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Responsible Data Science - technical & societal issues

12/2/2019

5 Comments

 
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Data science is an emerging discipline that offers both promise and peril. Responsible data science refers to efforts that address both the technical and societal issues in emerging data-driven technologies. Dr. Lise Getoor is a computer scientist, professor and research director of the UC Santa Cruz D3 Data Science Research Center and strategic partner of Omny IQ. She is well known for her theoretical work that integrates logic and probability to reason collectively and holistically about context in structured domains. In this video presentation, 
she will describe some of the opportunities and challenges in developing the foundations for responsible data science. How can machine learning and AI systems reason effectively about complex dependencies and uncertainty? Furthermore, how do we understand the ethical and justice issues involved in data-driven decision-making? There is a pressing need to integrate algorithmic and statistical principles, social science theories, and basic humanist concepts so that we can think critically and constructively about the socio-technical systems we are building. 
Dr. Lise Getoor, AI and Omny IQ
Dr. Lise Getoor is a professor in the Computer Science Department at UC Santa Cruz and the director of the UC Santa Cruz D3 Data Science Center. Her research areas include machine learning and reasoning under uncertainty; in addition she works in data management, visual analytics and social network analysis. She has over 200 publications and extensive experience with machine learning and probabilistic 
modeling methods for graph and network data. She is a Fellow of the Association for Artificial Intelligence, an elected board member of the International Machine Learning Society, serves on the board of the Computing Research Association (CRA), among other titles.  She has received her PhD from Stanford University.

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5 Comments
Sarah
12/2/2019 06:36:51 pm

Love your talk Dr. Getoor! Responsibility in data science includes fairness, transparency, diversity and data protection. These are difficult but important problems, and you have made it easier for me to understand the issues, yay! As I perused the web I've come across a related blog from Dr. Michel Kana here: https://towardsdatascience.com/wild-wide-ai-responsible-data-science-16b860e1efe9

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Jim B
12/2/2019 06:39:17 pm

Nice video preso Dr. Lise Getoor. Thanks for posting Omny IQ!

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Heather S
12/10/2019 04:58:57 pm

Insightful, and very helpful talk. Thanks for sharing!

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Eugene C
12/15/2019 03:44:16 pm

Agree. We have seen too many mistakes with ML algorithms that create negligence, abuse, opacity and inaccuracies. Great talk!

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Gerald H
12/20/2019 05:46:45 pm

Great post! Learned a few things.

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