 Online dispute resolution: can we leave the initial decision to Large Language Models (LLM)?Mario Ferrer-Benítez Metaverse Basic and Applied Research, 2022, vol. 1, 23 Abstract: In the era of digitization and artificial intelligence, online dispute resolution has become a topic of growing interest. In this article, we will explore the potential of Large Language Models (LLM) in online dispute resolution, how they can be implemented, the necessary technological resources, as well as their limitations and challenges. LLMs have the ability to process and analyze large volumes of data in a short period of time. This allows them to evaluate many indicators, criteria, and parameters, something that could take a long time for human judges or experts. This speed and efficiency can be particularly useful in cases involving a large number of documents, such as contracts, expert reports, and others. To implement LLMs in online dispute resolution, adequate technological resources are needed. One of the main challenges is ensuring the security and privacy of the data processed by these models. To do this, the use of technologies such as blockchain can be of great help, as it allows for the creation of secure, decentralized, and unalterable records of transactions and decisions made during the dispute resolution process. LLMs are promising tools for online dispute resolution, but it is important to recognize their limitations and challenges. Although they can offer greater efficiency and agility in the analysis of legal cases, they should not be used as substitutes for human legal professionals. Instead, LLMs should be considered as complementary tools, which can enhance and enrich the decision-making process in legal cases. By responsibly and ethically implementing LLMs in online dispute resolution, and proactively addressing the risks of bias and partiality, these tools can provide great value in the legal field and improve accessibility to justice for all. Date: 2022 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:dbk:metave:v:1:y:2022:i::p:23:id:23 DOI: 10.56294/mr202223 Access Statistics for this article More articles in Metaverse Basic and Applied Research from AG Editor |
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