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on Contract Theory and Applications |
| By: | Dae-Hyun Yoo; Caterina Giannetti |
| Abstract: | This paper presents a principal-agent model for aligning artificial intelligence (AI) behaviors with human ethical objectives. In this framework, the end-user acts as the principal, offering a contract to the system developer (the agent) that specifies desired ethical alignment levels for the AI system. This incentivizes the developer to align the AI’s objectives with ethical considerations, fostering trust and collaboration. When ethical alignment is unobservable and the developer is risk-neutral, the optimal contract achieves the same alignment and expected utilities as when it is observable. For observable alignment levels, a fixed reward is uniquely optimal for strictly risk-averse developers, while for risk-neutral developers, a fixed reward is one of several optimal options. Our findings demonstrate that even a basic principal-agent model can enhance the understanding of how to balance responsibility between users and developers in the pursuit of ethical AI. Users seeking higher ethical alignment must compensate developers appropriately, and they also share responsibility for ethical AI by adhering to design specifications and regulations. |
| Keywords: | AI Ethics, Ethical Alignment, Principal-Agent Model, Contract Theory, Responsibility Allocation, Economic Incentives |
| JEL: | D82 D86 O33 |
| Date: | 2024–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:pie:dsedps:2024/313 |
| By: | Simon Dohn; Kristoffer Arnsfelt Hansen; Asger Klinkby |
| Abstract: | We study computational problems in financial networks of banks connected by debt contracts and credit default swaps (CDSs). A main problem is to determine \emph{clearing} payments, for instance right after some banks have been exposed to a financial shock. Previous works have shown the $\varepsilon$-approximate version of the problem to be $\mathrm{PPAD}$-complete and the exact problem $\mathrm{FIXP}$-complete. We show that $\mathrm{PPAD}$-hardness hold when $\varepsilon \approx 0.101$, improving the previously best bound significantly. Due to the fact that the clearing problem typically does not have a unique solution, or that it may not have a solution at all in the presence of default costs, several natural decision problems are also of great interest. We show two such problems to be $\exists\mathbb{R}$-complete, complementing previous $\mathrm{NP}$-hardness results for the approximate setting. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.18717 |