Fireside Chat | How to Engineer Ethics into AI


How to Engineer Ethics into AI

Fireside Chat - 10:40 am - 11:00 am

The growth of facial recognition (FR) technology is accompanied by consistent assertions (as catalogued by Georgetown University) that demographic dependencies could lead to accuracy variations and potential bias. Film and video technology has a well-documented history of bias in its treatment of skin tone. While engineers have benefited from the increased availability of AI and machine learning tools, allowing them to train their models to ever higher accuracy, the fairness and ethics of their algorithms have often been an afterthought. Modern digital algorithms are similarly susceptible to asymmetric representation and treatment of face types. This paper draws from the National Institute of Standards and Technology (NIST) report which dissects these demographic dependencies of over 100 FR algorithms and then details the strategies and techniques needed, to not only reduce bias, but deliberately design for fairness and socially responsible outcomes. Furthermore, it shows that a model trained for low-bias actually delivers higher performance. 

Takeaways: Workshops attendees will 

  • Learn the key factors that affect bias in AI models with the specific examples from facial recognition. 
  • Gain insight into the ethical implications of unscientific AI 
  • Receive practical steps to setting their own AI program on an ethical foundation

Presented by:

Reza Rassool, Chief Technology Officer, RealNetworks