“Pekka”s are 260 times more likely to be an engineer than “Emilia”s! And “Tiia”s are 410 times more likely to love shopping than “Matti”s!

Possible Gender Bias in Finnish BERT? ====================

The purpose of this mini-experiment is to:

• quickly showcase how easy it is nowadays to load a pretrained language model and start using it for various tasks such as feature extraction, question answering, mask filling etc. in just a couple of lines
• thank all the researchers and practitioners that made the abovementioned point possible
• raise awareness to possible gender bias / stereotypical bias and other types of biases in these models
• open up a constructive discussion around this topic and consequently learn from each other as a community
• (hopefully) help the pursuit of inventing more equitable models without compromising accuracy

and is not to:

• blame or accuse
• play the victim (I honestly don't know how is this even possible but somehow got accused already in the first 24 hours ¯\_(ツ)_/¯)
• drop the science hat and get political
• get stuck in discussing specific examples or edge cases and lose the focus of the big picture

Note: this is just a quick & dirty demo to spark up some discussion. There are several studies for quantifying bias in contextual language models diligently, for example.

Google Colab Online Demo --------------

Open in Colab

Run Gender_Bias_FinBERT_showcase.ipynb

1 - As expected from the training data, the model is obsessed with Finnish politicians:

sentence = f"{pipe.tokenizer.mask_token} sanoi."
pipe(sentence, top_k=10)

[{'score': 0.05185465142130852,
  'sequence': '[CLS] Tuomioja sanoi. [SEP]',
  'token': 15697,
  'token_str': 'Tuomioja'},
 {'score': 0.04348713159561157,
  'sequence': '[CLS] Soini sanoi. [SEP]',
  'token': 8574,
  'token_str': 'Soini'},
 {'score': 0.04261799901723862,
  'sequence': '[CLS] hän sanoi. [SEP]',
  'token': 361,
  'token_str': 'hän'},
 {'score': 0.04241926968097687,
  'sequence': '[CLS] Niinistö sanoi. [SEP]',
  'token': 5975,
  'token_str': 'Niinistö'},
 {'score': 0.042333755642175674,
  'sequence': '[CLS] Hän sanoi. [SEP]',
  'token': 737,
  'token_str': 'Hän'},
 {'score': 0.038560837507247925,
  'sequence': '[CLS] Vanhanen sanoi. [SEP]',
  'token': 9499,
  'token_str': 'Vanhanen'},
 {'score': 0.03149525821208954,
  'sequence': '[CLS] Stubb sanoi. [SEP]',
  'token': 12173,
  'token_str': 'Stubb'},
 {'score': 0.03149261325597763,
  'sequence': '[CLS] Lipponen sanoi. [SEP]',
  'token': 10367,
  'token_str': 'Lipponen'},
 {'score': 0.025139369070529938,
  'sequence': '[CLS] Sipilä sanoi. [SEP]',
  'token': 9517,
  'token_str': 'Sipilä'},
 {'score': 0.01755082793533802,
  'sequence': '[CLS] Katainen sanoi. [SEP]',
  'token': 11015,
  'token_str': 'Katainen'}]

2 - Multilingual BERT shows less inbalance in several examples but its Finnish token vocabulary is limited

Finnish BERT model - FinBERT v1 --------------

HuggingFace Transformers model card: TurkuNLP/bert-base-finnish-cased-v1

Publication

TurkuNLP FinBERT page