Business process search device, business process search method, and business process search program

Abstract

To construct an operation process using AI after evaluation of risk when an inference error occurs in AI.SOLUTION: For each path to a conclusion that can be taken by an operation process candidate, a risk evaluation unit 20 calculates a risk score on the basis of a probability of occurrence of the path and a disadvantage score calculated based on an evaluation value of a negative effect among effects that occur in the path, and calculates, as the risk score of the operation process candidate, the sum of risk scores calculated for a plurality of paths that can be taken by the operation process candidate. A display unit 12 displays, to a user, process flows of operation process candidates and the risk score of the operation process candidate calculated by the risk evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a business process search device, a business process search method, and a business process search program. [Background technology]

[0002] Patent Document 1 discloses a business process evaluation method that monitors the performance of business processes, and when a performance decline is observed, identifies whether the cause is external or internal, and extracts performance declines caused by internal factors as targets for improvement.

[0003] In recent years, there has been a trend towards introducing AI (artificial intelligence) into business processes, which can dramatically improve the performance of business processes. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2018-5550 A Summary of the Invention [Problem to be solved by the invention]

[0005] While introducing AI into business processes can improve their performance, depending on the content of the business process, the results of the AI's inferences may cause psychological, economic, or physical harm to organizations or individuals. Take the example of applying AI to task assignment. For example, suppose a business process is being considered in which AI assesses a candidate's skills based on a promotional video submitted by the candidate and assigns tasks to candidates determined to have the appropriate skills. If the AI's inference of candidate skills is incorrect, it could result in the assignment of a candidate with insufficient skill levels, or the under-assignment of a candidate with sufficient skill levels. In this case, even if the performance of task assignment tasks improved with the introduction of AI, it is difficult to say that the original purpose of task assignment tasks has been achieved.

[0006] Therefore, when introducing AI into a business process, it is necessary not only to judge its suitability based on performance indicators as shown in Patent Document 1, but also to evaluate the risk of AI inference errors before building the business process. Furthermore, depending on the content of the business process, risk assessment from the perspective of AI ethics is also important. For example, in the above example, even if the AI ​​correctly judges the skill level, if the inference results appear to be biased by race or gender, the inference is inappropriate. Even if AI inference is incorporated into a business process, it is desirable to be able to explain to the people and organizations affected that the business process has been built in a convincing manner in light of the original purpose of the business process. [Means for solving the problem]

[0007] A business process search device according to one embodiment of the present invention is a business process search device including a memory and a processor that functions as a functional unit by executing a program loaded into the memory, the functional unit including an input unit, a risk assessment unit, and a display unit, The input unit receives data of a plurality of business process candidates from a user and stores it in the data storage unit, and the data of the business process candidates includes a process flow including a process for performing inference using artificial intelligence, an impact assessment table in which the impact and impact assessment value of the conclusion of the business process, which is the content of the final process of the business process candidate, on the stakeholders is registered, and a transition probability table in which transition probabilities of branches included in the process flow are registered; The risk assessment unit calculates a risk score for each path leading to a possible conclusion of the business process candidate based on a disadvantage score calculated based on a negative impact assessment value of the impacts occurring on the path and the occurrence probability of the path, and calculates the sum of the risk scores calculated for multiple paths that the business process candidate can take as the risk score of the business process candidate, The display unit displays to the user the process flows of the multiple business process candidates and the risk scores of the business process candidates calculated by the risk assessment unit. [Effects of the Invention]

[0008] It is possible to build a business process that uses AI after assessing the risk of an inference error occurring in the AI. Other issues and novel features will become apparent from the description of this specification and the accompanying drawings. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 2 is a functional block diagram of the business process search device according to the first embodiment. [Figure 2] 1 is an example of a hardware configuration of an information processing device. [Figure 3] 10 is an example of a business process candidate. [Figure 4] 1 is an example of an impact assessment table. [Figure 5] 1 is an example of a transition probability table. [Figure 6] FIG. 10 is a diagram for explaining a risk score calculation method. [Figure 7] FIG. 10 is a diagram for explaining a risk score calculation process. [Figure 8] 10 is an example of a business process candidate evaluation screen. [Figure 9] This is the business process before AI was introduced. [Figure 10] 1 is an example of a change list. [Figure 11] 10 is an example of an ease evaluation table. [Figure 12] FIG. 10 is a functional block diagram of a business process search device according to a second embodiment. [Figure 13] 10 is an example of a confirmation ratio list. [Figure 14] 1 is an example of a sensitive attribute table. [Figure 15] FIG. 10 is a diagram for explaining a process for calculating a verification cost. [Figure 16] 10 is an example of a list of evaluation results. [Figure 17] 1 is an example of an execution cost list. [Figure 18] FIG. 10 is a diagram for explaining a process for calculating an execution cost. [Figure 19] 10 is an example of a list of evaluation results. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. [Example]

