• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

Researchers find that debiasing doesn’t eliminate racism from hate speech detection models

February 6, 2021   Big Data

Current AI hate speech and toxic language detection systems exhibit problematic and discriminatory behavior, research has shown. At the core of the issue are training data biases, which often arise during the dataset creation process. When trained on biased datasets, models acquire and exacerbate biases, for example flagging text by Black authors as more toxic than text by white authors.

Toxicity detection systems are employed by a range of online platforms including Facebook, Twitter, YouTube, and various publications. While one of the premiere providers of these systems, Alphabet-owned Jigsaw, claims it’s taken pains to remove bias from its models following a study showing it fared poorly on Black-authored speech, it’s unclear the extent to which this might be true of other AI-powered solutions.

To see whether current model debiasing approaches can mitigate biases in toxic language detection, researchers at the Allen Institute investigated techniques to address lexical and dialectal imbalances in datasets. Lexical biases associate toxicity with the presence of certain words, like profanities, while dialectal biases correlate toxicity with “markers” of language variants like African-American English (AAE).

 Researchers find that debiasing doesn’t eliminate racism from hate speech detection models

In the course of their work, the researchers looked at one debiasing method designed to tackle “predefined biases” (e.g., lexical and dialectal). They also explored a process that filters “easy” training examples with correlations that might mislead a hate speech detection model.

According to the researchers, both approaches face challenges in mitigating biases from a model trained on a biased dataset for toxic language detection. In their experiments, while filtering reduced bias in the data, models trained on filtered datasets still picked up lexical and dialectal biases. Even “debiased” models disproportionately flagged text in certain snippets as toxic. Perhaps more discouragingly, mitigating dialectal bias didn’t appear to change a model’s propensity to label text by Black authors as more toxic than white authors.

In the interest of thoroughness, the researchers embarked on a proof-of-concept study involving relabeling examples of supposedly toxic text whose translations from AAE to “white-aligned English” were deemed nontoxic. They used OpenAI’s GPT-3 to perform the translations and create a synthetic dataset — a dataset, they say, that resulted in a model less prone to dialectal and racial biases.

 Researchers find that debiasing doesn’t eliminate racism from hate speech detection models

“Overall, our findings indicate that debiasing a model already trained on biased toxic language data can be challenging,” wrote the researchers, who caution against deploying their proof-of-concept approach because of its limitations and ethical implications. “Translating” the language a Black person might use into the language a white person might use both robs the original language of its richness and makes potentially racist assumptions about both parties. Moreover, the researchers note that GPT-3 likely wasn’t exposed to many African American English varieties during training, making it ill-suited for this purpose.

“Our findings suggest that instead of solely relying on development of automatic debiasing for existing, imperfect datasets, future work focus primarily on the quality of the underlying data for hate speech detection, such as accounting for speaker identity and dialect,” the researchers wrote. “Indeed, such efforts could act as an important step towards making systems less discriminatory, and hence safe and usable.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform
  • networking features, and more

Become a member

Let’s block ads! (Why?)

Big Data – VentureBeat

debiasing, Detection, Doesn’t, Eliminate, Find, from, hate, Models, racism, researchers, SPEECH
  • Recent Posts

    • ANOTHER SIMPLE EXAMPLE OF FASCIST NAZI LEFTISTS AT WORK
    • Nvidia and Harvard develop AI tool that speeds up genome analysis
    • Export with large E instead of small e
    • You’ll be back
    • Building AI for the Global South
  • Categories

  • Archives

    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited