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Cited by 12Law, AI, and Intellectual PropertyDigitalization, Law, and RegulationIntellectual Property Law

The Role of Artificial Intelligence in Shaping Future Intellectual Property Law and Policy: Regulatory Challenges and Ethical Considerations

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Mbah, Geraldine O.·2024·International Journal of Research Publication and Reviews
DOI 10.55248/gengpi.5.1024.3123
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Abstract

The integration of artificial intelligence (AI) into intellectual property (IP) law is fundamentally reshaping the landscape of ownership rights, regulatory frameworks, and ethical considerations in automated IP generation and compliance.As AI technology evolves, it challenges traditional concepts of IP ownership, particularly in determining the eligibility of AI-generated content for copyright and patent protections and delineating ownership rights among human creators and AI developers.This paper explores the regulatory and ethical dimensions of AI's impact on IP law, addressing key issues such as ownership of AI-generated IP, the ethical implications of automated IP compliance and surveillance, and the evolving role of regulatory bodies in developing policies suited to AI's influence on IP.Ethical concerns emerge as AI systems take on roles in IP enforcement, potentially leading to privacy infringements, increased surveillance, and biased decision-making.These challenges demand attention to ensure that IP law aligns with principles of fairness, transparency, and accountability.Policymakers face the task of balancing innovation with robust protections, possibly requiring new legal frameworks to support IP rights effectively in an AIdominated landscape.This paper thus provides a forward-looking analysis of the interplay between AI and IP law, underscoring the necessity for proactive regulatory and ethical considerations to address AI's transformative role in IP management and policy.

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  • Bias arises in feature selection and model objectives: if a marketing algorithm is optimized purely for engagement or conversion, it might exploit whatever correlations exist in data, including those that coincide with sensitive attributes.

    “Bias also arises in feature selection and model objectives: if a marketing algorithm is optimized purely for engagement or conversion, it might exploit whatever correlations exist in data, including those that coincide with sensitive attributes (Mbah, 2024;Mutashar, 2024;Oguntibeju, 2024;Qwaider et al., 2024).”

    From Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence (2025) · cites this paper as Mbah, 2024

    Section 3.2.1, Algorithmic Bias and Fairness (page unavailable), says that biased training data can produce unfair outcomes for underrepresented groups. It does not address bias arising from feature selection or model objectives, or the proposed engagement- or conversion-optimized marketing algorithm. (LLM, conf=0.83)

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Research works printed but not linked: 28

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Authors
Mbah, G. O.
Affiliations
University of the Pacific (US)
Corresponding author
Mbah, Geraldine O.
Venue
International Journal of Research Publication and Reviews
Publisher
Genesis Global Publication
Published
2024-10
Volume
5
Issue
10
Pages
5023-5037
ISSN
2582-7421 (ISSN-L)
DOI
10.55248/gengpi.5.1024.3123
OpenAlex
W4404357935
Cited by
11
References
0
Citations by year
2026: 1 · 2025: 9 · 2024: 1
FWCI
1.63
Type
journal article
Language
en
Open access
Locations
International Journal of Research Publication and Reviews (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Law › Intellectual Property Law
Keywords
artificial intelligence, intellectual property law, AI-generated content, copyright, patent eligibility, ownership rights, surveillance, privacy violations, algorithmic bias, regulatory framework, AI ethics, transparency and accountability

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