[0011] FIG. 1 shows a functional block diagram of a business process search device 10 according to a first embodiment. FIG. 2 shows the hardware configuration of the business process search device 10. The business process search device 10 is realized by an information processing device including, as shown in FIG. 2, a processor (CPU) 1, a memory 2, a storage device 3, an input device 4, an output device 5, a communication device 6, and a bus 7 as its main components. The processor 1 functions as a functional unit that provides a predetermined function by executing processing in accordance with a program loaded in the memory 2. The storage device 3 stores data and programs used in the functional unit. For the storage device 3, a non-volatile storage medium such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive) is used. The input device 4 is a keyboard, a pointing device, etc., and the output device 5 is a display, etc. The communication device 6 enables communication with other information processing devices and terminals via a network. These are connected to each other via the bus 7 so that they can communicate with each other.

[0012] The business process search device 10 does not need to be realized by one information processing device, but may be realized by multiple information processing devices. Also, some or all of the functions of the business process search device 10 may be realized as an application on the cloud.

[0013] The business process search device 10 is a device that is realized by an information processing device executing a business process search program, and has functional units of an input unit 11, a display unit 12, and a risk assessment unit 20. The business process search device 10 will be described using an example of business process construction in which AI is applied to task allocation work.

[0014] The input unit 11 is a functional unit that receives information input from the user regarding the business process to be constructed and stores it in the data storage unit 30. The input business process information includes the contents of business process candidates considered by the user for the business process to be constructed, i.e., business process candidate data 31 indicating the process flow of the business process candidate, an impact assessment table 32 which is information for evaluating the business process candidate, and a transition probability table 33. Details of these will be described later. This data input may be performed from the input device 4 or may be performed via the communication device 6 from a user terminal connected via a network. In addition, the data 31 to 33 may be stored in the storage device 3, or may be stored in a data server that can be connected to the business process exploration device 10 via a network, and an address for accessing the data server may be stored in the storage device 3.

[0015] The risk assessment unit 20 is a functional unit that calculates a risk score for each business process candidate. The risk assessment unit 20 includes sub-functional units: a disadvantage level calculation unit 21, a likelihood calculation unit 22, and a risk score calculation unit 23. These will be described in detail later.

[0016] The display unit 12 is a functional unit that presents the business process candidates to the user along with the risk scores calculated by the risk assessment unit 20. The user selects one of the business process candidates based on the risk scores. This allows the user to select a business process taking into account the risks that may arise from inference errors by the AI. The results may be presented to the user from the output device 5 or from the communication device 6 to a user terminal connected via a network.

[0017] Figure 3 shows business process candidates that the user inputs as business process candidate data 31. The business process candidate data 31 can be in any data format as long as it identifies the steps included in the business process candidate and the possible routes for the business process candidate. Here, it is assumed that the user inputs five business process candidates (#1 to #5).

[0018] The details of the business process for the first business process candidate 31-1 are explained below. First, the candidate consents to the use of AI (S01). If the candidate does not consent, the AI ​​will not be used to evaluate the candidate. After consenting to the use of AI, the candidate logs into the application system (S02) and films a promotional video highlighting their skills for the recruiting task (S03). The promotional video is then evaluated by the supervisor (evaluation manager) (S04a), and if the candidate's skills are deemed sufficient, the task is assigned to the candidate (S05). On the other hand, if the supervisor determines that the candidate's skills are insufficient, the AI ​​evaluates the promotional video (S04b), and if the AI ​​determines that the candidate's skills are sufficient, the candidate is assigned a task (S05). If both the supervisor and the AI ​​determine that the candidate's skills are insufficient, training is provided to improve the candidate's skills (S06).

[0019] The second to fourth business process candidates have the same steps as the first business process candidate, but the order of the supervisor's evaluation (S04a) and the AI's evaluation (S04b) and the steps after skill assessment are different. The fifth business process candidate does not include a supervisor's evaluation (S04a). The risks of an AI inference error differ not only for the fifth business process candidate, but also for the first to fourth business process candidates, which have the same steps. Therefore, the business process exploration device 10 visualizes and presents the risks of each business process candidate using a risk score.

[0020] The impact assessment table 32 and transition probability table 33 are basic information for assessing the risks in the business process candidates.

