Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model
A high-stakes decision requires deep thought to understand the complex factors that stop a situation from becoming worse. Such decisions are carried out under high pressure, with a lack of information, and in limited time. This research applies Causal Artificial Intelligence to high-stakes decisions...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9722845/ |
| _version_ | 1818504712315469824 |
|---|---|
| author | Bukhoree Sahoh Kanjana Haruehansapong Mallika Kliangkhlao |
| author_facet | Bukhoree Sahoh Kanjana Haruehansapong Mallika Kliangkhlao |
| author_sort | Bukhoree Sahoh |
| collection | DOAJ |
| description | A high-stakes decision requires deep thought to understand the complex factors that stop a situation from becoming worse. Such decisions are carried out under high pressure, with a lack of information, and in limited time. This research applies Causal Artificial Intelligence to high-stakes decisions, aiming to encode causal assumptions based on human-like intelligence, and thereby produce interpretable and argumentative knowledge. We develop a Causal Bayesian Networks model based on causal science using <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-separation and <italic>do</italic>-operations to discover the causal graph aligned with cognitive understanding. Causal odd ratios are used to measure the causal assumptions integrated with the real-world data to prove the proposed causal model compatibility. Causal effect relationships in the model are verified based on causal P-values and causal confident intervals and approved less than 1% by random chance. It shows that the causal model can encode cognitive understanding as precise, robust relationships. The concept of model design allows software agents to imitate human intelligence by inferring potential knowledge and be employed in high-stakes decision applications. |
| first_indexed | 2024-12-10T21:40:54Z |
| format | Article |
| id | doaj.art-5b4de778a9af47448aa6cc7f729a5797 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T21:40:54Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5b4de778a9af47448aa6cc7f729a57972022-12-22T01:32:31ZengIEEEIEEE Access2169-35362022-01-0110243272433910.1109/ACCESS.2022.31551189722845Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning ModelBukhoree Sahoh0https://orcid.org/0000-0002-5953-9874Kanjana Haruehansapong1Mallika Kliangkhlao2https://orcid.org/0000-0002-1534-3255School of Informatics, Walailak University, Tha Sala, Nakhon Si Thammarat, ThailandSchool of Informatics, Walailak University, Tha Sala, Nakhon Si Thammarat, ThailandDepartment of Computer Engineering, Prince of Songkla University, Hat Yai, Songkhla, ThailandA high-stakes decision requires deep thought to understand the complex factors that stop a situation from becoming worse. Such decisions are carried out under high pressure, with a lack of information, and in limited time. This research applies Causal Artificial Intelligence to high-stakes decisions, aiming to encode causal assumptions based on human-like intelligence, and thereby produce interpretable and argumentative knowledge. We develop a Causal Bayesian Networks model based on causal science using <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-separation and <italic>do</italic>-operations to discover the causal graph aligned with cognitive understanding. Causal odd ratios are used to measure the causal assumptions integrated with the real-world data to prove the proposed causal model compatibility. Causal effect relationships in the model are verified based on causal P-values and causal confident intervals and approved less than 1% by random chance. It shows that the causal model can encode cognitive understanding as precise, robust relationships. The concept of model design allows software agents to imitate human intelligence by inferring potential knowledge and be employed in high-stakes decision applications.https://ieeexplore.ieee.org/document/9722845/Artificial intelligencecounterfactualscausal sciencedo-calculuscausal inferencecognitive computing |
| spellingShingle | Bukhoree Sahoh Kanjana Haruehansapong Mallika Kliangkhlao Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model IEEE Access Artificial intelligence counterfactuals causal science do-calculus causal inference cognitive computing |
| title | Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model |
| title_full | Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model |
| title_fullStr | Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model |
| title_full_unstemmed | Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model |
| title_short | Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model |
| title_sort | causal artificial intelligence for high stakes decisions the design and development of a causal machine learning model |
| topic | Artificial intelligence counterfactuals causal science do-calculus causal inference cognitive computing |
| url | https://ieeexplore.ieee.org/document/9722845/ |
| work_keys_str_mv | AT bukhoreesahoh causalartificialintelligenceforhighstakesdecisionsthedesignanddevelopmentofacausalmachinelearningmodel AT kanjanaharuehansapong causalartificialintelligenceforhighstakesdecisionsthedesignanddevelopmentofacausalmachinelearningmodel AT mallikakliangkhlao causalartificialintelligenceforhighstakesdecisionsthedesignanddevelopmentofacausalmachinelearningmodel |