AI-based judicial assistance and case prediction have been extensively studied in criminal and civil domains, but remain largely unexplored in consumer law, especially in India. In this paper, we present Nyay-Darpan, a novel two-in-one framework that (i) summarizes consumer case files and (ii) retrieves similar case judgements to aid decision-making in consumer dispute resolution. Our methodology not only addresses the gap in consumer law AI tools but also introduces an innovative approach to evaluate the quality of the summary. The term 'Nyay-Darpan' translates into 'Mirror of Justice', symbolizing the ability of our tool to reflect the core of consumer disputes through precise summarization and intelligent case retrieval. Our system achieves over 75 percent accuracy in similar case prediction and approximately 70 percent accuracy across material summary evaluation metrics, demonstrating its practical effectiveness. We will publicly release the Nyay-Darpan framework and dataset to promote reproducibility and facilitate further research in this underexplored yet impactful domain.

Users increasingly query LLM-enabled web chatbots for help with scam defense. The Consumer Financial Protection Bureau's complaints database is a rich data source for evaluating LLM performance on user scam queries, but currently the corpus does not distinguish between scam and non-scam fraud. We developed an LLM ensemble approach to distinguishing scam and fraud CFPB complaints and describe initial findings regarding the strengths and weaknesses of LLMs in the scam defense context.

Causal reasoning, in particular, counterfactual reasoning plays a central role in testing for discrimination. Counterfactual reasoning materializes when testing for discrimination, what is known as the counterfactual model of discrimination, when we compare the discrimination comparator with the discrimination complainant, where the comparator is a similar (or similarly situated) profile to that of the complainant used for testing the discrimination claim of the complainant. In this paper, we revisit the comparator by presenting two kinds of comparators based on the sort of causal intervention we want to represent. We present the ceteris paribus and the mutatis mutandis comparator, where the former is the standard and the latter is a new kind of comparator. We argue for the use of the mutatis mutandis comparator, which is built on the fairness given the difference notion, for testing future algorithmic discrimination cases.

We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalised regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes, topic-word distributions which can be manually-interpreted to gain insights about significant topics, and case-topic weights which can be used to identify representative cases for each topic. We demonstrate the method on a new dataset of domain name disputes and a canonical dataset of European Court of Human Rights violation cases. Topic models based on latent semantic analysis as well as language model embeddings are evaluated. We show that topics derived by the pipeline are consistent with legal doctrines in both areas and can be useful in other related legal analysis tasks.

We present counterfactual situation testing (CST), a causal data mining framework for detecting discrimination in classifiers. CST aims to answer in an actionable and meaningful way the intuitive question "what would have been the model outcome had the individual, or complainant, been of a different protected status?" It extends the legally-grounded situation testing of Thanh et al. (2011) by operationalizing the notion of fairness given the difference using counterfactual reasoning. For any complainant, we find and compare similar protected and non-protected instances in the dataset used by the classifier to construct a control and test group, where a difference between the decision outcomes of the two groups implies potential individual discrimination. Unlike situation testing, which builds both groups around the complainant, we build the test group on the complainant's counterfactual generated using causal knowledge. The counterfactual is intended to reflect how the protected attribute when changed affects the seemingly neutral attributes used by the classifier, which is taken for granted in many frameworks for discrimination. Under CST, we compare similar individuals within each group but dissimilar individuals across both groups due to the possible difference between the complainant and its counterfactual. Evaluating our framework on two classification scenarios, we show that it uncovers a greater number of cases than situation testing, even when the classifier satisfies the counterfactual fairness condition of Kusner et al. (2017).

We develop computational models to analyze court statements in order to assess judicial attitudes toward victims of sexual violence in the Israeli court system. The study examines the resonance of "rape myths" in the criminal justice system's response to sex crimes, in particular in judicial assessment of victim's credibility. We begin by formulating an ontology for evaluating judicial attitudes toward victim's credibility, with eight ordinal labels and binary categorizations. Second, we curate a manually annotated dataset for judicial assessments of victim's credibility in the Hebrew language, as well as a model that can extract credibility labels from court cases. The dataset consists of 855 verdict decision documents in sexual assault cases from 1990-2021, annotated with the help of legal experts and trained law students. The model uses a combined approach of syntactic and latent structures to find sentences that convey the judge's attitude towards the victim and classify them according to the credibility label set. Our ontology, data, and models will be made available upon request, in the hope they spur future progress in this judicial important task.

Lauren Peace talks with attorney Sharon Stiller and Michelle Cammarata of Restore Sexual Assault services about how to identify instances of sexual harassment and what to do about it. Since the allegations surfaced publicly in early September against psycholinguistics professor T. Florian Jaeger, 41, the number of alleged victims has grown to 14, according to managing partner Jef McAllister of McAllister Olivarius law firm, the complainants' legal team. Psycholinguistics is the study of the psychological and neuroscientific processes that allow people to use and understand language, and Jaeger was at the forefront of that research. Sept. 14: What you need to know about university's sexual harassment case Sept. 13: Student criticism on handling of sex harassment allegations mounts Sept. 11: Clearing of prof accused of sex harassment focus of faculty complaint The private university itself is accused of protecting Jaeger, even going so far as to retaliate against those who complained about his behavior before relief was sought from the federal Equal Employment Opportunity Commission. The two who wrote a letter to the university Board of Trustees have been past department chairpersons who have worked a total of 57 years for the University of Rochester.

We propose a mechanism to learn communicative action structure to analyze adversarial reasoning patterns in customer complaints. An efficient way to assist customers and companies is to reuse previous experience with similar agents. A formal representation of customer complaints and a machine learning technique for handling scenarios of interaction between conflicting human agents are proposed. It is shown that analyzing the structure of communicative actions without context information is frequently sufficient to advise on complaint resolution strategies. Therefore, being domain-independent, the proposed machine learning technique is a good complement to a wide range of customer response management applications where formal treatment of inter-human interactions is required.