[0021] Impact Assessment Table 32 is a list that scores the impact that the conclusion of a business process has on stakeholders. Figure 4 shows an example of an impact assessment table applied to the business process candidate in Figure 3. Here, the conclusion of a business process refers to the content of the final step of the business process. In the example of Figure 3, the steps that could be the final step are consent to the use of AI (S01), task assignment (S05), and training (S06). Furthermore, the steps that could be the final step are classified as correct and incorrect. In the risk assessment of this embodiment, if the final step is a correct or incorrect step, the risk assessment is performed by dividing it into correct and incorrect steps. This is because, in general, the benefits and disadvantages of a correct conclusion in a business process are asymmetrical with the benefits and disadvantages of an incorrect conclusion. Here, consent (rejection) to the use of AI is a step without correct or incorrect decisions, while task assignment and training are steps with correct or incorrect decisions. Specifically, the correct task assignment and training steps mean that the task is assigned to a candidate with sufficient skills and that skill training is provided to a candidate with insufficient skills, respectively. In contrast, an error in the task allocation process and the training process means that a task is assigned to a candidate with insufficient skills, and skill training is provided to a candidate with sufficient skills.

[0022] The impact of the conclusion of a business process on the stakeholders and the evaluation value are determined by the user's consideration of the impact that the conclusion of the business process (including whether it is correct or incorrect, if any) will have on the stakeholders.

[0023] The impact ID 41 is an ID that uniquely identifies the impact that the conclusion of the business process extracted by the user has on the relevant parties. The conclusion of the business process is indicated by the combination of the final step 42 and the correct / incorrect judgment result 43. In this example, there are five possible conclusions for the business process: task assignment (correct / incorrect), training (correct / incorrect), and consent to the use of AI. The affected party 44 is the person who will be affected and is determined according to the content of the business process. In this example, it is the candidate or the superior. The impact item 45 and impact type 46 indicate the content of the impact on the affected party, and the impact evaluation value 47 indicates an evaluation value that scores the impact. The impact evaluation value 47 can be positive or negative; if the impact is positive for the affected party, the value is positive, and if the impact is negative for the affected party, the value is negative.

[0024] The transition probability table 33 is a list showing the transition probabilities when a path branches depending on the output of a process in a business process. FIG. 5 shows an example of a transition probability table applied to the business process candidate in FIG. 3. In the example of FIG. 3, the processes whose outputs cause branching are consent to the use of AI (S01), evaluation by a supervisor (S04a), and evaluation by AI (S04b). Furthermore, the output of a process is classified into correct and incorrect outputs. In the risk assessment of this embodiment, if the output of a process is correct or incorrect, the risk assessment is performed by dividing it into correct and incorrect outputs. Here, consent / rejection to the use of AI is an output without correct or incorrect output, and the evaluation by a supervisor (sufficient tasks / insufficient tasks) and the evaluation by AI (sufficient tasks / insufficient tasks) are outputs with correct or incorrect outputs. Specifically, the correct outputs of the supervisor's evaluation and the AI's evaluation mean that a candidate with sufficient skills is evaluated as having sufficient skills, and a candidate with insufficient skills is evaluated as having insufficient skills, respectively. In contrast, if the evaluation by a supervisor or the output of the AI ​​evaluation is incorrect, it means that a candidate with sufficient skills is evaluated as lacking skills, and a candidate with insufficient skills is evaluated as having sufficient skills, respectively.

[0025] The transition probability of a branch (if there is a right or wrong, this refers to the branch including the right or wrong) is determined by the user. Transition probability ID 51 is an ID that uniquely identifies a branch that can occur in a business process. A transition probability is set for each combination of process 52, output 53, and right or wrong judgment result 54. In this example, there are 10 possibilities: consent to use of AI (Yes / No), supervisor's evaluation "sufficient skills" (correct / incorrect), supervisor's evaluation "lack of skills" (correct / incorrect), AI evaluation "sufficient skills" (correct / incorrect), and AI evaluation "lack of skills" (correct / incorrect). Probability 55 indicates the transition probability of each branch, and the transition probability is set to a value of 100% for each process.

[0026] Using the above data, the risk assessment unit 20 calculates a risk score for each business process candidate. Figure 7 shows the risk score calculation process for (some of) the business process candidates shown in Figure 3. The risk score is calculated for each path included in the business process candidate. Here, a path refers to the path from the first step (here, consent to the use of AI) to the conclusion. As mentioned above, when the conclusion of a business process is correct or incorrect, the correct conclusion and the incorrect conclusion are treated as different conclusions. Therefore, when the final step is correct or incorrect, the path leading to the correct conclusion and the path leading to the incorrect conclusion are treated as different paths, even though the process flow of the paths is the same.

