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Towards a Transparency-Based, Value-Sensitive Design Solution for Bias in Self-driving Cars: An Ethical Violation Assessment and Risk Analysis Framework on Consumer-Held Values /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Towards a Transparency-Based, Value-Sensitive Design Solution for Bias in Self-driving Cars: An Ethical Violation Assessment and Risk Analysis Framework on Consumer-Held Values // Nada Ahmad Madkour.
作者:	Madkour, Nada Ahmad,
面頁冊數:	1 electronic resource (201 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
標題:	Computer engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31558114
ISBN:	9798384054009

Towards a Transparency-Based, Value-Sensitive Design Solution for Bias in Self-driving Cars: An Ethical Violation Assessment and Risk Analysis Framework on Consumer-Held Values /
Madkour, Nada Ahmad,

Towards a Transparency-Based, Value-Sensitive Design Solution for Bias in Self-driving Cars: An Ethical Violation Assessment and Risk Analysis Framework on Consumer-Held Values /Nada Ahmad Madkour. - 1 electronic resource (201 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Background: The rapid growth of automated systems and artificial intelligence (AI), particularly, self-driving cars (SDCs), has attracted significant investments and can potentially contribute to humanity's flourishing. However, before widespread adoption, it is important to address ethical violations such as bias in AI, highlighted by many real-world cases of bias in AI leading to unfair outcomes in tools like facial recognition, hiring software, and pedestrian detection. Bias in AI can lead to potentially fatal outcomes in SDCs, emphasizing the need for a thorough examination of bias in SDCs. Purpose: To enhance AI ethics by providing tools to support transparency and value-sensitive design in self-driving cars (SDCs). Methods: The four-methodology framework (a) an AI value-mapping database, (b) a consumer values and SDC acceptance survey, (c) an ethical violation analysis and risk assessment (EVARA), and (d) a demonstration of the open ethics data passport for AI bias mitigation in SDCs. Findings: The study's findings indicate that human welfare, universal usability, and trust were the prevalent values in the AIVMDB filtering analysis case study output, and significantly influenced SDC acceptance. Human welfare was highlighted as a critical value in the EVARA case study, with its violation posing a high risk. While the OEDP is not fully demonstrated in a case study, its elements were explained to showcase its potential utility and application and revealed patterns and areas of potential mitigation.Contribution: The results of the AIVMDB, survey, EVARA, and OEDP analysis revealed concerning patterns in machine learning (ML) model training data labeling and gathering practices that can be easily identified and potentially mitigated.Conclusions: Aligning AI systems like SDCs with human values is achievable but requires careful attention. Identifying and mitigating ethical issues in ML model training data is crucial, as these mistakes can have severe consequences. Collaborative efforts are needed to ensure AI's positive impact on society while minimizing potential harm.

English

ISBN: 9798384054009Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

AI ethics

Towards a Transparency-Based, Value-Sensitive Design Solution for Bias in Self-driving Cars: An Ethical Violation Assessment and Risk Analysis Framework on Consumer-Held Values /
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