Ethics and Responsible AI Deployment
- URL: http://arxiv.org/abs/2311.14705v1
- Date: Sun, 12 Nov 2023 13:32:46 GMT
- Title: Ethics and Responsible AI Deployment
- Authors: Petar Radanliev, Omar Santos
- Abstract summary: Article explores the need for ethical AI systems that safeguard individual privacy while complying with ethical standards.
Research examines innovative algorithmic techniques such as differential privacy, homomorphic encryption, federated learning, international regulatory frameworks, and ethical guidelines.
- Score: 1.3597551064547502
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As Artificial Intelligence (AI) becomes more prevalent, protecting personal
privacy is a critical ethical issue that must be addressed. This article
explores the need for ethical AI systems that safeguard individual privacy
while complying with ethical standards. By taking a multidisciplinary approach,
the research examines innovative algorithmic techniques such as differential
privacy, homomorphic encryption, federated learning, international regulatory
frameworks, and ethical guidelines. The study concludes that these algorithms
effectively enhance privacy protection while balancing the utility of AI with
the need to protect personal data. The article emphasises the importance of a
comprehensive approach that combines technological innovation with ethical and
regulatory strategies to harness the power of AI in a way that respects and
protects individual privacy.
Related papers
- Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives [0.0]
This study explores stakeholder perspectives on privacy in AI systems, focusing on educators, parents, and AI professionals.
Using qualitative analysis of survey responses from 227 participants, the research identifies key privacy risks, including data breaches, ethical misuse, and excessive data collection.
The findings provide actionable insights into balancing the benefits of AI with robust privacy protections.
arXiv Detail & Related papers (2025-01-23T02:06:25Z) - Securing the AI Frontier: Urgent Ethical and Regulatory Imperatives for AI-Driven Cybersecurity [0.0]
This paper critically examines the evolving ethical and regulatory challenges posed by the integration of artificial intelligence in cybersecurity.
We trace the historical development of AI regulation, highlighting major milestones from theoretical discussions in the 1940s to the implementation of recent global frameworks such as the European Union AI Act.
Ethical concerns such as bias, transparency, accountability, privacy, and human oversight are explored in depth, along with their implications for AI-driven cybersecurity systems.
arXiv Detail & Related papers (2025-01-15T18:17:37Z) - Implications of Artificial Intelligence on Health Data Privacy and Confidentiality [0.0]
The rapid integration of artificial intelligence in healthcare is revolutionizing medical diagnostics, personalized medicine, and operational efficiency.
However, significant challenges arise concerning patient data privacy, ethical considerations, and regulatory compliance.
This paper examines the dual impact of AI on healthcare, highlighting its transformative potential and the critical need for safeguarding sensitive health information.
arXiv Detail & Related papers (2025-01-03T05:17:23Z) - Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground [55.2480439325792]
I argue that efforts to promote responsible and ethical AI can inadvertently contribute to and seemingly legitimize this disregard for established contextual norms.
I question the current narrow prioritization in AI ethics of moral innovation over moral preservation.
arXiv Detail & Related papers (2024-12-06T15:36:13Z) - From Principles to Practice: A Deep Dive into AI Ethics and Regulations [13.753819576072127]
The article thoroughly analyzes the ground-breaking AI regulatory framework proposed by the European Union.
Considering the technical efforts and strategies undertaken by academics and industry to uphold these principles, we explore the synergies and conflicts among the five ethical principles.
arXiv Detail & Related papers (2024-12-06T00:46:20Z) - Towards A Unified Utilitarian Ethics Framework for Healthcare Artificial
Intelligence [0.08192907805418582]
This study attempts to identify the major ethical principles influencing the utility performance of AI at different technological levels.
Justice, privacy, bias, lack of regulations, risks, and interpretability are the most important principles to consider for ethical AI.
We propose a new utilitarian ethics-based theoretical framework for designing ethical AI for the healthcare domain.
arXiv Detail & Related papers (2023-09-26T02:10:58Z) - Fairness in Agreement With European Values: An Interdisciplinary
Perspective on AI Regulation [61.77881142275982]
This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them.
We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives.
We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
arXiv Detail & Related papers (2022-06-08T12:32:08Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Trustworthy AI Inference Systems: An Industry Research View [58.000323504158054]
We provide an industry research view for approaching the design, deployment, and operation of trustworthy AI inference systems.
We highlight opportunities and challenges in AI systems using trusted execution environments.
We outline areas of further development that require the global collective attention of industry, academia, and government researchers.
arXiv Detail & Related papers (2020-08-10T23:05:55Z) - More Than Privacy: Applying Differential Privacy in Key Areas of
Artificial Intelligence [62.3133247463974]
We show that differential privacy can do more than just privacy preservation in AI.
It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI.
arXiv Detail & Related papers (2020-08-05T03:07:36Z) - Hacia los Comit\'es de \'Etica en Inteligencia Artificial [68.8204255655161]
It is priority to create the rules and specialized organizations that can oversight the following of such rules.
This work proposes the creation, at the universities, of Ethical Committees or Commissions specialized on Artificial Intelligence.
arXiv Detail & Related papers (2020-02-11T23:48:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.