Toward Accountable and Explainable Artificial Intelligence Part one: Theory and Examples
Like other Artificial Intelligence (AI) systems, Machine Learning (ML) applications cannot explain decisions, are marred with training-caused biases, and suffer from algorithmic limitations. Their eXplainable Artificial Intelligence (XAI) capabilities are typically measured in a two-dimensional spac...
| Main Authors: | , |
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| Format: | Journal Article |
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IEEE
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/89344 |
| _version_ | 1848765203484770304 |
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| author | Khan, Masood Vice, Jordan |
| author_facet | Khan, Masood Vice, Jordan |
| author_sort | Khan, Masood |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Like other Artificial Intelligence (AI) systems, Machine Learning (ML) applications cannot explain decisions, are marred with training-caused biases, and suffer from algorithmic limitations. Their eXplainable Artificial Intelligence (XAI) capabilities are typically measured in a two-dimensional space of explainability and accuracy ignoring the accountability aspects. During system evaluations, measures of comprehensibility, predictive accuracy and accountability remain inseparable. We propose an Accountable eXplainable Artificial Intelligence (AXAI) capability framework for facilitating separation and measurement of predictive accuracy, comprehensibility and accountability. The proposed framework, in its current form, allows assessing embedded levels of AXAI for delineating ML systems in a three-dimensional space. The AXAI framework quantifies comprehensibility in terms of the readiness of users to apply the acquired knowledge and assesses predictive accuracy in terms of the ratio of test and training data, training data size and the number of false-positive inferences. For establishing a chain of responsibility, accountability is measured in terms of the inspectability of input cues, data being processed and the output information. We demonstrate applying the framework for assessing the AXAI capabilities of three ML systems. The reported work provides bases for building AXAI capability frameworks for other genres of AI systems. |
| first_indexed | 2025-11-14T11:31:31Z |
| format | Journal Article |
| id | curtin-20.500.11937-89344 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:31:31Z |
| publishDate | 2022 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-893442022-09-30T06:26:21Z Toward Accountable and Explainable Artificial Intelligence Part one: Theory and Examples Khan, Masood Vice, Jordan 0801 - Artificial Intelligence and Image Processing 4602 - Artificial intelligence Like other Artificial Intelligence (AI) systems, Machine Learning (ML) applications cannot explain decisions, are marred with training-caused biases, and suffer from algorithmic limitations. Their eXplainable Artificial Intelligence (XAI) capabilities are typically measured in a two-dimensional space of explainability and accuracy ignoring the accountability aspects. During system evaluations, measures of comprehensibility, predictive accuracy and accountability remain inseparable. We propose an Accountable eXplainable Artificial Intelligence (AXAI) capability framework for facilitating separation and measurement of predictive accuracy, comprehensibility and accountability. The proposed framework, in its current form, allows assessing embedded levels of AXAI for delineating ML systems in a three-dimensional space. The AXAI framework quantifies comprehensibility in terms of the readiness of users to apply the acquired knowledge and assesses predictive accuracy in terms of the ratio of test and training data, training data size and the number of false-positive inferences. For establishing a chain of responsibility, accountability is measured in terms of the inspectability of input cues, data being processed and the output information. We demonstrate applying the framework for assessing the AXAI capabilities of three ML systems. The reported work provides bases for building AXAI capability frameworks for other genres of AI systems. 2022 Journal Article http://hdl.handle.net/20.500.11937/89344 10.1109/ACCESS.2022.3207812 http://creativecommons.org/licenses/by/4.0/ IEEE fulltext |
| spellingShingle | 0801 - Artificial Intelligence and Image Processing 4602 - Artificial intelligence Khan, Masood Vice, Jordan Toward Accountable and Explainable Artificial Intelligence Part one: Theory and Examples |
| title | Toward Accountable and Explainable Artificial Intelligence Part one: Theory and Examples |
| title_full | Toward Accountable and Explainable Artificial Intelligence Part one: Theory and Examples |
| title_fullStr | Toward Accountable and Explainable Artificial Intelligence Part one: Theory and Examples |
| title_full_unstemmed | Toward Accountable and Explainable Artificial Intelligence Part one: Theory and Examples |
| title_short | Toward Accountable and Explainable Artificial Intelligence Part one: Theory and Examples |
| title_sort | toward accountable and explainable artificial intelligence part one: theory and examples |
| topic | 0801 - Artificial Intelligence and Image Processing 4602 - Artificial intelligence |
| url | http://hdl.handle.net/20.500.11937/89344 |