Bots to Combat Casual Sexism at Work

Representation of Women in Tech

• National Center for Women & Information Technology (NCWIT), 25% of the computing workforce was female in 2015.

• Hiring Across Technical roles: It’s an imbalance that’s reflected in company diversity reports. Microsoft, for example, boasts 29% female workers across its staff, but in technical positions only 17% are women. Of Google’s senior management and executive officer team, 17 are male while only three are women. Men make up 83% of Google’s engineering staff; Apple’s technical team is 80% male

The Stats say it all!

Sexism at the Workplace: The uncomfortable truth

• Four in ten women in the United States have faced gender discrimination in their workplace. Sexism at the workplace is a global problem.
• Casual Sexism-What is that?
• But the masculine skew allows casual sexism to go unchecked, exemplified by ill-advised comments from certain chief executives. It’s so subtle that you’re often left wondering if it really happened, and when you try to talk about it, you sound like a crazy person because it’s so small – but it’s death by a thousand paper cuts

Solution?

Global Level:

• Create awareness in your company. 
• Conduct Meetups and talks that center around women and their contributions

Personal Level: 

• Join Women in Tech organizations
• Discuss and talk about this to other female co-workers 
• Use your strengths- Technology as a weapon

Building Bots to Combat Sexism

• Bot Flow
• Get Data
• Bot Flow-Preprocessing the data
• Understanding Workplace Conversations: A Brief Introduction to Word Embeddings

• Model Training

• Integrating with the Slack Platform 

• Future Work
• Toxic Comment on Reddit
• How can you contribute?

Bot Flow

Get Data

• Deep Pavlov Data-Slack bot
• Created a few of our own sexist remarks that are commonly used in the workplace
• Toxic Comment Classification Challenge Kaggle: Link

Bot Flow-Preprocessing the data

• Removing Stopwords 
• Removing Punctuations
• Tokenising the sentences
• Conversion into numbers

Understanding Workplace Conversations: A Brief Introduction to Word Embeddings

• Capturing Conversations- 24 percent of conversations in workplaces are written communications. Hence can be analyzed and monitored
• Word Embeddings: Word Embeddings are Word converted into numbers 

• Humans can deal with text format quite intuitively whereas computers cannot
• A computer can match two strings and tell you whether they are same or not. How do you make a computer understand that “Apple” in “Apple is a tasty fruit” is a fruit that can be eaten and not a company?
• Conversion of the sentences to a dictionary

• Frequency-based word embeddings
  • Count Vector: occurrence of a word in a single document
  • TF-IDF Vector: takes into account the entire corpus
  • Co-Occurrence Vector: Similar words tend to occur together and will have similar context for example – Apple is a fruit. Mango is a fruit.

• Prediction based learning-
  • Language Model
  • Word2Vec: A toolkit that allows the seamless training and use of pre-trained embeddings
    • ​​CBOW
    • Skip-gram

Language Modelling:  Bengio's Work in 2003

consists of a one-hidden-layer feed-forward neural network that predicts the next word in a sequence

• Word2Vec: In 2013,  Mikolov et al.proposed two architectures for learning word embeddings that are computationally less expensive than previous models

  • CBOW: Continuous Bag of Words uses surrounding words to predict the centre word

  • Skip Gram: uses the center word to predict the surrounding words​​​

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• TSNE Plots using Tensorflow: Link

Model Training

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Integrating with the Slack Platform

• Function using Slack RTM API: Parses a list of events coming from the Slack RTM API to find bot commands. If a bot command is found, this function returns a tuple of command and channel.
• Documentation for using bots on slack: Link

Further Work

• Logging to catch repeated offenders: The bot will log details of the repeat offenders and will send a message to the workspace admin. Regular offenders will also be shown and incremental 'testosterone level' value

• Moving it into more workplace conversation apps like Microsoft Teams, Google Hangouts,   

• Audio Analysis: If the data gathered is significant enough, we can move this to audio/speech recognition of sexist comments. For example, this bot could be integrated with existing virtual assistants to give out warnings if a sexist remark is heard in a meeting.

Reddit Bot to Analyse Toxic Data

• The objective of the bot: Raise awareness of the toxic remarks in day-to-day forums used by people
• Easily available toxic comment data
• Used the same preprocessing steps
• Used a classification model instead of LSTM
• Future Work: Bots similar to Reddit bots: Analyzing the toxic nature of other social media platforms: Twitter, Forum channels

How can you contribute?

• Beginner: Use the code in github for toxic comment and build it for reddit channels

• Create Analysis of your reports: What should people learn from it?

• Please feel free to push any sort of data or remark you find suitable for this project. I believe that sexism in workplace conversations is a global problem. I hope that this bot will be a step towards eliminating it. Therefore, I need crowdsourced to improve the model and make it more usable

• Crowdsourcing data

• Unfortunately, there are no proper datasets for casual sexist remarks at workplaces. The available datasets that I have come across are extremely vulgar/obscene. However, sexist remarks in workplaces are often subtle or contain some usual phrases like "Girls are like that".

Acknowledgments

• Soham Chatterjee-For contributing and helping with this project

• Adam Shamsudeen- For helping me research data and finding the deep pavlov data

• Saama Technologies

Contact Details

• Feedback- A link to give me a feedback- https://tinyurl.com/bots-to-combat-sexism
• I would love to hear from you and your responses will be Anonymous!
• You can get a link to the slides right after filling the feedback form!
• Contact us:
• Archana Iyer:
  • varchanaiyer139@gmail.com