computing system102and/or trained computer machine learning model104to determine whether the trained cognitive computing system102and/or trained computer machine learning model104predicts that claim is fraudulent or not.

The fairness metric erroneous inclination source identifier122then determines a fairness metric using a disparate impact ratio based on the original record data and the newly considered perturbed record data to determine whether the trained cognitive computing system102and/or trained computer machine learning model104is truly acting in an erroneous manner in the group of 26-30. If the newly determined fairness metric meets the fairness threshold, indicating that the trained cognitive computing system102and/or trained computer machine learning model104is not acting in an erroneous manner, then the reference/monitored group modification tool114splits the new monitored group of 26-30 into half and repeat the process, e.g., the reference/monitored group modification tool114considers the group of 26-28 as the new monitored group repeats the process. If, on the other hand, the newly determined fairness metric fails to meet the fairness threshold, indicating that the trained cognitive computing system102and/or trained computer machine learning model104is acting in an erroneous manner, the reference/monitored group modification tool114considers the next group of 31-35 as a new monitored group and repeats the process. The reference/monitored group modification tool114may repeat the process for a predetermined number of iterations, e.g. 5 iterations, and, if at the end of the predetermined number of iterations, the trained cognitive computing system102and/or trained computer machine learning model104is consistently exhibiting erroneous inclinations for the new monitored group, then a new monitored group range of the original monitored group plus any new monitored group(s) and the modified reference group minus the new monitored group(s) may be transmitted as a monitored/reference groups notifications108by the accurate reference/monitored grouping reporting engine116to an authorized computing system, such as administrator computing device106. Moreover, such notifications may be logged or otherwise stored for later retrieval and use in evaluating the operation of the trained cognitive computing system102and/or trained computer machine learning model104. In response to receiving the monitored/reference groups notifications108, a user of the administrator computing device106may provide a request to the machine learning model data quality improvement detection mechanism100to reduce erroneous inclinations of the trained cognitive computing system102and/or trained computer machine learning model104, e.g., re-training of the trained computer machine learning model104. In some illustrative embodiments, data quality improvement actions may be requested with an original request to identify accurate reference/monitored groupings124or may be performed automatically in response to identifying the new monitored group range.

The data quality improvement engine118is provided by the machine learning model data quality improvement detection mechanism100to address the newly identified reference/monitored groupings identified in the monitored/reference groups notifications108. That is, utilizing the newly identified ranges for the monitored and reference groups, data quality improvement engine118may initiate a retraining of the trained cognitive computing system102and/or trained computer machine learning model104via the machine learning training engine110utilizing data that more accurate reflects the set of user-defined monitored and reference groupings120or another set of monitored and reference groupings based on the needs of the client.

Thus, again, the mechanisms of the illustrative embodiments provide an improved computing tool and computing tool operation that identifies reference and monitored values utilized by a machine learning model so as to provide an accurate picture of the machine learning model behavior to the client, reduce erroneous inclinations, and perform a corrective operation for correcting the AI computing system and/or computer model after having reduce erroneous inclinations, e.g., a retraining operation based on the accurate reference and monitored values as a basis for the machine learning processes. The erroneous inclination may be determined based on fairness metric evaluations and/or explanation based determinations. Based on the identified erroneous inclination, data quality improvement operations may be performed to remove the erroneous inclination based on the accurate reference and monitored values as a basis for the machine learning processes. In cases where an AI computing system or computer model has been trained using the original reference and monitored groupings, corrective operations may be performed to retrain the AI computing system or computer model based on the actual reference and monitored groupings to thereby reduce any erroneous inclinations present in the operation of the trained AI computing system or computer model.

As the present invention is specifically directed to computer technology and specifically to an improved computing tool and computing tool operations for identifying sources of erroneous inclinations in datasets used by artificial intelligence computing systems and/or computing models, it is clear that the present invention may be implemented in various computing environments and with various types of data processing systems.FIG. 2is an example block diagram of an example distributed data processing system environment in which aspects of the illustrative embodiments may be implemented.

As shown inFIG. 2, a cognitive computing system200, which may employ one or more trained computer models, such as neural networks, deep learning networks, ensemble learning systems, and the like, is provided on one or more server computing devices204A-D comprising one or more processors and one or more memories, and potentially any other computing device elements generally known in the art including buses, storage devices, communication interfaces, and the like, connected to the computer network202. For purposes of illustration only,FIG. 2depicts the cognitive system200being implemented on computing device204A only, but as noted above the cognitive system200may be distributed across multiple computing devices, such as a plurality of computing devices204A-D.

