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Artificial Intelligence and Law
Erschienen in:

13.07.2022 | Original Research

A computational model of facilitation in online dispute resolution

verfasst von: Karl Branting, Sarah McLeod, Sarah Howell, Brandy Weiss, Brett Profitt, James Tanner, Ian Gross, David Shin

Erschienen in: Artificial Intelligence and Law | Ausgabe 3/2023

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Abstract

Online dispute resolution (ODR) is an alternative to traditional litigation that can both significantly reduce the disadvantages suffered by litigants unable to afford an attorney and greatly improve court efficiency and economy. An important aspect of many ODR systems is a facilitator, a neutral party who guides the disputants through the steps of reaching an agreement. However, insufficient availability of facilitators impedes broad adoption of ODR systems. This paper describes a novel model of facilitation that integrates two distinct but complementary knowledge sources: cognitive task analysis of facilitator behavior and corpus analysis of ODR session transcripts. This model is implemented in a decision-support system that (1) monitors cases to detect situations requiring immediate attention and (2) automates selection of standard text messages appropriate to the current state of the negotiations. This facilitation model has the potential to compensate for shortages of facilitators by improving the efficiency of experienced facilitators, assisting novice facilitators, and providing autonomous facilitation.
Vorheriger Artikel Patterns for legal compliance checking in a decidable framework of linked open data
Nächster Artikel The potential of an artificial intelligence (AI) application for the tax administration system’s modernization: the case of Indonesia

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Fußnoten
1
This work does not address the potential for inequality arising in the ODR context as a result of disparities in internet access or familiarity with web applications. ODR is just one among many technical innovations needed to equalize access to justice.
 
2
See Himonas and Hubbard (2020) at p.12.
 
3
In this subsection we use the following terminology: a case is an actual dispute initiated in an ODR system; a scenario is a pattern or sequence of events occurring in one or more cases; a script is a simulated case based on one or more scenarios.
 
4
The list of decision-support features as prioritized by facilitators in our study is set forth in Appendix A.
 
5
Figure 2 is based on the augmented transition network in Gardner (1987) page 121. A more contemporary formalization of contracts as deterministic finite automata is set forth in Flood and Goodenough (2021). Transition networks, particularly Hidden Markov Models, have a long history of use for modeling dialogues as sequences of transitions among dialogue states, e.g., Woszczyna and Waibel (1994).
 
6
The manual annotation process started with an initial meeting to review the taxonomy and collaboratively apply the taxonomy to two transcripts. The remaining cases were sampled to create a balanced distribution of the key case characteristics reported by facilitators during interviews: whether the case was settled; whether the case was forwarded for trial; and whether the case involved two individual citizens or instead involved an individual citizen and an organization or company (e.g., payday lenders). Cases were excluded if the number of messages was too short (less than 2) or too long (more than 75), if one or both parties did not speak English, or if the case was terminated from the ODR for some reason unrelated to the merits of the case or the actions of the disputants. Each annotator labeled an initial set of 12–15 transcripts from the sample, then reviewed the work of the other two annotators and marked any inconsistencies or disagreements. All annotators met several times thereafter to discuss these inconsistencies until a consensus was reached.
 
7
A set representative of disputant utterances were also annotated. However, since the focus of this work was on predicting facilitator decisions, the disputant annotations were used only for validation of the disputant feature calculation process described in Sect. 4.3.
 
8
We used the scikit-learn implementation of these algorithms (Pedregosa et al. 2011) with the following settings:
  • Random forest classifier: 100 estimators and a max depth of 20
  • CRF: L-BFGS gradient descent training algorithm with 100 max iterations and calculation of all possible states and all possible transitions
  • K-Nearest Neighbor classifier: \(\mathrm{k} = 5\), uniform weight function, and Euclidean distance.
The values for the max depth of the RF classifier and the max iterations for the CRF were selected through experimentation; the remaining values were the default values in sci-kit learn.
 
9
A representative collection of current systems is described in JTC (2020).
 
10
For an empirical study of the perception of unfairness in algorithmic mediation, see Lee and Baykal (2017).
 
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Metadaten
Titel
A computational model of facilitation in online dispute resolution
verfasst von
Karl Branting
Sarah McLeod
Sarah Howell
Brandy Weiss
Brett Profitt
James Tanner
Ian Gross
David Shin
Publikationsdatum
13.07.2022
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence and Law / Ausgabe 3/2023
Print ISSN: 0924-8463
Elektronische ISSN: 1572-8382
DOI
https://doi.org/10.1007/s10506-022-09318-7

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