[0027] The path ID 61 is an ID that uniquely identifies a path. For ease of understanding, the ID is in the "XY" format, where X indicates a path with the same process flow and Y indicates a different conclusion. For example, path ID 1-1 and path ID 1-2 have the same process flow 63, but the conclusion of the business process candidate indicated as a combination of the final process 64 and the correctness judgment result 65 is different. The business process candidate ID indicates which of the first to fifth business process candidates shown in Figure 3 it corresponds to.

[0028] The processing of the risk assessment unit 20 will be explained below for each sub-function unit with reference to FIG.

[0029] The disadvantage level calculation unit 21 calculates the disadvantage score A for each route. p 68, disadvantage score D p 69 and disadvantage level DL p Calculate the profit score A p and disadvantage score D p The calculation is based on the impact assessment table 32 shown in Figure 4. Benefit score A p is the sum of the impact assessment values ​​for the pathway conclusions that are positive, and the detriment score D p is calculated as the sum of the absolute values ​​of the impact assessment values ​​for the conclusion of the path that are negative. For example, in the case of path 3-1, the final step is "education" and the result of the correct / incorrect judgment is "correct", and the benefit score A is calculated by looking up the impact assessment table (impact ID 6-9) for the case where the final step is "education" and the result of the correct / incorrect judgment is "correct". p is 3, disadvantage score D p Similarly, for Route 3-2, the benefit score A is calculated by referring to the impact assessment table (impact ID 10) when the final step is "education" and the correct / incorrect judgment result is "incorrect." p is 0, disadvantage score D p is calculated as 3. The same applies to the other routes.

[0030] Calculated profit score A p , disadvantage score D p From disadvantage level DL p Here, the disadvantage score D p From disadvantage level DL p An example of calculating is shown as (Equation 1).

[0031]

number

[0032] (Number 1) is the disadvantage score D p This is an example of a formula for dividing the magnitude of disadvantage into three levels. The formula will differ depending on the number of levels. p The maximum value of (max(D p )) is calculated for each business process candidate. The disadvantage score D for the first business process candidate pSince the maximum value of is 5, the disadvantage level DL p 1, disadvantage level DL in route 3-2 p is calculated as 2.

[0033] The likelihood calculation unit 22 calculates the occurrence probability P p 66 and likelihood L p Calculate the probability of occurrence of the route P p is calculated based on the transition probability table 33 shown in Fig. 5. For each path, it is sufficient to multiply the transition probability every time there is a branch along the process flow 63.

[0034] Calculated occurrence probability P p From the likelihood L p An example of the calculation formula is shown as (Equation 2).

[0035]

number

[0036] (Equation 2) is the occurrence probability P p This is an example of a formula for dividing the size of into three levels. The formula will differ depending on the number of levels.

[0037] The risk score calculation unit 23 calculates a risk score R for each route. p and calculate the risk score R for each business process candidate. p is the disadvantage level DL for each route p and likelihood L p An example of the calculation formula is shown in (Equation 3).

[0038]

number

[0039] (Equation 3) is a calculation formula for the risk score calculation method shown in Figure 6. That is, the disadvantage level DL for each route p and likelihood L pWhen the sum of is 3 or less, the risk score for each route R p 0, disadvantage level DL for each route p and likelihood L p When the sum of is greater than 3 and less than 4, the risk score for each route R p 1, disadvantage level DL for each route p and likelihood L p When the sum of is greater than 4 and less than 6, the risk score for each route R p The risk score R for each classification range and classification is p The value can be set arbitrarily by the user.

[0040] In the calculation example in Figure 7, the risk score R for each route is calculated based on (Equation 3). p For example, the risk score R for each route is calculated using the formula (4): p It is also possible to calculate

[0041]

number

[0042] The risk score R for each business process candidate is calculated by dividing the risk score R for each route of the business process candidate. p For example, the risk score R of the first business process candidate is calculated as the sum of the risk scores R of routes 1-1 to 1-4 in FIG. p Since it is the sum of

[0043] Through the above processing, the risk assessment unit 20 calculates a risk score R for each business process candidate.

[0044] 8 is an example of a business process candidate evaluation screen 80 displayed by the display unit 12. The business process candidates shown in FIG. 3 are displayed in a business process candidate display field 81, and the risk score R of each business process candidate calculated by the risk evaluation unit 20 is displayed in an evaluation result list 82. For example, it is desirable to highlight 83 business process candidates that are highly evaluated in terms of risk score R. In this example, the second and fourth business process candidates were highly evaluated (low risk).

[0045] Below, a modified example of the risk assessment method in the risk assessment unit 20 will be shown.

