Enabling Artificial Intelligence Adoption through Assurance
The wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair intelligent syst...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
|
| Series: | Social Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0760/10/9/322 |
| _version_ | 1797517168313630720 |
|---|---|
| author | Laura Freeman Abdul Rahman Feras A. Batarseh |
| author_facet | Laura Freeman Abdul Rahman Feras A. Batarseh |
| author_sort | Laura Freeman |
| collection | DOAJ |
| description | The wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair intelligent systems. AI assurance provides the necessary tools to enable AI adoption into applications, software, hardware, and complex systems. AI assurance involves quantifying capabilities and associating risks across deployments including: data quality to include inherent biases, algorithm performance, statistical errors, and algorithm trustworthiness and security. Data, algorithmic, and context/domain-specific factors may change over time and impact the ability of AI systems in delivering accurate outcomes. In this paper, we discuss the importance and different angles of AI assurance, and present a general framework that addresses its challenges. |
| first_indexed | 2024-03-10T07:12:58Z |
| format | Article |
| id | doaj.art-e75970e6cca840f5ab94629bedf11c4e |
| institution | Directory Open Access Journal |
| issn | 2076-0760 |
| language | English |
| last_indexed | 2024-03-10T07:12:58Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Social Sciences |
| spelling | doaj.art-e75970e6cca840f5ab94629bedf11c4e2023-11-22T15:17:07ZengMDPI AGSocial Sciences2076-07602021-08-0110932210.3390/socsci10090322Enabling Artificial Intelligence Adoption through AssuranceLaura Freeman0Abdul Rahman1Feras A. Batarseh2Virginia Polytechnic Institute, State University (Virginia Tech), 900 N. Glebe Road, Arlington, VA 22203, USAVirginia Polytechnic Institute, State University (Virginia Tech), 900 N. Glebe Road, Arlington, VA 22203, USAVirginia Polytechnic Institute, State University (Virginia Tech), 900 N. Glebe Road, Arlington, VA 22203, USAThe wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair intelligent systems. AI assurance provides the necessary tools to enable AI adoption into applications, software, hardware, and complex systems. AI assurance involves quantifying capabilities and associating risks across deployments including: data quality to include inherent biases, algorithm performance, statistical errors, and algorithm trustworthiness and security. Data, algorithmic, and context/domain-specific factors may change over time and impact the ability of AI systems in delivering accurate outcomes. In this paper, we discuss the importance and different angles of AI assurance, and present a general framework that addresses its challenges.https://www.mdpi.com/2076-0760/10/9/322AI assurancedata qualityoperating envelopesvalidation and verificationXAIAI trustworthiness |
| spellingShingle | Laura Freeman Abdul Rahman Feras A. Batarseh Enabling Artificial Intelligence Adoption through Assurance Social Sciences AI assurance data quality operating envelopes validation and verification XAI AI trustworthiness |
| title | Enabling Artificial Intelligence Adoption through Assurance |
| title_full | Enabling Artificial Intelligence Adoption through Assurance |
| title_fullStr | Enabling Artificial Intelligence Adoption through Assurance |
| title_full_unstemmed | Enabling Artificial Intelligence Adoption through Assurance |
| title_short | Enabling Artificial Intelligence Adoption through Assurance |
| title_sort | enabling artificial intelligence adoption through assurance |
| topic | AI assurance data quality operating envelopes validation and verification XAI AI trustworthiness |
| url | https://www.mdpi.com/2076-0760/10/9/322 |
| work_keys_str_mv | AT laurafreeman enablingartificialintelligenceadoptionthroughassurance AT abdulrahman enablingartificialintelligenceadoptionthroughassurance AT ferasabatarseh enablingartificialintelligenceadoptionthroughassurance |