TY  - JOUR
AU  - Shao, Yinzi
AU  - Zhang, Bowen
PY  - 2025
DA  - 2025/09/13
TI  - The Accountability Paradox: How Generative AI Challenges Our Notions of Responsibility
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 2
IS  - 3
SP  - 169
EP  - 172
DO  - 10.62762/TETAI.2025.549572
UR  - https://www.icck.org/article/abs/TETAI.2025.549572
KW  - generative AI
KW  - algorithmic accountability
KW  - AI ethics
KW  - technology governance
KW  - legal innovation
AB  - The rapid advancement of generative AI has created a critical gap between technological innovation and accountability frameworks. Traditional responsibility mechanisms fail structurally when confronted with AI's black box nature, emergent behaviors, and capacity for autonomous decision-making—characteristics that sever the causal chains upon which legal and ethical liability depends. This Perspective argues that resolving this accountability paradox requires not a single regulatory fix but a distributed responsibility model spanning four interconnected domains: the proactive obligations of technology developers across the AI lifecycle, the paradigm shifts required in legal frameworks including algorithmic accountability and socialized compensation mechanisms, the transformation of users from passive recipients into active governance participants, and the construction of interoperable international governance capable of accommodating legitimate regulatory diversity. We contend that robust accountability frameworks are not constraints on innovation but prerequisites for sustainable AI development—and that their boundaries must be continuously renegotiated through the dynamic interplay of technological evolution, institutional adaptation, and collective human judgment.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Shao2025The,
  author = {Yinzi Shao and Bowen Zhang},
  title = {The Accountability Paradox: How Generative AI Challenges Our Notions of Responsibility},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2025},
  volume = {2},
  number = {3},
  pages = {169-172},
  doi = {10.62762/TETAI.2025.549572},
  url = {https://www.icck.org/article/abs/TETAI.2025.549572},
  abstract = {The rapid advancement of generative AI has created a critical gap between technological innovation and accountability frameworks. Traditional responsibility mechanisms fail structurally when confronted with AI's black box nature, emergent behaviors, and capacity for autonomous decision-making—characteristics that sever the causal chains upon which legal and ethical liability depends. This Perspective argues that resolving this accountability paradox requires not a single regulatory fix but a distributed responsibility model spanning four interconnected domains: the proactive obligations of technology developers across the AI lifecycle, the paradigm shifts required in legal frameworks including algorithmic accountability and socialized compensation mechanisms, the transformation of users from passive recipients into active governance participants, and the construction of interoperable international governance capable of accommodating legitimate regulatory diversity. We contend that robust accountability frameworks are not constraints on innovation but prerequisites for sustainable AI development—and that their boundaries must be continuously renegotiated through the dynamic interplay of technological evolution, institutional adaptation, and collective human judgment.},
  keywords = {generative AI, algorithmic accountability, AI ethics, technology governance, legal innovation},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}