[0046] (Variation 1) If the introduction of AI changes the evaluation process and the benefits gained in the business process before the introduction of AI are no longer obtained after the introduction of AI, it is thought that the parties involved will perceive the loss of benefits as a disadvantage. Modification 1 is designed to reflect the benefits that are no longer obtained due to the introduction of AI in the disadvantage score. Modification 1 will be explained based on the example in Figure 3.

[0047] FIG. 9 shows the business process before the introduction of AI. The same steps as in FIG. 3 are assigned the same symbols. FIG. 10 shows a change list 34 indicating whether or not there is a change due to the introduction of AI for each process flow. The change list 34 is one piece of business process information entered by the user and is stored in the data storage unit 30. The process flow ID 91 is an ID that uniquely identifies the process flow 93, and the presence or absence of change before AI introduction 94 indicates whether or not there is a change for each process flow compared to the business process before the introduction of AI. For example, in the case of process flow ID2, even though the evaluation by the superior (S04a) is that the skill is insufficient, a change due to the introduction of AI has occurred in that task allocation (S05) has been carried out. In this modified example, in this case, the disadvantage score D of the route ID2-2 (correctness judgment result: incorrect) corresponding to the process flow ID2 is p ' is the disadvantage score D of the original route ID2-2 p and Route ID2-1 (correctness judgment result: correct) profit score A p Sum of (D p +Ap ) In other words, in the example of FIG. 7, the disadvantage score D p ' is 0, disadvantage score D for route ID 2-2 p ' is 7. Disadvantage Level DL p is the disadvantage score D in (Equation 1) in Example 1 p Variation 1 disadvantage score D p It can be calculated by replacing it with (Equation 5).

[0048]

number

[0049] (Variation 2) Modification 2 evaluates the ease of detecting errors in the AI ​​inference results by using the likelihood L p The second modification will be described with reference to the example of FIG.

[0050] Detecting errors in AI inference results is difficult for business process candidates that do not include evaluation by a superior. Even for business process candidates that include evaluation by a superior, it can be difficult depending on the order of human evaluation and AI evaluation. Specifically, when the order is evaluation by a superior followed by evaluation by an AI, it is difficult to detect errors in the AI ​​inference results if the evaluations are the same. Conversely, when the order is evaluation by an AI followed by evaluation by a superior, it is difficult to detect errors in the AI ​​inference results if the evaluations are different. Based on the above concept, Figure 11 shows a table of easiness evaluations 35 that evaluates the ease of AI error detection for each process flow. The table of easiness evaluations 35 is a piece of business process information entered by a user and stored in the data storage unit 30. The process flow ID 101 is an ID that uniquely identifies the process flow 103. The AI ​​error detectability 104 indicates the ease of error detection in the AI ​​inference results evaluated for each process flow according to the above criteria.

[0051] In the second modification, the likelihood L pis calculated using a formula that reflects the ease of error detection in the AI's inference results. An example of the formula is shown in (Equation 6).

[0052]

number

[0053] Here, e is the ease score, and the probability of the path occurring is corrected by the ease score. Specifically, if it is easy to detect an error in the AI's inference result, e = 0.5, and if it is difficult to detect an error in the AI's inference result, e = 1, and the likelihood L p Calculate. [Example]

[0054] FIG. 12 shows a functional block diagram of a business process search device 10 according to the second embodiment. Components common to those in the business process search device 10 according to the first embodiment are assigned the same reference numerals, and redundant descriptions will be omitted. As will be described in detail later, the business process search device 10 according to the second embodiment includes a cost evaluation unit 110 as an additional functional unit in addition to the functional units of the first embodiment. The cost evaluation unit 110 includes a confirmation cost calculation unit 111 and an execution cost calculation unit 112, which are sub-functional units. Furthermore, the data storage unit 30 stores a confirmation ratio list 120, a sensitive attribute table 130, and an execution cost list 150 in addition to the business process information of the first embodiment. These details will be described later. The business process search device 10 according to the second embodiment enables selection of a business process based on the costs incurred in its operation. The costs incurred in the operation of the business process include the confirmation cost and the execution cost. The hardware configuration of the business process search device 10 according to the second embodiment is also the same as that according to the first embodiment.

[0055] (Confirmation cost) In business processes where AI is introduced, it is necessary to continually check whether the conclusion of the business process is correct. Furthermore, it is necessary to check the conclusion of the business process from the perspective of AI ethics. Therefore, the confirmation cost calculation unit 111 visualizes the cost of checking the conclusion of the business process (hereinafter referred to as confirmation cost). Also, in order to reduce the confirmation cost C1, instead of checking all cases, the confirmation ratio, which is the percentage of cases to be checked, is determined in conjunction with the risk score.

