Breakthrough Days: Beneficial AI + Gender Data

As artificial intelligence (AI) and machine learning replace manual design and human oversight, society gains efficiency. However, the inputs used to determine these machine-made choices rely on flawed data that lacks critical information about female preferences, behaviors, and needs.

AI is learning gender bias from humans. Data with implicit gender bias may lead to biased analytics and predictive models, biased inferences and insights, biased policies and solutions, and ultimately a gender-biased world. However, if harnessed for good, AI and machine learning can create a future in which unbiased insights tell the story of all humanity —not just a subset of it.

Speakers, Panelists and Moderators

  • STUART RUSSELL
    STUART RUSSELL
    Professor, Author of "Human Compatible"
    UC-Berkeley
    Stuart Russell received his B.A. with first-class honours in physics from Oxford University in 1982 and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is Professor (and formerly Chair) of Electrical Engineering and Computer Sciences and holder of the Smith-Zadeh Chair in Engineering. He is also an Adjunct Professor of Neurological Surgery at UC San Francisco and Vice-Chair of the World Economic Forum's Council on AI and Robotics. Russell is a recipient of the Presidential Young Investigator Award of the National Science Foundation, the IJCAI Computers and Thought Award, the World Technology Award (Policy category), the Mitchell Prize of the American Statistical Association and the International Society for Bayesian Analysis, the ACM Karlstrom Outstanding Educator Award, and the AAAI/EAAI Outstanding Educator Award. In 1998, he gave the Forsythe Memorial Lectures at Stanford University and from 2012 to 2014 he held the Chaire Blaise Pascal in Paris. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His research covers a wide range of topics in artificial intelligence including machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, multitarget tracking, computer vision, computational physiology, global seismic monitoring, and philosophical foundations. His books include "The Use of Knowledge in Analogy and Induction", "Do the Right Thing: Studies in Limited Rationality" (with Eric Wefald), and "Artificial Intelligence: A Modern Approach" (with Peter Norvig). His current concerns include the threat of autonomous weapons and the long-term future of artificial intelligence and its relation to humanity.
  • CAROLINE CRIADO-PEREZ
    CAROLINE CRIADO-PEREZ
    British feminist author, journalist and activist, Author of "Invisible Women"
    Caroline Emma Criado Perez OBE (born June 1984) is a British feminist author, journalist and activist. Her first national campaign, the Women's Room project, aimed to increase the presence of female experts in the media. She opposes the removal of the only woman from British banknotes (other than The Queen), leading to the Bank of England's swift announcement that the image of Jane Austen would appear on the £10 note by 2017.[1] That campaign led to sustained harassment on the social networking website Twitter of Criado Perez and other women; as a result, Twitter announced plans to improve its complaint procedures. Her most recent campaign was for a sculpture of a woman in Parliament Square; the statue of Millicent Fawcett was unveiled in April 2018, as part of the centenary celebrations of the winning of women's suffrage in the United Kingdom. Her 2019 book Invisible Women: Exposing Data Bias in a World Designed for Men was a Sunday Times bestseller.
  • AMIR BANIFATEMI
    AMIR BANIFATEMI
    Chief Innovation Officer; Chair of the AI for Good Programme Committee
    XPRIZE
    My general focus is on identifying and developing emerging and transformative technologies that can impact society in significant and exponential ways. I do this by helping create interdisciplinary frameworks for understanding and planning new developments and the funding required to bring new innovations to market. I help start, grow, and run innovative ventures, and focus on working with startups and growth-oriented companies on products and initiatives that could trigger significant breakthrough with substantial economic and societal impact. Particular emphasis on machine learning and predictive systems, IoT, knowledge sharing and crowdsourcing, Education, and digital health. I usually help teams on initial market opportunity validation, product roadmap strategy, pricing and business model, go-to-market operations, fundraising and mentorship, Mergers and high-growth partnerships. I also have managed a few private and public technology investment and venture capital funds and know how to establish fund operations and their overall strategies and requirements. I enjoy teaching and have been a guest lecturer and adjunct professor at UC Berkeley, HEC Paris, Chapman University, Claremont McKenna College, and UC Irvine.

Hourly Schedule

17:00 - 18:00
Beneficial AI to advance SDGs
AI can bring immense benefits to humanity, potentially ending the scarcity and inequality that have haunted the last 10,000 years of human history. It also has risks, some already apparent and others on the horizon - in particular, how do we retain control over entities more intelligent and more powerful than ourselves? And, if we answer that question, then we face another: now that we have a choice, what do we want the future to be like?
Moderator:
AMIR BANIFATEMI
Speaker:
STUART RUSSELL
18:00 - 18:45
How to Close the Data Gap and Design a World That Works for Everyone
Keynote address
Moderator:
AMIR BANIFATEMI
Speaker:
CAROLINE CRIADO-PEREZ
18:45 - 19:00
BREAK
15min break / transition to workshop
19:00 - 20:30
Gendery Equity Breakout
See calendar for the separate registration
19:15 - 20:45
Future of Food Breakout
See calendar for the separate registration
19:30 - 21:00
Collective Pandemic Intelligence Breakout
See calendar for the separate registration
AMIR BANIFATEMI
AMIR BANIFATEMI
Chief Innovation Officer; Chair of the AI for Good Programme Committee
As artificial intelligence (AI) and machine learning replace manual design and human oversight, society gains efficiency. However, the inputs used to determine these machine-made choices rely on flawed data that lacks critical information about female preferences, behaviors, and needs. AI is learning gender bias from humans. Data with implicit gender bias may lead to biased analytics and predictive models, biased inferences and insights, biased policies and solutions, and ultimately a gender-biased world. However, if harnessed for good, AI and machine learning can create a future in which unbiased insights tell the story of all humanity —not just a subset of it.
STUART RUSSELL
STUART RUSSELL
Professor, Author of "Human Compatible"
As artificial intelligence (AI) and machine learning replace manual design and human oversight, society gains efficiency. However, the inputs used to determine these machine-made choices rely on flawed data that lacks critical information about female preferences, behaviors, and needs. AI is learning gender bias from humans. Data with implicit gender bias may lead to biased analytics and predictive models, biased inferences and insights, biased policies and solutions, and ultimately a gender-biased world. However, if harnessed for good, AI and machine learning can create a future in which unbiased insights tell the story of all humanity —not just a subset of it.
CAROLINE CRIADO-PEREZ
CAROLINE CRIADO-PEREZ
British feminist author, journalist and activist, Author of "Invisible Women"
As artificial intelligence (AI) and machine learning replace manual design and human oversight, society gains efficiency. However, the inputs used to determine these machine-made choices rely on flawed data that lacks critical information about female preferences, behaviors, and needs. AI is learning gender bias from humans. Data with implicit gender bias may lead to biased analytics and predictive models, biased inferences and insights, biased policies and solutions, and ultimately a gender-biased world. However, if harnessed for good, AI and machine learning can create a future in which unbiased insights tell the story of all humanity —not just a subset of it.
Tags:

Date

22 Sep 2020

Time

CEST, Geneva
17:00 - 19:15
Sessions

Topics

Inclusivity,
SDGs