Abstract

Machine Learning models face increased concerns regarding the storage of personal user data and adverse impacts of corrupted data like backdoors or systematic bias. Machine Unlearning can address these by allowing post-hoc deletion of affected training data from a learned model. Achieving this task exactly is computationally expensive; consequently, recent works have proposed inexact unlearning algorithms to solve this approximately as well as evaluation methods to test the effectiveness of these algorithms. In this work, we first outline some necessary criteria for evaluation methods and show no existing evaluation satisfies them all. Then, we design a stronger black-box evaluation method called the Interclass Confusion (IC) test which adversarially manipulates data during training to detect the insufficiency of unlearning procedures. We also propose two analytically motivated baseline methods~(EU-k and CF-k) which outperform several popular inexact unlearning methods. Overall, we demonstrate how adversarial evaluation strategies can help in analyzing various unlearning phenomena which can guide the development of stronger unlearning algorithms.

Abstract

Prior work has shown that pretrained language models often make incorrect predictions for negated inputs. The reason for this behaviour has remained unclear. It has been argued that since language models (LMs) don’t change their predictions about factual propositions under negation, they might not detect negation. We show encoder LMs do detect negation as their representations across layers reliably distinguish negated inputs from non-negated inputs, and when negation leads to contradictions. However, probing experiments show that these LMs indeed don’t use negation when evaluating whether a factual statement is true, even when fine-tuned with the objective of changing outputs on negated sentences (Hosseini et al., 2021). We hypothesize about why pretrained LMs are inconsistent under negation: when the statement could refer to multiple ground entities with conflicting properties, negation may not entail a change in output. This means negation minimal pairs in different training samples can have the same completion in pretraining corpora. We argue pretraining may not provide enough signal to learn the distribution of ground referents a token could have, confusing the LM on how to handle negation.

Abstract

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Abstract

Social media provides a supportive and anonymous environment for discussing mental health issues, including depression. Existing research on identifying the cause of depression focuses primarily on improving classifier models, while neglecting the importance of learning better data representations. To address this gap, we introduce an architecture that enhances the identification of the cause of depression by learning improved data representations. Our work enables a deeper interpretation of the cause of depression in social media contexts, emphasizing the significance of effective rep- resentation learning for this task. Our work can act as a foundation for self-help applications in the field of mental health.

Abstract

The default approach to deal with the enormous size and lim- ited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnet- works will crucially depend on the sampling scheme. Mo- tivated by studies of homophily and opinion formation, we propose a variant of snowball sampling designed to priori- tize inclusion of entire cohesive communities rather than any kind of representativeness, breadth, or depth of coverage. The method is illustrated on a concrete example, and experiments on synthetic networks suggest that it behaves as desired.

Abstract

The burdensome impact of a skewed judges-to-cases ratio on the judicial system manifests in an overwhelming backlog of pending cases alongside an ongoing influx of new ones. To tackle this issue and expedite the judicial process, the proposition of an automated system capable of suggesting case outcomes based on factual evidence and precedent from past cases gains significance. This research paper centres on developing a graph neural network-based model to address the Legal Judgment Prediction (LJP) problem, recognizing the intrinsic graph structure of judicial cases and making it a binary node classification problem. We explored various embeddings as model features, while nodes such as time nodes and judicial acts were added and pruned to evaluate the model's performance. The study is done while considering the ethical dimension of fairness in these predictions, considering gender and name biases. A link prediction task is also conducted to assess the model's proficiency in anticipating connections between two specified nodes. By harnessing the capabilities of graph neural networks and incorporating fairness analyses, this research aims to contribute insights towards streamlining the adjudication process, enhancing judicial efficiency, and fostering a more equitable legal landscape, ultimately alleviating the strain imposed by mounting case backlogs. Our best-performing model with XLNet pre-trained embeddings as its features gives the macro F1 score of 75% for the LJP task. For link prediction, the same set of features is the best performing giving ROC of more than 80%

Abstract

Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors’ posts on the Internet. Research on ID becomes important when authors’ written online communication pertains to sensitive domains, e.g., mental health. Over time, researchers have utilized AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing content. However, these tools fail to satisfy the purpose of ID as their paraphrased content still leads to the source when queried on search engines. There is limited prior work on judging the effectiveness of paraphrasing methods for ID on search engines or

Abstract

Recent advances and applications of language technology and artificial intelligence have enabled much success across multiple domains like law, medical and mental health. AI-based Language Models, like Judgement Prediction, have recently been proposed for the legal sector. However, these models are strife with encoded social biases picked up from the training data. While bias and fairness have been studied across NLP, most studies primarily locate themselves within a Western context. In this work, we present an initial investigation of fairness from the Indian perspective in the legal domain. We highlight the propagation of learnt algorithmic biases in the bail prediction task for models trained on Hindi legal documents. We evaluate the fairness gap using demographic parity and show that a decision tree model trained for the bail prediction task has an overall fairness disparity of 0.237 between input features associated with Hindus and Muslims. Additionally, we highlight the need for further research and studies in the avenues of fairness/bias in applying AI in the legal sector with a specific focus on the Indian context.

Abstract

Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors’ posts on the Internet. Research on ID becomes important when authors’ written online communication pertains to sensitive domains, e.g., mental health. Over time, researchers have utilized AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing content. However, these tools fail to satisfy the purpose of ID as their paraphrased content still leads to the source when queried on search engines. There is limited prior work on judging the effectiveness of paraphrasing methods for ID on search engines or their proxies, neural retriever (NeurIR) models. We propose a framework where, for a given sentence from an author’s post, we perform iterative perturbation on the sentence in the direction of paraphrasing with an attempt to confuse the search mechanism of a NeurIR system when the sentence is queried on it. Our experiments involve the subreddit “r/AmItheAsshole” as the source of public content and Dense Passage Retriever as a NeurIR system-based proxy for search engines. Our work introduces a novel method of phrase-importance rankings using perplexity scores and involves multi-level phrase substitutions via beam search. Our multi-phrase substitution scheme succeeds in disguising sentences 82% of the time and hence takes an essential step towards enabling researchers to disguise sensitive content effectively before making it public. We also release the code of our approach

Abstract

E-commerce features like easy cancellations, returns, and refunds can be exploited by bad actors or uninformed customers, leading to revenue loss for organization. One such problem faced by e-commerce platforms is Return To Origin (RTO), where the user cancels an order while it is in transit for delivery. In such a scenario platform faces logistics and opportunity costs. Traditionally, models trained on historical trends are used to predict the propensity of an order becoming RTO. Sociology literature has highlighted clear correlations between socio-economic indicators and users’ tendency to exploit systems to gain financial advantage. Social media profiles have information about location, education, and profession which have been shown to be an estimator of socio-economic condition. We believe combining social media data with e-commerce information can lead to improvements in a variety of tasks like RTO …