Towards Consumer-Empowering Artificial Intelligence
Authors: Giuseppe Contissa, Francesca Lagioia, Marco Lippi, Hans-Wolfgang Micklitz, Przemyslaw Palka, Giovanni Sartor, Paolo Torroni
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For instance, in [Lippi et al., 2018] we show a publicly available online system, named CLAUDETTE, for detecting potentially unfair clauses in online terms of service with over 80% precision and recall. |
| Researcher Affiliation | Academia | Giuseppe Contissa1,2, Francesca Lagioia2, Marco Lippi3, Hans-Wolfgang Micklitz2 Przemysław Pałka2, Giovanni Sartor1,2 and Paolo Torroni4 1 CIRSFID, Alma Mater Universit a di Bologna, Italy 2 Law Department, European University Institute, Florence, Italy 3 DISMI Universit a di Modena e Reggio Emilia, Italy 4 DISI, Alma Mater Universit a di Bologna, Italy |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions an online system named CLAUDETTE and provides a URL (http://claudette.eui.eu). However, this URL leads to a web demo of the system, not to its open-source code or a code repository as specified by the question criteria. |
| Open Datasets | No | The paper discusses data collection and mentions specific systems like CLAUDETTE, but it does not provide concrete access information (link, DOI, or specific citation with author/year for a dataset) that is publicly available or open for training purposes. |
| Dataset Splits | No | The paper does not provide specific details about dataset splits (e.g., training, validation, test percentages or counts) needed to reproduce experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions "IBM Watson technology" and the CLAUDETTE system, but it does not list any specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |