GB2612001A - System and method for generating decision confidence index scores and bias assessment scores for interactive decision-making - Google Patents
System and method for generating decision confidence index scores and bias assessment scores for interactive decision-making Download PDFInfo
- Publication number
- GB2612001A GB2612001A GB2302272.6A GB202302272A GB2612001A GB 2612001 A GB2612001 A GB 2612001A GB 202302272 A GB202302272 A GB 202302272A GB 2612001 A GB2612001 A GB 2612001A
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- United Kingdom
- Prior art keywords
- decision
- score
- bias
- dimension
- statement
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/045—Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/9032—Query formulation
- G06F16/90332—Natural language query formulation or dialogue systems
Abstract
A computer-based decisioning tool is disclosed that enables the generation of Bias Assessment Scores to indicate a level of hidden or unconscious bias an individual being tested may have and enables the generation of Confidence Index Scores the indicate the best choices for making three types of decisions, which are searching/match decisions, comparison decisions, and binary (Yes/No) decisions.
Claims (16)
1. A computer-based method for generating a bias assessment score that is indicative of a level of at least hidden or unconscious bias with regard to a specific subject area, comprising the steps of: A. selecting a potential bias subject area that a first individual may have hidden or unconscious bias; B. develop a computer-based template that includes at least one bias profile statement directed to the potential bias subject area, at least one bias object that is a first level of statement below the bias profile statement directed to the potential bias subject area, and at least two bias dimension statements that are a first level of statement below the bias object statement and a second level of statement below the bias profile statement directed to the potential bias subject area, with each bias dimension statement including at least two parts for which the first individual is capable of providing a response to each part on a predetermined scale and uploading the template to a database of a system computer that is accessible by the first individual, wherein the first part of the two parts includes a question for a response is needed and the second part is an importance of the first part question to the first individual; C. the first individual downloading the template and separately recording a response along the scale of each of the two parts of each of the two bias dimension statements and uploading the responses to the system computer, D. the system computer generating a bias assessment score indicative of the level of at least hidden or unconscious bias in the first individual by i. receiving inputs for a system dimension importance score for each bias dimension, a system object dimension score for the bias object, and an ideal object importance score, ii. determining a bias dimension score for each bias dimension by determining a product of the first part score, the second part score, and the system dimension importance score, with a result being a first bias dimension score and a second bias dimension score, iii. determining a bias object score by summing the first and second bias dimension scores, 54 iv. determining a bias profile score by summing the bias object score with any additional bias object scores and if there is just the bias object score at step D(iii), the bias profile score equals the bias object score, and v. determining the bias assessment score for the first individual as a percentage by subtracting an absolute value between the bias profile score and the ideal object importance score from the ideal object importance score and then divided by the ideal object importance score and multiplying by 100.
2. The method as recited in claim 1, wherein the bias object statement includes a question.
3. The method as recited in claim 1, wherein the predetermine scale for first and second parts of each bias dimension includes a range.
4. The method as recited in claim 3, wherein the ranges for each bias dimension is capable of being divides into sub-ranges.
5. The method as recited in claim 4, wherein each sub-range is capable of being assigned a value for determining the bias assessment score.
6. The method as recited in claim 3, wherein each range includes having a portion thereof as acceptable for selection of a score that is capable value for determining the bias assessment score.
7. The method as recited in claim 3, wherein ranges for the first part of the bias dimension statement includes numeric or percentage ranges.
8. The method as recited in claim 3, wherein ranges for the second part of the bias dimension statement includes percentage ranges.