The network202includes multiple computing devices204A-D, which may operate as server computing devices, and210-212which may operate as client computing devices, e.g., an administrator computing system such as106inFIG. 1, in communication with each other and with other devices or components via one or more wired and/or wireless data communication links, where each communication link comprises one or more of wires, routers, switches, transmitters, receivers, or the like. In some illustrative embodiments, the cognitive system200and network202enables question processing and answer generation (QA) functionality for one or more cognitive system users via their respective computing devices210-212. In other embodiments, the cognitive system200and network202may provide other types of cognitive operations including, but not limited to, request processing and cognitive response generation which may take many different forms depending upon the desired implementation, e.g., cognitive information retrieval, training/instruction of users, cognitive evaluation of data, recommendation generation, data pattern analysis, or the like. Other embodiments of the cognitive system200may be used with components, systems, sub-systems, and/or devices other than those that are depicted herein.

The cognitive computing system200and/or computing models employed by the cognitive computing system200, may be trained on and/or operate on one or more input datasets provided by one or more of the computing devices204A-D,210-212or otherwise provided via a network attached storage206or other source of data accessible via the network202. For example, a user of a computing device210may provide a computer model and corresponding training dataset to a computing model training and hosting service provided via server204A which then trains the computing model based on the training dataset and deploys the trained computer model as part of the cognitive computing system200for use. Such an arrangement may be provided via a cloud based cognitive computing service, for example.

As shown inFIG. 2, in accordance with one illustrative embodiment, the machine learning model data quality improvement detection mechanism100ofFIG. 1may be implemented on one or more of the server computing devices204A-D. The machine learning model data quality improvement detection mechanism100operates as previously described above to detect erroneous inclinations in the operation of the cognitive computing system, and then to identify the sources of the erroneous inclinations in the input dataset, as well as reducing erroneous inclinations and retraining of computer models and/or the cognitive computing system200. WhileFIG. 2shows the machine learning model data quality improvement detection mechanism100as being implemented on the same computing device204A as the cognitive computing system200, this is not a requirement and in fact they may be implemented on separate computing devices with accessibility by the machine learning model data quality improvement detection mechanism100to the cognitive computing system200and/or computing models employed by the cognitive computing system, as well as the input dataset being provided via the network202.

FIG. 3is an example block diagram of an example computing device in which aspects of the illustrative embodiments may be implemented. As shown inFIG. 3, in the depicted distributed data processing system, data processing system300is an example of a computer, such as server204A or client210inFIG. 2, in which computer usable code or instructions implementing the processes for illustrative embodiments of the present invention are located. In one illustrative embodiment,FIG. 3represents a server computing device, such as a server204A, which implements a cognitive system200and/or the machine learning model data quality improvement detection mechanism100.FIG. 3is just an example of one type of computing system in which the cognitive computing system200and/or the machine learning model data quality improvement detection mechanism100may be implemented and other architectures may also be utilized.

In the depicted example, data processing system300employs a hub architecture including north bridge and memory controller hub (NB/MCH)302and south bridge and input/output (I/O) controller hub (SB/ICH)304. Processing unit306, main memory308, and graphics processor310are connected to NB/MCH302. Graphics processor310is connected to NB/MCH302through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter312connects to SB/ICH304. Audio adapter316, keyboard and mouse adapter320, modem322, read only memory (ROM)324, hard disk drive (HDD)326, CD-ROM drive330, universal serial bus (USB) ports and other communication ports332, and PCI/PCIe devices334connect to SB/ICH304through bus338and bus340. PCI/PCIe devices may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. ROM324may be, for example, a flash basic input/output system (BIOS).

An operating system runs on processing unit306. The operating system coordinates and provides control of various components within the data processing system300inFIG. 3. As a client, the operating system is a commercially available operating system such as Microsoft® Windows 10®. An object-oriented programming system, such as the Java™ programming system, may run in conjunction with the operating system and provides calls to the operating system from Java™ programs or applications executing on data processing system300.

As a server, data processing system300may be, for example, an IBM® eServer™ System P® computer system, running the Advanced Interactive Executive (AIX®) operating system or the LINUX® operating system. Data processing system300may be a symmetric multiprocessor (SMP) system including a plurality of processors in processing unit306. Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as HDD326, and are loaded into main memory308for execution by processing unit306. The processes for illustrative embodiments of the present invention are performed by processing unit306using computer usable program code, which is located in a memory such as, for example, main memory308, ROM324, or in one or more peripheral devices326and330, for example.

