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Found 25 papers, 1 papers with code
Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach
Online reviews are an essential aspect of online shopping for both customers and retailers.
AI Safety: Necessary, but insufficient and possibly problematic
This article critically examines the recent hype around AI safety.
AI and Core Electoral Processes: Mapping the Horizons
In this paper, we consider five representative avenues within the core electoral process which have potential for AI usage, and map the challenges involved in using AI within them.
Cluster-level Group Representativity Fairness in $k$-means Clustering
There has been much interest recently in developing fair clustering algorithms that seek to do justice to the representation of groups defined along sensitive attributes such as race and gender.
Gender Bias in Fake News: An Analysis
Data science research into fake news has gathered much momentum in recent years, arguably facilitated by the emergence of large public benchmark datasets.
Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers
From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP).
Exploring Rawlsian Fairness for K-Means Clustering
Our focus is on the task of clustering, specifically the k-means clustering algorithm.
FiSH: Fair Spatial Hotspots
Pervasiveness of tracking devices and enhanced availability of spatially located data has deepened interest in using them for various policy interventions, through computational data analysis tasks such as spatial hot spot detection.
Exploring Thematic Coherence in Fake News
The spread of fake news remains a serious global issue; understanding and curtailing it is paramount.
ReSCo-CC: Unsupervised Identification of Key Disinformation Sentences
Disinformation is often presented in long textual articles, especially when it relates to domains such as health, often seen in relation to COVID-19.
Representativity Fairness in Clustering
We illustrate the importance of representativity fairness in real-world decision making scenarios involving clustering and provide ways of quantifying objects' representativity and fairness over it.
Local Connectivity in Centroid Clustering
Clustering is a fundamental task in unsupervised learning, one that targets to group a dataset into clusters of similar objects.
Whither Fair Clustering?
Within the relatively busy area of fair machine learning that has been dominated by classification fairness research, fairness in clustering has started to see some recent attention.
Fair Outlier Detection
Being a novel task, we develop an evaluation framework for fair outlier detection, and use that to benchmark FairLOF on quality and fairness of results.
Unsupervised Separation of Native and Loanwords for Malayalam and Telugu
Quite often, words from one language are adopted within a different language without translation; these words appear in transliterated form in text written in the latter language.
Fairness in Clustering with Multiple Sensitive Attributes
Our experimental evaluation illustrates that the clusters generated by FairKM fare significantly better on both clustering quality and fair representation of sensitive attribute groups compared to the clusters from a state-of-the-art baseline fair clustering method.
Sentiment and Emotion Based Representations for Fake Reviews Detection
They can be unethical as well as harmful.
On the Coherence of Fake News Articles
The generation and spread of fake news within new and online media sources is emerging as a phenomenon of high societal significance.
Warping Resilient Scalable Anomaly Detection in Time Series
These two challenges make it hard to detect anomalies in time series as often such warpings could get detected as anomalies erroneously.
Topic-Specific Sentiment Analysis Can Help Identify Political Ideology
Ideological leanings of an individual can often be gauged by the sentiment one expresses about different issues.
Unsupervised Separation of Transliterable and Native Words for Malayalam
Differentiating intrinsic language words from transliterable words is a key step aiding text processing tasks involving different natural languages.
Latent Space Embedding for Retrieval in Question-Answer Archives
The idea is that such a space mirrors semantic similarity among questions as well as answers, thereby enabling high quality retrieval.
