Robustness score for an opaque model
A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing a robustness assessment operation, the robustness assessment operation assessing robustness of the cognitive computing function, the robustness assessment operation generating a robustness score representing robustness of the cognitive computing function; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.
1. A computer-implementable method for performing a counterfactual generation operation, the method comprising:
receiving a subject data point;
classifying the data point via a trained classifier, the classifying providing a classified data point;
identifying a counterfactual using the classified data point, the counterfactual comprising another datapoint, the another data point being close to the subject data point, the another data point resulting in production of a different outcome when provided to a model when compared to an outcome resulting from the subject data point being provided to the model; and,
providing the counterfactual to a destination.
2. The method of claim 1 , wherein:
the identifying comprises searching a plurality of datapoints to identify the datapoint closest to the subject datapoint.
3. The method of claim 1 , wherein:
the identifying uses a genetic algorithm.
4. The method of claim 3 , wherein:
the genetic algorithm comprises an evolutionary algorithm.
5. The method of claim 3 , wherein:
the genetic algorithm generates a set of random data points, the trained classifier providing classification for the set of random data points, a classification for at least some of the random data points differing from the classification of the subject data point.
6. The method of claim 5 , further comprising:
iteratively revising the set of random data points to provide revised data points, the revised data points being closer to the subject data point than the set of random data points while maintaining classifications that differ from the classification of the subject data point.
7. A system comprising:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:
receiving a subject data point;
classifying the data point via a trained classifier, the classifying providing a classified data point;
identifying a counterfactual using the classified data point, the counterfactual comprising another datapoint, the another data point being close to the subject data point, the another data point resulting in production of a different outcome when provided to a model when compared to an outcome resulting from the subject data point being provided to the model; and,
providing the counterfactual to a destination.
8. The system of claim 7 , wherein:
the identifying comprises searching a plurality of datapoints to identify the datapoint closest to the subject datapoint.
9. The system of claim 7 , wherein:
the identifying uses a genetic algorithm.
10. The system of claim 9 , wherein:
the genetic algorithm comprises an evolutionary algorithm.
11. The system of claim 9 , wherein:
the genetic algorithm generates a set of random data points, the trained classifier providing classification for the set of random data points, a classification for at least some of the random data points differing from the classification of the subject data point.
12. The system of claim 11 , wherein the instructions executable by the processor are further configured for:
iteratively revising the set of random data points to provide revised data points, the revised data points being closer to the subject data point than the set of random data points while maintaining classifications that differ from the classification of the subject data point.
13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
receiving a subject data point;
classifying the data point via a trained classifier, the classifying providing a classified data point;
identifying a counterfactual using the classified data point, the counterfactual comprising another datapoint, the another data point being close to the subject data point, the another data point resulting in production of a different outcome when provided to a model when compared to an outcome resulting from the subject data point being provided to the model; and,
providing the counterfactual to a destination.
14. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the identifying comprises searching a plurality of datapoints to identify the datapoint closest to the subject datapoint.
15. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the identifying uses a genetic algorithm.
16. The non-transitory, computer-readable storage medium of claim 15 , wherein:
the genetic algorithm comprises an evolutionary algorithm.
17. The non-transitory, computer-readable storage medium of claim 15 , wherein:
the genetic algorithm generates a set of random data points, the trained classifier providing classification for the set of random data points, a classification for at least some of the random data points differing from the classification of the subject data point.
18. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are further configured for:
iteratively revising the set of random data points to provide revised data points, the revised data points being closer to the subject data point than the set of random data points while maintaining classifications that differ from the classification of the subject data point.
19. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the computer executable instructions are deployable to a client system from a server system at a remote location.
20. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the computer executable instructions are provided by a service provider to a user on an on-demand basis.