A bus system, such as bus338or bus340as shown inFIG. 3, is comprised of one or more buses. Of course, the bus system may be implemented using any type of communication fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit, such as modem322or network adapter312ofFIG. 3, includes one or more devices used to transmit and receive data. A memory may be, for example, main memory308, ROM324, or a cache such as found in NB/MCH302inFIG. 3.

FIG. 4is a flowchart outlining a first example of identifying accurate monitored and reference groups exhibited by a machine learning model and reducing erroneous inclinations of the machine learning model based on the identified accurate monitored and reference groups in accordance with one illustrative embodiment. As shown inFIG. 4, the operation starts by the machine learning model data quality improvement detection engine receiving a request to identify accurate reference/monitored groupings (step402) which includes a set of user-defined monitored and reference groupings. The machine learning model data quality improvement detection engine then monitors a behavior of a trained cognitive computing system and/or trained computer machine learning model every predetermined time frame, such as 1, hour 2 hours, or the like (step404).

Utilizing the identified set of user-defined monitored and reference groupings, the machine learning model data quality improvement detection engine looks at the monitored behavior data (step406) and computes the percentage of favorable outcomes for the set of user-defined monitored and reference groupings (step408). The machine learning model data quality improvement detection engine determines a fairness metric using a disparate impact ratio (step410) and compares the fairness metric (disparate impact DI) to a fairness threshold (step412). If at step412the machine learning model data quality improvement detection engine determines that the fairness metric meets the fairness threshold, the operation terminates. If at step412the machine learning model data quality improvement detection engine determines that the fairness metric fails to meet the fairness threshold, the machine learning model data quality improvement detection engine splits the reference group into smaller buckets (step414).

The machine learning model data quality improvement detection engine then moves the smaller bucket of the reference group bordering the monitored group to the monitored group (step416). The machine learning model data quality improvement detection engine determines a new fairness metric using a disparate impact ratio (step418) and compares the fairness metric (disparate impact DI) to the fairness threshold (step420). If at step420the machine learning model data quality improvement detection engine determines that the newly calculated fairness metric fails to meet the fairness threshold, then the machine learning model data quality improvement detection engine adds the utilized smaller bucket to the monitored group (step422) with the process returning to step416to process the next smaller bucket. If at step420the machine learning model data quality improvement detection engine determines that the fairness threshold is met, the machine learning model data quality improvement detection engine adds the last analyzed smaller bucket to the monitored group (step424), thereby identifying a new monitored group and new reference group.

Having identified the new monitored group and new reference group, the machine learning model data quality improvement detection engine transmits the accurate identification of the monitored group and the reference group to an authorized computing system (step426). In response to receiving the accurate identification of the monitored group and the reference group, the machine learning model data quality improvement detection engine reduces erroneous inclinations of the trained cognitive computing system and/or trained computer machine learning model to address the newly identified the accurate identification of the monitored group and the reference group (step428). That is, utilizing the newly identified ranges for the monitored and reference groups, the machine learning model data quality improvement detection engine may initiate a retraining of the trained cognitive computing system and/or the trained computer machine learning model via a machine learning training engine utilizing data that more accurate reflects the set of user-defined monitored and reference groupings or another set of monitored and reference groupings based on the needs of the client. The operation terminates thereafter.

FIG. 5is a flowchart outlining a second example of identifying accurate monitored and reference groups exhibited by a machine learning model and reducing erroneous inclinations of the machine learning model based on the identified accurate monitored and reference groups in accordance with one illustrative embodiment. As shown inFIG. 5, the operation starts by the machine learning model data quality improvement detection engine receiving a request to identify accurate reference/monitored groupings (step502) which includes a set of user-defined monitored and reference groupings. The machine learning model data quality improvement detection engine then monitors a behavior of a trained cognitive computing system and/or trained computer machine learning model every predetermined time frame, such as 1, hour 2 hours, or the like (step504).

