GN-GloVe AI to Have No Gender Prejudice

New model of scientists at the University of California showed 35% fewer mistakes
10 September 2018   585

Scientists from the University of California created a model for learning neural networks called Gender-Neutral Global Vectors (GN-GloVe). The development is intended for AI, specializing in the recognition of speech and texts. According to programmers, this training model will reduce the percentage of false gender associations. This is reported by Venture Beat.

Neural networks designed for speech recognition are trained on special data sets. However, these kits carry the imprint of a living language, filled with stereotypes. For example, the words "cook" or "secretary" are more often associated with the female sex, and "locksmith" or "welder" - with the male. Or, another examples: "doctor" is usually replaced by the pronoun "he", and "nurse" - "she".

Artificial intelligence, trained on such datasets, assimilates all the prejudices inherent in them. In particular, if a "doctor" is mentioned in the text without mentioning a particular sex, the neural network will more likely be considered a man. GN-GloVe, as claimed by its creators, removes the false associations with the sex.

This technology does not affect those areas where the sex is specified directly. To achieve this effect, the method determines gender-neutral words simultaneously with the formation of the semantic vectors of the text. Another advantage of development scientists call independence from the language being processed.

In a comparative analysis with GloVe, one of the most common teaching methods, the new model of scientists at the University of California showed 35% fewer mistakes due to false identification of a person's sex by type of activity.

Data sets for training contain many prerequisites for the formation of retraining errors. For example, smart speakers from Amazon and Google are 30% less likely to recognize English, pronounced with accent. And this problem is not just about speech: face recognition algorithms are worse at copying images of African Americans than Caucasians.

The bias of artificial intelligence bias surfaced in the work of Princeton University scientists in early 2017. While protection from such errors does not exist, however, similar GN-GloVe algorithms can in time reduce the bias error to an acceptable level.

OpenAI to Create Fake News Creating Algorithm

On the basis of one or two phrases that set the theme, it is able to “write” a fairly plausible story
18 February 2019   145

The GPT-2 algorithm, created by OpenAI for working with language and texts, turned out to be a master in creating fake news. On the basis of one or two phrases that set the theme, it is able to “compose” a fairly plausible story. For example:

  • an article about scientists who have found a herd of unicorns in the Andes;
  • news about pop star Miley Cyrus caught on shoplifting;
  • artistic text about Legolas and Gimli attacking the orcs;
  • an essay on how waste recycling harms the economy, nature, and human health.

The developers did not publish the source code of the model entirely, fearing abuse by unscrupulous users. For fellow researchers, they posted on GitHub a simplified version of the algorithm and gave a link to the preprint of the scientific article. The overall results are published on the OpenAI blog.

GPT-2 is a general purpose algorithm. The developers taught it to answer questions, “understand” the logic of a text, a sentence, finish building phrases. In this case, the algorithm worked worse than the model of a specific purpose. Researchers suggest that the indicators can be improved by expanding the training datasets and choosing computers more efficiently.