[0056] FIG. 13 shows a confirmation ratio list 120. The confirmation ratio is set according to the risk score 121. Here, the risk score R shown in FIG. 6 is used, and has three levels of values. R max The reason for this is that, as will be described later, the confirmation cost is first calculated for each process flow of the business process candidate, so if the process flow contains multiple paths (i.e., the conclusion is correct or incorrect), the risk score R of multiple paths in the same process flow will be calculated. p This is because the confirmation ratio is set to the maximum value among the above. Correctness confirmation ratio (IR1) 122 indicates the ratio at which correctness confirmation is performed, and in the example of Figure 3, correctness confirmation refers to checking whether tasks are assigned according to skills. The higher the risk score, the greater the impact of an erroneous conclusion, so the correctness confirmation ratio IR1 is increased according to the risk score. Performance bias confirmation ratio (IR2) 123 indicates the ratio at which performance bias confirmation is performed, and in the example of Figure 3, performance bias confirmation refers to checking whether task assignments are biased from the perspective of AI ethics. Since business process candidates with low risk scores are more likely to be adopted, the performance bias confirmation ratio IR2 is decreased according to the risk score.

[0057] Figure 14 is an example of a sensitive attribute table 130 for checking for performance bias. From the perspective of AI ethics, it is undesirable for unreasonable bias to occur in the attributes of the candidates to be assigned in the conclusions of a business process. Therefore, attributes for which it is particularly undesirable for bias to occur (sensitive attributes) are registered in advance as a sensitive attribute table, and it is checked whether unexplainable bias has occurred in the conclusions of the business process. Attribute 131 indicates the sensitive attribute to be checked, classification 132 indicates the classification for checking for bias in sensitive attributes, and number of classifications 133 indicates the number of classifications CN in classification 132. Here, an example is shown in which gender and age are treated as sensitive attributes.

[0058] Based on the above business process information, the confirmation cost C1 for each process flow of the business process candidate is calculated. f An example of the calculation formula is shown in (Equation 7).

[0059]

number

[0060] The confirmation cost C1 for each process flow for the example shown in Figure 7 f A calculation example of the risk score is shown in Figure 15. For example, in the process flow ID 1, the maximum risk score R max is the risk score R for Pathway 1-1 and Pathway 1-2. p (See Figure 7) and compare it to get 1. By substituting the correctness confirmation ratio IR1 (50 in this case), the performance bias confirmation ratio IR2 (10 in this case), and the sum of the number of classifications CN (5) into (Equation 7), the confirmation cost C1 f is calculated as 100.

[0061] After that, the cost of confirming the process flow included in the business process candidate is C1 fThe sum of these is calculated as the confirmation cost C1 of the business process candidate. The result is displayed on the business process candidate evaluation screen displayed by the display unit 12 (see FIG. 8). An evaluation result list 82b in the second embodiment is shown in FIG. 16. In addition to the risk score R of each business process candidate calculated by the risk evaluation unit 20, the confirmation cost C1 of each business process candidate calculated by the cost evaluation unit 110 is also displayed.

[0062] (Execution cost) The execution cost calculation unit 112 visualizes the cost required to execute a business process (hereinafter referred to as the execution cost).

[0063] 17 shows an execution cost list 150. An execution cost is set for each step included in a business process candidate. A step ID 151 is an ID that uniquely identifies a step, and an execution cost 153 is set for each step 152.

[0064] Based on the execution cost of each process, the execution cost C2 of each process flow in the business process candidate is calculated. f Calculate the process flow execution cost C2 f is the execution cost of the process flow (called the total execution cost SC) and the occurrence probability of the process flow P f The product of (C2 f =P f × SC). The confirmation cost for each process flow for the example shown in Figure 7 is C2 f An example of calculation of is shown in Figure 18. For example, the occurrence probability P f is the probability of occurrence of route 1-1 and route 1-2 P p (See Figure 7) The total execution cost SC of process flow ID 1 is 10 because it includes process ID 4 (evaluation by superior). Therefore, the execution cost C2 of process flow ID 1 f =285.

[0065] After that, the cost of confirming the process flow included in the business process candidate is C2 fThe sum of these is calculated as the execution cost C2 of the business process candidate. The result is displayed on the business process candidate evaluation screen displayed by the display unit 12 (see FIG. 8). An evaluation result list 82c in the second embodiment is shown in FIG. 19. In addition to the risk score R of each business process candidate calculated by the risk evaluation unit 20, the confirmation cost C1 and execution cost C2 of each business process candidate calculated by the cost evaluation unit 110 are also displayed. The user can select a business process candidate to adopt by taking into consideration the risk evaluation and cost evaluation.

