چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده ثبت کننده مقاله | مریم زارع نهندی |
| مرحله جاری مقاله | تایید نهایی |
| دانشکده/مرکز مربوطه | دانشکده پزشکی |
| کد مقاله | 86975 |
| عنوان فارسی مقاله | تقویت نظارت اخلاقی در هوش مصنوعی: راهبردهایی برای شفافیت و اعتماد |
| عنوان لاتین مقاله | Enhancing Ethical Oversight in Artificial Intelligence: Strategies for Transparency and Trust |
| نوع ارائه | پوستر |
| عنوان کنگره / همایش | The First International Virtual Congress on the Application of Artificial Intelligence in Medical Sciences |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | Iran (Islamic Republic) |
| شهر محل برگزاری کنگره/ همایش | Tabriz |
| سال انتشار/ ارائه شمسی | 1403 |
| سال انتشار/ارائه میلادی | 2025 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1403/11/13 الی 1403/11/17 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | https://tabrizvai.ir/ |
| آدرس علمی (Affiliation) نویسنده متقاضی | Internal Medicine Department, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran |
| نویسنده | نفر چندم مقاله |
|---|---|
| مریم زارع نهندی | اول |
| علی بناگذارمحمدی | دوم |
| علی استادی | سوم |
| عنوان | متن |
|---|---|
| خلاصه مقاله | Introduction As artificial intelligence (AI) increasingly influences critical and complex decision-making, ensuring ethical oversight has become a major challenge. To guarantee that AI operates responsibly and ethically, innovative monitoring approaches are essential. Two prominent strategies that have recently garnered attention are Community-Based Oversight and AI Monitoring AI. These methods aim to enhance transparency and reduce biases in AI systems. Methodology The community-based oversight approach involves entrusting the monitoring of AI systems to users and the broader community. Users can report ethical concerns or biases within the systems, helping to improve their performance. This process is typically facilitated through platforms designed to gather direct user feedback. In contrast, the AI monitoring AI approach relies on a secondary AI system to oversee the primary system's performance. This monitoring AI automatically analyzes data and outcomes, identifying and flagging instances that conflict with ethical guidelines. Expected Outcomes Both approaches are expected to lead to greater transparency in AI decision-making processes, better identification and mitigation of biases, and increased public trust in these systems. Furthermore, implementing these strategies can assist organizations and governments in quickly detecting and addressing ethical issues. Conclusion Community-based oversight and AI monitoring AI are two promising methods for enhancing ethical oversight in artificial intelligence. By combining the strengths of human insight and technological precision, these approaches can effectively prevent ethical violations and build trust in AI for sensitive decision-making applications |
| کلمات کلیدی | Artificial Intelligence (AI) ; Ethical Oversight; Community-Based Oversight ; AI Monitoring AI |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| maryam zaare.pdf | 1403/12/07 | 26492 | دانلود |
| maryam zaree.png | 1403/12/07 | 2801590 | دانلود |
