10,668 research outputs found
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction
As algorithms are increasingly used to make important decisions that affect
human lives, ranging from social benefit assignment to predicting risk of
criminal recidivism, concerns have been raised about the fairness of
algorithmic decision making. Most prior works on algorithmic fairness
normatively prescribe how fair decisions ought to be made. In contrast, here,
we descriptively survey users for how they perceive and reason about fairness
in algorithmic decision making.
A key contribution of this work is the framework we propose to understand why
people perceive certain features as fair or unfair to be used in algorithms.
Our framework identifies eight properties of features, such as relevance,
volitionality and reliability, as latent considerations that inform people's
moral judgments about the fairness of feature use in decision-making
algorithms. We validate our framework through a series of scenario-based
surveys with 576 people. We find that, based on a person's assessment of the
eight latent properties of a feature in our exemplar scenario, we can
accurately (> 85%) predict if the person will judge the use of the feature as
fair.
Our findings have important implications. At a high-level, we show that
people's unfairness concerns are multi-dimensional and argue that future
studies need to address unfairness concerns beyond discrimination. At a
low-level, we find considerable disagreements in people's fairness judgments.
We identify root causes of the disagreements, and note possible pathways to
resolve them.Comment: To appear in the Proceedings of the Web Conference (WWW 2018). Code
available at https://fate-computing.mpi-sws.org/procedural_fairness
Exploring Graph Neural Networks for Indian Legal Judgment Prediction
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
ALJP: An Arabic Legal Judgment Prediction in Personal Status Cases Using Machine Learning Models
Legal Judgment Prediction (LJP) aims to predict judgment outcomes based on
case description. Several researchers have developed techniques to assist
potential clients by predicting the outcome in the legal profession. However,
none of the proposed techniques were implemented in Arabic, and only a few
attempts were implemented in English, Chinese, and Hindi. In this paper, we
develop a system that utilizes deep learning (DL) and natural language
processing (NLP) techniques to predict the judgment outcome from Arabic case
scripts, especially in cases of custody and annulment of marriage. This system
will assist judges and attorneys in improving their work and time efficiency
while reducing sentencing disparity. In addition, it will help litigants,
lawyers, and law students analyze the probable outcomes of any given case
before trial. We use a different machine and deep learning models such as
Support Vector Machine (SVM), Logistic regression (LR), Long Short Term Memory
(LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) using representation
techniques such as TF-IDF and word2vec on the developed dataset. Experimental
results demonstrate that compared with the five baseline methods, the SVM model
with word2vec and LR with TF-IDF achieve the highest accuracy of 88% and 78% in
predicting the judgment on custody cases and annulment of marriage,
respectively. Furthermore, the LR and SVM with word2vec and BiLSTM model with
TF-IDF achieved the highest accuracy of 88% and 69% in predicting the
probability of outcomes on custody cases and annulment of marriage,
respectively
Hybrid model using logit and nonparametric methods for predicting micro-entity failure
Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper
by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to
detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods
(Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as
either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and
Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method
implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic
variables complement financial ratios for bankruptcy prediction
Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction
Given the fact description text of a legal case, legal judgment prediction
(LJP) aims to predict the case's charge, law article and penalty term. A core
problem of LJP is how to distinguish confusing legal cases, where only subtle
text differences exist. Previous studies fail to distinguish different
classification errors with a standard cross-entropy classification loss, and
ignore the numbers in the fact description for predicting the term of penalty.
To tackle these issues, in this work, first, we propose a moco-based supervised
contrastive learning to learn distinguishable representations, and explore the
best strategy to construct positive example pairs to benefit all three subtasks
of LJP simultaneously. Second, in order to exploit the numbers in legal cases
for predicting the penalty terms of certain cases, we further enhance the
representation of the fact description with extracted crime amounts which are
encoded by a pre-trained numeracy model. Extensive experiments on public
benchmarks show that the proposed method achieves new state-of-the-art results,
especially on confusing legal cases. Ablation studies also demonstrate the
effectiveness of each component.Comment: Accepted to Findings of EMNLP 202
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