Tahmida
Mahmud
Deep Learning Engineer at Nauto, Inc.
"Overcoming Gender Bias in AI"
"Overcoming Gender Bias in AI"
Tue June 8 - 2:30 PM EDT/New York (See in local time)
Add to Calendar
04//2024 2:30 PM
04//2024 02:50 PM
America/New_York
#WTGC2024
"Overcoming Gender Bias in AI"
#WTGC2024
"Overcoming Gender Bias in AI"
https://app.events.ringcentral.com/events/women-in-tech-global-conference-2024/reception
https://www.womentech.net/vfairs
"Overcoming Gender Bias in AI"
"Overcoming Gender Bias in AI"
Don’t miss out and join visionaries, innovators, and thought leaders from all over the world at the Women in Tech Global Conference.
Vote by Sharing
Unite 100 000 Women in Tech to Drive Change with Purpose and Impact.
Do you want to see this session? Help increase the sharing count and the session visibility. Sessions with +10 votes will be available to career ticket holders.
Please note that it might take some time until your share & vote is reflected.
Session: Overcoming Gender Bias in AI
Artificial Intelligence refers to a set of concepts, tools, and techniques that allows machines to simulate human intelligence and mimic human-like tasks through analyzing data and identifying patterns, and insights. Although it may seem like these machines have a mind of their own, they actually mirror human experience and behavior since AI models are trained with real-life data. Being a reflection of human perception, the historical data that AI models use are not free from inherent social biases and more data is not always better specially when it is skewed. In this talk, I will discuss one of the major biases internalized by such models which is gender bias. This is a crucial problem since there is a tendency to think that machines are always perfect, and their decisions are free from human flaws which is clearly not the case here.Key Takeaways:
- Acknowledging the problem is the first steps towards solution
- We need to feed meaningful data to our models which represents the level-playing field we want the world to be
- Data scientists should review the context, limitations and validity of the data which includes exploring what is missing in the dataset and asking the right questions during data collection
- We need to increase diversity in tech and have more allies for an enabled and equal world
- AI companies need to lead by example by hiring more women in product design, data science, and engineering since diversity of thought leads to better problem-solving
Key Takeaways
- We need to feed meaningful data to our models which represents the level-playing field we want the world to be
- Data scientists should review the context, limitations and validity of the data which includes exploring what is missing in the dataset and asking the right questions during data collection
- We need to increase diversity in tech and have more allies for an enabled and equal world
- AI companies need to lead by example by hiring more women in product design, data science, and engineering since diversity of thought leads to better problem-solving
Bio
Tahmida Mahmud is currently working as a Deep Learning Engineer at Nauto, Inc. She is responsible for developing and implementing AI-based algorithms for external safety features and driver behavior improvement and currently leading the Predictive Collision Alerts (PCA) project. She received her Masters degree and Ph.D. degree in Electrical Engineering from University of California, Riverside in 2017 and 2019 respectively. She received her Bachelors degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET) in 2013. Her broad research interests include computer vision and machine learning with more focus on activity recognition and prediction, video captioning, frame reconstruction, object detection etc. Tahmida wants her research to have real impact on people’s life. In her free time, she loves to write and travel.