9. A computer-based decision-making method for generating a confidence index score to indicate a best decision option, comprising the steps of: A. a system user selecting a decision type to conduct a decision; B. the system user defining the subject area for which a decision is sought; C. the system user defining sourcing for potential decision targets in the defined subject area and generating a searchable computer database for identifying potential target; D. the system user developing a computer-based template that includes at least one decision profile statement directed to the decision subject area, at least one decision object that is a first level of statement below the decision profile statement directed to the decision subject 55 area, and at least two decision dimension statements that are a first level of statement below the decision object statement and a second level of statement below the decision profile statement directed to the decision subject area, with each decision dimension statement including at least two parts for which the first individual is capable of providing a response to each part on a predetermined scale and uploading the template to a database of a system computer that can be retrieved the system user for transmission to decision targets, wherein the first part of the two parts includes a question for a response is needed and the second part is an importance of the first part question to a decision target; E. the system user searching the targetâ s searchable database with the decision profile, decision objects, and decision dimensions for matching decision targets, and receiving matched decision targets ranked by confidence index scores with the confidence index score for each decision target being determined by i. a system computer receiving for each decision target a targetâ s scores for the first and second parts of each decision dimension statement, the system userâ s importance score for each decision dimension, and an ideal target object importance score, ii. the system computer determining a decision dimension score for each decision dimension by determining a product of the first part score, the second part score, and the system user dimension importance score, with a result being a first decision dimension score and a second decision dimension score, iii. determining a decision object score by summing the first and second decision dimension scores, iv. determining a decision profile score by summing the decision object score with any additional decision object scores and if there is just the decision object score at step E(iii), the decision profile score equals the decision object score, and v. determining the confidence index score for each decision target as a percentage by subtracting an absolute value between the decision profile score and the ideal target object importance score from the ideal target object importance score and then divided by the ideal target object importance score and multiplying by 100, 56 F. the computer system ranking the decision targets by confidence index scores and transmitting the ranking to the system, and if the system user selects the decision target with the highest confidence index score, then end the process, otherwise step G; G. the system user sending addition one or more additional questions to one or more decision targets and generating new confidence index scores according to step D for the decision targets and ranking the decision targets according to the new confidence index scores; and H. repeating steps F and G until the system user selects a decision target.
10. The method as recited in claim 9, wherein the decision object statement includes a question.
11. The method as recited in claim 9, wherein the predetermine scale for first and second pails of each decision dimension includes a range.
12. The method as recited in claim 11, wherein the ranges for each decision dimension is capable of being divides into sub-ranges.
13. The method as recited in claim 12, wherein each sub-range is capable of being assigned a value for determining the confidence index score.
14. The method as recited in claim 11, wherein each range includes having a portion thereof as acceptable for selection of a score that is capable value for determining the confidence index score.
15. The method as recited in claim 11 , wherein ranges for the first part of the decision dimension statement includes numeric or percentage ranges.
16. The method as recited in claim 11, wherein ranges for the second part of the decision dimension statement includes percentage ranges. 57
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202063103701P | 2020-08-19 | 2020-08-19 | |
| PCT/US2021/010035 WO2022039777A1 (en) | 2020-08-19 | 2021-08-19 | System and method for generating decision confidence index scores and bias assessment scores for interactive decision-making |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| GB202302272D0 GB202302272D0 (en) | 2023-04-05 |
| GB2612001A true GB2612001A (en) | 2023-04-19 |
Family
ID=80323039
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GB2302272.6A Pending GB2612001A (en) | 2020-08-19 | 2021-08-19 | System and method for generating decision confidence index scores and bias assessment scores for interactive decision-making |
Country Status (4)
| Country | Link |
|---|---|
| EP (1) | EP4200764A1 (en) |
| CA (1) | CA3190074A1 (en) |
| GB (1) | GB2612001A (en) |
| WO (1) | WO2022039777A1 (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20010041980A1 (en) * | 1999-08-26 | 2001-11-15 | Howard John Howard K. | Automatic control of household activity using speech recognition and natural language |
| US20060259475A1 (en) * | 2005-05-10 | 2006-11-16 | Dehlinger Peter J | Database system and method for retrieving records from a record library |
| US20150294377A1 (en) * | 2009-05-30 | 2015-10-15 | Edmond K. Chow | Trust network effect |
-
2021
- 2021-08-19 WO PCT/US2021/010035 patent/WO2022039777A1/en unknown
- 2021-08-19 CA CA3190074A patent/CA3190074A1/en active Pending
- 2021-08-19 GB GB2302272.6A patent/GB2612001A/en active Pending
- 2021-08-19 EP EP21858741.8A patent/EP4200764A1/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20010041980A1 (en) * | 1999-08-26 | 2001-11-15 | Howard John Howard K. | Automatic control of household activity using speech recognition and natural language |
| US20060259475A1 (en) * | 2005-05-10 | 2006-11-16 | Dehlinger Peter J | Database system and method for retrieving records from a record library |
| US20150294377A1 (en) * | 2009-05-30 | 2015-10-15 | Edmond K. Chow | Trust network effect |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2022039777A1 (en) | 2022-02-24 |
| GB202302272D0 (en) | 2023-04-05 |
| EP4200764A1 (en) | 2023-06-28 |
| WO2022039777A9 (en) | 2023-11-02 |
| CA3190074A1 (en) | 2022-02-24 |
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