inclusion

Overcoming Gender Bias in AI

Overcoming Gender Bias in AI Hello, my name is Tahmina Mahmud and I am currently working as a deep learning engineer at NATO, developing AI-based algorithms to enhance external safety features and improve driver behaviours. Today, I'd like to tackle an important issue - Gender Bias in Artificial Int

By Tahmida Mahmud

·

Overcoming Gender Bias in AI

Hello, my name is Tahmina Mahmud and I am currently working as a deep learning engineer at NATO, developing AI-based algorithms to enhance external safety features and improve driver behaviours. Today, I'd like to tackle an important issue - Gender Bias in Artificial Intelligence.

Session Overview

My talk aims to:

  1. Illustrate the relationship between Artificial Intelligence, Machine Learning, and Data Science,
  2. identify the biases found within AI algorithms,
  3. provide examples of the tangible impacts of gender bias on our daily lives,
  4. and suggest best practices to avoid AI bias.

AI, Machine Learning, and Data Science

AI is a set of concepts or techniques used to simulate tasks usually performed by humans. A subset of AI, Machine Learning, instructs machines to mimic human intelligence. On the other hand, Data Science uses statistical and machine learning techniques to identify patterns within the data.

The Importance of Data

Data, often referred to as "the new oil," is the most crucial resource in the world today, as it has the power to guide an entire civilization. Unbiased, well-processed data is elemental for building efficient AI models and hence, should be a paramount concern for all AI developers.

Gender Bias in AI

Bias is manifested when AI algorithms are trained using real-world data, tainted with inherent socio-economic inequalities and historical prejudices, hence introducing bias into AI models.

Illustrations of Gender Bias in AI

  • In Computer Vision, female politicians are often labelled related to their appearance, while their male counterparts are tagged as business people.
  • In Natural Language Processing, certain translations show a clear bias in gender associations based on conventions and societal norms.
  • Virtual assistant voices are most commonly women's, while the most powerful Computer Watson has a male voice, reinforcing existing stereotypes.

Cause and Effect of AI Bias

Training data based on gender discrimination from the real world leads to AI bias. It's time we focus on ‘Responsible AI’ to ensure these technologies promote equality and fairness.

Creating a Bias-Free AI World

While developing AI systems, let's be mindful of the data used, reviewing the context, limitations and validity. Ensuring data is representative of diverse demographics can help reduce bias. Let's also aim for gender diversity while forming teams of AI developers. AI companies worldwide should lead by example, hiring more women across teams.

Conclusion

While bias may be unavoidable in life, it should not be part of our technologies. We must strive for fairness and use AI to foster positive change. With men and women working together, we can shape the future of a bias-free AI world.

Please feel free to reach out via Email or LinkedIn with any queries, comments or further discussions on the topic. Thanks for joining this session. Together, let's make strides towards eliminating gender bias in AI.