[0066] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and are not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations. [Explanation of symbols]

[0067] 1: Processor (CPU), 2: Memory, 3: Storage device, 4: Input device, 5: Output device, 6: Communication device, 7: Bus, 10: Business process search device, 11: Input unit, 12: Display unit, 20: Risk assessment unit, 21: Disadvantage level calculation unit, 22: Likelihood calculation unit, 23: Risk score calculation unit, 30: Data storage unit, 31: Business process candidate data, 32: Impact assessment table, 33: Transition probability table, 34: Change list table, 35: Ease assessment list table, 80: Business process candidate evaluation screen, 81: Business process candidate display column, 82, 82b, 82c: Evaluation result list, 110: Cost assessment unit, 111: Confirmation cost calculation unit, 112: Execution cost calculation unit, 120: Confirmation ratio list table, 130: Sensitive attribute table, 150: Execution cost list table.

Claims

1. A business process search device comprising a memory and a processor that functions as a functional unit by executing a program loaded into the memory, The functional unit includes an input unit, a risk assessment unit, and a display unit, The input unit receives data of a plurality of business process candidates from a user and stores it in the data storage unit, and the data of the business process candidates includes a process flow including a process for performing inference using artificial intelligence, an impact assessment table in which the impact and impact assessment value of the conclusion of the business process, which is the content of the final process of the business process candidate, on stakeholders is registered, and a transition probability table in which transition probabilities of branches included in the process flow are registered. the risk assessment unit calculates a risk score for each path leading to a possible conclusion of the business process candidate based on a disadvantage score calculated based on the negative impact assessment value of the impacts occurring on the path and the occurrence probability of the path, and calculates the sum of the risk scores calculated for multiple paths that the business process candidate can take as the risk score of the business process candidate; The display unit displays to a user process flows of the plurality of business process candidates and risk scores of the business process candidates calculated by the risk assessment unit.

2. In claim 1, The data of the business process candidate includes a change list that determines whether or not there is a change by comparing the process flow of the business process candidate with the process flow of a business process that does not include a process that performs inference using artificial intelligence, The risk assessment unit calculates the disadvantage score of a first path that leads to an incorrect conclusion as the sum of the negative impact assessment value of the impacts that occur on the first path and the positive impact assessment value of the impacts that occur on a second path that leads to a correct conclusion via the same process flow as the first path.

3. In claim 1, The data of the business process candidate includes an ease evaluation list that determines the ease of detecting errors in inference by artificial intelligence in the process flow of the business process candidate, The risk assessment unit calculates a risk score for each path based on the corrected occurrence probability obtained by correcting the occurrence probability of a path leading to a conclusion that the business process candidate can take based on the judgment of the ease assessment list.

4. In claim 1, The functional unit includes a cost evaluation unit, The data of the business process candidate includes a confirmation ratio list that defines confirmation ratios for confirming the conclusions of the business process candidate, and the confirmation ratios are determined according to risk scores of the process flows of the business process candidate; the cost evaluation unit calculates a confirmation cost for each process flow of the business process candidate based on a confirmation ratio corresponding to the risk score of the process flow, and calculates the sum of the confirmation costs calculated for the process flows of the business process candidate as the confirmation cost of the business process candidate; The display unit displays to a user the process flows of the business process candidates and the confirmation costs of the business process candidates calculated by the cost evaluation unit.

5. In claim 4, The confirmation ratio list of the business process exploration device specifies a confirmation ratio for correctness confirmation, which confirms the correctness of the conclusion of the business process, and a confirmation ratio for performance bias confirmation, which checks whether biased decisions are being made from the perspective of AI ethics.

6. In claim 1, The functional unit includes a cost evaluation unit, The data of the business process candidates includes an execution cost list showing the execution costs of each step included in the business process candidates, the cost evaluation unit calculates an execution cost for each process flow of the business process candidate based on the occurrence probability of the process flow and the total execution cost of the process flow calculated from the execution cost list, and calculates the sum of the execution costs calculated for the process flows of the business process candidate as the execution cost of the business process candidate; The display unit displays to a user the process flows of the plurality of business process candidates and the execution costs of the business process candidates calculated by the cost evaluation unit.

