{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:47:11Z","timestamp":1760575631683,"version":"build-2065373602"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIES"],"abstract":"<jats:p>Software systems increasingly mediate critical societal\nfunctions involving large-scale use of data. The use of\npersonal and sensitive data introduces ethical and legal\nconcerns, necessitating architectures that support\nResponsible AI and enforce open access safeguards grounded\nin ethical principles. These principles are conventionally\nderived through laws, regulations, codes of conduct, and\nframeworks.\nMuch of the focus in responsible AI has been on privacy\nalone. However, there can be other sensitive information in\nthe enterprise and public domain that needs to be handled\nin a responsible and ethical manner as well. Ethical\ncompliance traditionally has been achieved through\nsystematic manual adherence to guidelines, regulations, and\nother documentation. However, there is a lack of concrete\nsoftware architectures, frameworks, or tools that can\nprovide automation to enable compliance directly in\nsoftware systems.\nThis paper introduces Guardrail Framework, a modular,\nreusable software framework that operationalizes\nresponsible AI principles by decoupling ethical constraints\nfrom the functional requirements of software applications.\nThe proposed novel framework addresses the issue of ethical\ncompliance in application software by creating a robust\nsoftware framework that can be plugged into multiple\napplication software development environments. The\nframework allows ethical filtering of information on the\nbasis of data sensitivity (Low to High), the trust score of\nthe user (Low to High), and granularity of the data (Cell,\nRow, Column, or Table). The proposed Guardrail framework is\nimplemented and evaluated using Open Government Dataset\n(OGD), demonstrating high cohesion, low coupling, and\nlong-term maintainability, in line with fundamental\nsoftware engineering architectural design principles.\nNotably, the proposed framework is equally effective for\nensuring ethical use of personal, enterprise, and public\ndata.<\/jats:p>","DOI":"10.1609\/aies.v8i2.36650","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T13:21:38Z","timestamp":1760534498000},"page":"1513-1523","source":"Crossref","is-referenced-by-count":0,"title":["GRAILS - A Framework for Embedding Ethical Safeguards in Software Applications for Responsible AI"],"prefix":"10.1609","volume":"8","author":[{"given":"Apurva","family":"Kulkarni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chandrashekar","family":"Ramanathan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2025,10,15]]},"container-title":["Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIES\/article\/download\/36650\/38788","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIES\/article\/download\/36650\/38788","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T13:21:38Z","timestamp":1760534498000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIES\/article\/view\/36650"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,10,15]]}},"URL":"https:\/\/doi.org\/10.1609\/aies.v8i2.36650","relation":{},"ISSN":["3065-8365"],"issn-type":[{"value":"3065-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}