Utilizing the identified set of user-defined monitored and reference groupings, the machine learning model data quality improvement detection engine looks at the monitored behavior data (step506) and computes the percentage of favorable outcomes for the set of user-defined monitored and reference groupings (step508). The machine learning model data quality improvement detection engine determines a fairness metric using a disparate impact ratio (step510) and compares the fairness metric (disparate impact DI) to a fairness threshold (step512). If at step512the machine learning model data quality improvement detection engine determines that the fairness metric meets the fairness threshold, the operation terminates. If at step512the machine learning model data quality improvement detection engine determines that the fairness metric fails to meet the fairness threshold, the machine learning model data quality improvement detection engine identifies the boundaries of the reference group (step514). Continuing with the example, the boundaries of the reference group would be 26 and 80. The machine learning model data quality improvement detection engine then divides the reference group into buckets (step516) where the bucket size is for example, 10% of the size of reference thus, the buckets would be: (80−26)/10=5.4 which would be rounded off to 5. So the first bucket that the machine learning model data quality improvement detection engine would consider would be 26-30. That is, the machine learning model data quality improvement detection engine considers that there is an additional monitored group of 26-30 and the reference group will be 31-80.

For the new monitored group of 26-30, the machine learning model data quality improvement detection engine uses data perturbation. Data perturbation is a data security technique that adds ‘noise’ to databases allowing individual record confidentiality. This technique allows users to ascertain key summary information about the data that is not distorted and does not lead to a security breach. Therefore, for all the records received in the last predetermined time frame, if the data value is greater than the new monitored group, the machine learning model data quality improvement detection engine changes the data value to a value in the new monitored group and keeps all the other features constant (step518). Similarly, for a record where the data value is less than or equal to the maximum data value in the new monitored group, the machine learning model data quality improvement detection engine changes the data value to greater than the maximum data value in the new monitored group and keeps all the other features constant (step520). The machine learning model data quality improvement detection engine sends the new (changed) records to the trained cognitive computing system and/or trained computer machine learning model to determine how the trained cognitive computing system and/or trained computer machine learning model handles the changed records (step522).

The machine learning model data quality improvement detection engine then determines a fairness metric using a disparate impact ratio based on the original record data and the newly considered perturbed record data (step524). The machine learning model data quality improvement detection engine determines whether a predetermined number of iterations have been met (step526). If at step526the predetermined number of iterations has not been met, then the machine learning model data quality improvement detection engine compares the fairness metric (disparate impact DI) to a fairness threshold to determine whether the trained cognitive computing system and/or trained computer machine learning model is truly acting in an erroneous manner (step528). If at step528the machine learning model data quality improvement detection engine determines that the fairness metric meets the fairness threshold indicating that the trained cognitive computing system and/or trained computer machine learning model is not acting in an erroneous manner, then the machine learning model data quality improvement detection engine splits the new monitored group into half (step530) with the operation returning to step518thereafter, e.g., the machine learning model data quality improvement detection engine considers group of 26-28 as the new monitored group. If, on the other hand, at step528the newly determined fairness metric fails to meet the fairness threshold, indicating that the trained cognitive computing system and/or trained computer machine learning model is acting in an erroneous manner, the machine learning model data quality improvement detection engine considers the next group, e.g. 31-35, as a new monitored group (step532), with the operation returning to step518thereafter.

If at step526the predetermined number of iterations has not been met, then the machine learning model data quality improvement detection engine, then the machine learning model data quality improvement detection engine transmits a new monitored group range of the original monitored group plus any new monitored group(s) and the modified reference group minus the new monitored group(s) may be transmitted to an authorized computing system (step534). In response to receiving the accurate identification of the monitored group and the reference group, the machine learning model data quality improvement detection reduces erroneous inclination of the trained cognitive computing system and/or trained computer machine learning model to address the newly identified the accurate identification of the monitored group and the reference group (step536). That is, utilizing the newly identified ranges for the monitored and reference groups, the machine learning model data quality improvement detection engine may initiate a retraining of the trained cognitive computing system and/or the trained computer machine learning model via a machine learning training engine utilizing data that more accurate reflects the set of user-defined monitored and reference groupings or another set of monitored and reference groupings based on the needs of the client. The operation terminates thereafter.

It should be appreciated that while the above illustrative embodiments are described with regard to a cognitive computing system implementing or employing a question answering system and pipeline in which one or more computer models are utilized, the present invention is not limited to such. This is only one possible implementation of the mechanisms of the illustrative embodiment. The mechanisms of the illustrative embodiments may be utilized with any trained cognitive computing system and/or trained computer model in which the training may be erroneously inclined due to the training process and/or the data upon which the training is performed, or due to the corpus of data used by the trained cognitive computing system and/or trained computer model to perform its cognitive computing operations. For example, in some illustrative embodiments, the cognitive computing system and/or computer model may run analysis of unstructured text in a batch manner, not in a question/answer form, for example.