Information processing apparatus, information processing method, and program

Patent metadata

Field	Value
Publication number	US-2024070486-A1
Application number	US-202118260040-A
Country	US
Kind code	A1
Filing date	Dec 23, 2021
Priority date	Jan 8, 2021
Publication date	Feb 29, 2024
Grant date	—

Abstract

An information processing apparatus (100) includes a control unit (130). The control unit (130) selects an input variable that affects a prediction result as a first explanatory variable based on a causal model regarding a causal relationship between a plurality of input variables and the prediction result in a prediction model using the machine learning. The control unit (130) outputs information on the selected first explanatory variable.

First claim

1 . An information processing apparatus comprising a control unit
selecting, as a first explanatory variable, an input variable that affects a prediction result based on a causal model related to a causal relationship between a plurality of input variables and the prediction result in a prediction model using machine learning, and outputting information on the selected first explanatory variable.

2 . The information processing apparatus according to claim 1 , wherein
the control unit selects the first explanatory variable as a reason for the prediction result from among the plurality of input variables based on information as to whether the input variable and the prediction result are pseudo correlations in the prediction model generated by using machine learning, and outputs the information on the selected first explanatory variable.

3 . The information processing apparatus according to claim 2 , wherein the control unit selects the input variable that is not in a pseudo correlation relationship with the prediction result as the first explanatory variable.

4 . The information processing apparatus according to claim 2 , wherein the control unit selects the input variable that is not conditionally independent of the prediction result as the first explanatory variable.

5 . The information processing apparatus according to claim 2 , wherein the control unit outputs strength information indicating strength of a relationship between the first explanatory variable selected as the reason and the prediction result.

6 . The information processing apparatus according to claim 2 , wherein the control unit selects a combination of at least two of the input variables as the reason for the prediction result.

7 . The information processing apparatus according to claim 6 , wherein the control unit outputs strength information indicating strength of a relationship between the at least two input variables included in the combination and the prediction result in association with information regarding the combination.

8 . The information processing apparatus according to claim 5 , wherein
the control unit determines an order or a color on a display screen corresponding to the first explanatory variable based on the strength information, and outputs the display screen.

9 . The information processing apparatus according to claim 6 , wherein
the control unit outputs an interface for determining the combination of the input variables, and determines a combination of the input variables based on an operation corresponding to the interface.

10 . The information processing apparatus according to claim 2 , wherein the control unit estimates a causal graph with an output variable indicating the prediction result as an objective variable for the plurality of the input variables, and selects the first explanatory variable as the reason from the input variables having a direct causal relationship with the objective variable.

11 . The information processing apparatus according to claim 2 , wherein for the plurality of input variables, the control unit estimates a causal graph regarding a nearest node using the nearest node included in a hidden layer closest to the prediction model as an objective variable, and selects the first explanatory variable as the reason from the input variables having a direct causal relationship with the objective variable.

12 . The information processing apparatus according to claim 11 , wherein the control unit selects the first explanatory variable serving as the positive reason on a basis of the causal graph related to the nearest node having a positive weight among the nearest nodes, and selects the first explanatory variable serving as the negative reason on a basis of the causal graph related to the nearest node having a negative weight among the nearest nodes.

13 . The information processing apparatus according to claim 2 , wherein the control unit calculates an intervention effect in a case of intervening in the first explanatory variable selected as the reason.

14 . The information processing apparatus according to
the input variable includes information acquired by a sensor.

15 . The information processing apparatus according to claim 14 , wherein
the input variable includes information on an operating environment or an operating state of a device acquired by a sensor.

16 . The information processing apparatus according to claim 15 , wherein
the input variable includes information regarding temperature, humidity, voltage, current, electric power, or vibration acquired by a sensor, and the control unit selects at least one of the information regarding temperature, humidity, voltage, current, electric power, or vibration acquired by the sensor as the first explanatory variable.

17 . The information processing apparatus according to claim 1 , wherein
the input variable includes information about an age or a history of a person.

18 . The information processing apparatus according to claim 13 , wherein
the control unit acquires a selection operation for the output first explanatory variable, and calculates an intervention effect for the first explanatory variable selected by the selection operation.

19 . The information processing apparatus according to
the control unit selects the input variable that does not affect the prediction result as a second explanatory variable based on the causal model, and outputs information on the second explanatory variable while distinguishing the information on the second explanatory variable from the information on the first explanatory variable.

20 . The information processing apparatus according to claim 19 , wherein
the control unit selects the second explanatory variable as a reason for the prediction result from among the plurality of input variables based on information as to whether the input variable and the prediction result are pseudo correlations in the prediction model generated by using machine learning, and outputs information on the selected second explanatory variable while distinguishing the information on the second explanatory variable from the information on the first explanatory variable.

21 . The information processing apparatus according to claim 20 , wherein the control unit selects the input variable having a pseudo correlation with the prediction result or the input variable that becomes conditionally independent as the second explanatory variable.

22 . An information processing method comprising:
selecting, as a first explanatory variable, an input variable that affects a prediction result based on a causal model related to a causal relationship between a plurality of input variables and the prediction result in a prediction model using machine learning; and outputting information on the selected first explanatory variable.

23 . A program for causing a computer:
to select, as a first explanatory variable, an input variable that affects a prediction result based on a causal model related to a causal relationship between a plurality of input variables and the prediction result in a prediction model using machine learning; and to output information on the selected first explanatory variable.

Assignees

• Sony Group Corp

Inventors

• Isozaki Takashi
• Yamamoto Masahiro
• Noda Atsushi

Classifications

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N5/045 Primary

  Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N3/0499

  Feedforward networks · CPC title