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SUMMARY:Trustworthy and Responsible Machine Learning - Dr. Pingfan Song\, 
  Department of Engineering\, University of Cambridge
DTSTART:20231130T150000Z
DTEND:20231130T170000Z
UID:TALK208876@talks.cam.ac.uk
CONTACT:96241
DESCRIPTION:Machine learning-based AI systems are experiencing widespread 
 adoption across various domains\, producing a profound influence on daily 
 lives\, industries\, scientific research\, and beyond. Ensuring the safety
  of these AI systems\, particularly in high-stakes real-world applications
 \, stands as an imperative priority. Recent landmark events\, such as the 
 first global AI safety summit\, have underscored the critical significance
  of AI safety. \n\nTrustworthy and responsible machine learning has gained
  significant attention from governments\, industries\, and scientific comm
 unities alike. It is recognized as an essential component and fundamental 
 pillar in the pursuit of AI safety objectives. This presentation will brie
 fly cover some noteworthy limitations in current AI systems\, such as opaq
 ueness\, bias\, fragility\, privacy invasion. Subsequently\, it will focus
  on the technical dimensions of trustworthy and responsible machine learni
 ng\, exploring measures and techniques designed to enhance transparency\, 
 interpretability\, robustness\, privacy protection. While this presentatio
 n may not offer an exhaustive and comprehensive overview\, hopefully\, it 
 aspires to provide researchers and users with some insights\, and to advoc
 ate a more prudent utilization of AI technology\, considering that it is c
 rucial for users not only to harness the benefits AI offers but also to mi
 tigate its potential harm and risks. 
LOCATION:Alison Richard Building\, 7 West Road (Sidgwick Site)\, Room SG2
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