Keynote SpeakerMargaret Mitchell

Biography

Popular Talks by Margaret Mitchell

• Forecasting the Future of AI From What We Know Today
  By examining how past priorities in AI development shaped the AI of today, we can make some predictions about how what we prioritize today will shape the AI of the future. In this talk, I will work through how AI (machine learning) systems came to be what they are now,... Read more

  By examining how past priorities in AI development shaped the AI of today, we can make some predictions about how what we prioritize today will shape the AI of the future. In this talk, I will work through how AI (machine learning) systems came to be what they are now, the different biases and values at play throughout the AI development lifecycle, what these suggest for how AI will evolve, and what’s worth focusing on now to create beneficial AI in the future.

• Data and Intentionality in Modern AI
  Data is a fundamental component of machine learning, serving as the foundation for what ML models learn. However, relatively little attention has been paid to how ML datasets can be collected, curated, measured, stored, and shared responsibly. In this talk, I will dig into these issues, covering details on datasets... Read more

  Data is a fundamental component of machine learning, serving as the foundation for what ML models learn. However, relatively little attention has been paid to how ML datasets can be collected, curated, measured, stored, and shared responsibly. In this talk, I will dig into these issues, covering details on datasets and data governance, and provide some guidance on how we can use human values to shape what (and who) ML data represents.

• Practical AI Ethics
  I will walk through what it means to operationalize AI Ethics within the product lifecycle, focusing in on developing machine learning models informed by ethical considerations. This talk covers the role of human cognitive biases, the utility of different artifacts during development, some fun mathematical ideas for ML models and larger AI systems, and... Read more

  I will walk through what it means to operationalize AI Ethics within the product lifecycle, focusing in on developing machine learning models informed by ethical considerations. This talk covers the role of human cognitive biases, the utility of different artifacts during development, some fun mathematical ideas for ML models and larger AI systems, and methods for ethics-focused launch protocols.

• Unpacking Data Ethics in AI Systems
  In this talk, I will go over details of how to understand data collection for training Machine Learning models from the perspective of human values and human biases. I will walk through how the data interacts with model training and evaluation protocols, and how to approach model development from the... Read more

  In this talk, I will go over details of how to understand data collection for training Machine Learning models from the perspective of human values and human biases. I will walk through how the data interacts with model training and evaluation protocols, and how to approach model development from the perspective of ethical goals. Time permitting, I will further discuss how the ideas here can be applied to a company at large, shaping the kind of work that is done and the technology that gets deployed.

• Diversity and Inclusion (The culture! The math! The glory!)
  “Diversity” can be seen as a function of the proportions of different marginalized subpopulations; “Inclusion” can be seen as a function of each individual’s sense of belonging. In other words, Diversity means lots of different people at the table, while Inclusion means each person feels comfortable talking at the table.... Read more

  “Diversity” can be seen as a function of the proportions of different marginalized subpopulations; “Inclusion” can be seen as a function of each individual’s sense of belonging. In other words, Diversity means lots of different people at the table, while Inclusion means each person feels comfortable talking at the table. This talk will tease apart these differences culturally and algorithmically, focusing on methods to improve both diversity and inclusion at an organizational level as well as at an “AI” level.

• Multi-Task Learning for Mental Health using Social Media Text
• Operationalizing AI Ethics at Scale
• AI and ethics and global impact
• Bias in Vision and Language in AI
• Seeing AI
• Unconscious Bias