7. A business process search method using a business process search device including a memory and a processor that functions as a functional unit by executing a program loaded into the memory, The functional unit includes an input unit, a risk assessment unit, and a display unit, The input unit receives data of a plurality of business process candidates from a user and stores it in the data storage unit, and the data of the business process candidates includes a process flow including a process for performing inference using artificial intelligence, an impact assessment table in which the impact and impact assessment value of the conclusion of the business process, which is the content of the final process of the business process candidate, on stakeholders is registered, and a transition probability table in which transition probabilities of branches included in the process flow are registered. the risk assessment unit calculates a risk score for each path leading to a possible conclusion of the business process candidate based on a disadvantage score calculated based on the negative impact assessment value of the impacts occurring on the path and the occurrence probability of the path, and calculates the sum of the risk scores calculated for multiple paths that the business process candidate can take as the risk score of the business process candidate; The display unit displays to a user process flows of the plurality of business process candidates and risk scores of the business process candidates calculated by the risk assessment unit.

8. In claim 7, The functional unit includes a cost evaluation unit, The data of the business process candidate includes a confirmation ratio list that defines confirmation ratios for confirming the conclusions of the business process candidate, and the confirmation ratios are determined according to risk scores of the process flows of the business process candidate; the cost evaluation unit calculates a confirmation cost for each process flow of the business process candidate based on a confirmation ratio corresponding to the risk score of the process flow, and calculates the sum of the confirmation costs calculated for the process flows of the business process candidate as the confirmation cost of the business process candidate; The display unit displays to a user process flows of the plurality of business process candidates and confirmation costs of the business process candidates calculated by the cost evaluation unit.

9. In claim 8, The confirmation ratio list defines a confirmation ratio for correctness confirmation, which confirms whether the conclusion of the business process is correct, and a confirmation ratio for performance bias confirmation, which confirms whether biased decisions are being made from the perspective of AI ethics.

10. In claim 7, The functional unit includes a cost evaluation unit, The data of the business process candidates includes an execution cost list showing the execution costs of each step included in the business process candidates, the cost evaluation unit calculates an execution cost for each process flow of the business process candidate based on the occurrence probability of the process flow and the total execution cost of the process flow calculated from the execution cost list, and calculates the sum of the execution costs calculated for the process flows of the business process candidate as the execution cost of the business process candidate; The display unit displays to a user process flows of the plurality of business process candidates and execution costs of the business process candidates calculated by the cost evaluation unit.

11. A business process search program executed by an information processing device having a memory and a processor, The business process search program is loaded into the memory and executed by the processor, thereby functioning as an input unit, a risk assessment unit, and a display unit; The input unit receives data of a plurality of business process candidates from a user and stores it in the data storage unit, and the data of the business process candidates includes a process flow including a process for performing inference using artificial intelligence, an impact assessment table in which the impact and impact assessment value of the conclusion of the business process, which is the content of the final process of the business process candidate, on stakeholders is registered, and a transition probability table in which transition probabilities of branches included in the process flow are registered. the risk assessment unit calculates a risk score for each path leading to a possible conclusion of the business process candidate based on a disadvantage score calculated based on the negative impact assessment value of the impacts occurring on the path and the occurrence probability of the path, and calculates the sum of the risk scores calculated for multiple paths that the business process candidate can take as the risk score of the business process candidate; The display unit displays to a user the process flows of the plurality of business process candidates and the risk scores of the business process candidates calculated by the risk assessment unit.

12. In claim 11, the business process search program is loaded into the memory and executed by the processor, thereby functioning as a cost evaluation unit; The data of the business process candidate includes a confirmation ratio list that defines confirmation ratios for confirming the conclusions of the business process candidate, and the confirmation ratios are determined according to risk scores of the process flows of the business process candidate; the cost evaluation unit calculates a confirmation cost for each process flow of the business process candidate based on a confirmation ratio corresponding to the risk score of the process flow, and calculates the sum of the confirmation costs calculated for the process flows of the business process candidate as the confirmation cost of the business process candidate; The display unit displays to a user the process flows of the business process candidates and the confirmation costs of the business process candidates calculated by the cost evaluation unit.

13. In claim 12, The confirmation ratio list defines a confirmation ratio for correctness confirmation, which confirms the correctness of the conclusion of the business process, and a confirmation ratio for performance bias confirmation, which checks whether biased decisions are being made from the perspective of AI ethics.

14. In claim 11, the business process search program is loaded into the memory and executed by the processor, thereby functioning as a cost evaluation unit; The data of the business process candidates includes an execution cost list showing the execution costs of each step included in the business process candidates, the cost evaluation unit calculates an execution cost for each process flow of the business process candidate based on the occurrence probability of the process flow and the total execution cost of the process flow calculated from the execution cost list, and calculates the sum of the execution costs calculated for the process flows of the business process candidate as the execution cost of the business process candidate; The display unit displays to a user process flows of the plurality of business process candidates and the execution costs of the business process candidates calculated by the cost evaluation unit.

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