Comments (8)
As I mentioned, I didn't do summaries up to now. I'll do them after I finish the assignments I have for this week.
from adila.
The summary is added.
from adila.
@Rounique
please explore their codeline, there are more good info :
https://perspectiveapi.com/how-it-works/
https://conversationai.github.io/
from adila.
2018-Reducing Gender Bias in Abusive Language Detection.pdf
from adila.
@Rounique
your summary?
from adila.
from adila.
@Rounique
Any update?
from adila.
Title: Reducing Gender Bias in Abusive Language Detection
Venue: EMNLP
Year: 2018
Introduction
As the use of social media and online platforms is increasing, people tend to share their ideas and words more and more. Here, automatic detection of abusive language plays an important role since abusive language can lead to cyber-bullying, personal trauma, hate crime, and discrimination. Therefore, using machine learning and NLP to automatically detect abusive language is useful for many websites or social media services.
In this paper, gender bias has been measured on models that are trained with abusive language datasets, and also some methods have been introduced for mitigating these biases. The bias measuring is done with a generated unbiased test set and the mitigating methods are: (1) debiased word embedding, (2) gender swap data augmentation, (3) fine-tuning with a larger corpus
Dataset:
Sexist Tweets, Abusive Tweets.
Measuring Gender Biases
It is not possible to measure gender bias on a dataset on which the model has been trained since it will follow the same biases. Therefore, it is necessary to generate an unbiased test set.
the test set generated in this work includes 1,152 samples (576 pairs) by filling the templates with common gender identity pairs (ex. male/female, man/woman, etc.). Some templates have been generated that contain both neutral and offensive nouns and adjectives inside the vocabulary to retain balance in neutral and abusive samples.
Mitigating Bias
Word Embeddings (DE)
This is an algorithm to correct word embeddings by removing gender-stereotypical information.
Gender Swap (GS)
What is basically done here is the identify male entities and swap them with equivalent female entities and vice-versa. This simple method removes the correlation between gender and classification decisions and has proven to be effective for correcting gender biases.
Bias fine-tuning (FT)
A method to use transfer learning from a less biased corpus to reduce the bias. Initially, a model is trained with a less-biased and larger source corpus and fine-tuned with a target corpus.
Metric used:
AUC
Conclusion
It is found that these proposed methods can reduce gender biases up to 90-98%, improving the robustness of the models.
Future Work
Increasing classification performance and reducing the bias at the same time.
Codes
https://github.com/conversationai/unintended-ml-bias-analysis
from adila.
Related Issues (20)
- The Epic Road to Fairness HOT 7
- Zero before/after NDKL for every input after code refactor HOT 7
- Summary of an Unsuccessful Attempt with Double-cutoff Method HOT 4
- equality of opportunity fairness criteria HOT 6
- Removing torch from Adila's pipeline HOT 5
- Key Error While Reranking with det_cons HOT 2
- 2017:CIKM: FA*IR: A Fair Top-k Ranking Algorithm HOT 4
- Bubble Plot for Fairness vs Utility HOT 10
- Cutoff for popular/nonpopular based on distribution diagram HOT 17
- IR Metrics Without a Cutoff HOT 2
- 2017:SSDBM:Measuring Fairness in Ranked Outputs
- Investigating gender fairness of recommendation algorithms in the music domain HOT 3
- pipeline progress flow HOT 2
- 2022:ACM: Fairness in Ranking, Part II: Learning-to-Rank and Recommender Systems (UNDER CONSTRUCTION)
- FA*IR Method for Neural Team Formation HOT 7
- 2022:KDD:Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking
- Discussion about Adding a Baseline to Adila HOT 7
- uspt gender labels and experiments HOT 4
- Gender Distribution For imdb, dblp and uspt Datasets.
- Exposure Metric
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from adila.