Difference between revisions of "Ethics"

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<b>Ethics and Bias in Artificial Intelligence - 18th Vienna Deep Learning Meetup
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<b>Yi Zeng on "Brain-inspired Artificial Intelligence and Ethics of Artificial Intelligence"
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</b><br>The Vienna Deep Learning Meetup and the Centre for Informatics and Society of TU Wien jointly organized an evening of discussion on the topic of Ethics and Bias in AI. As promising as machine learning techniques are in terms of their potential to do good, the technologies raise a number of ethical questions and are prone to biases that can subvert their well-intentioned goals. 
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</b><br>Yi Zeng of the Institute of Automation of the Chinese Academy of Sciences on "Brain-inspired Artificial Intelligence and Ethics of Artificial Intelligence" at a LASER/LAst Dialogues www.scaruffi.com/leonardo/sep2020.html
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Machine learning systems, from simple spam filtering or recommender systems to Deep Learning and AI, have already arrived at many different parts of society. Which web search results, job offers, product ads and social media posts we see online, even what we pay for food, mobility or insurance - all these decisions are already being made or supported by algorithms, many of which rely on statistical and machine learning methods. As they permeate society more and more, we also discover the real world impact of these systems due to inherent biases they carry. For instance, criminal risk scoring to determine bail for defendants in US district courts has been found to be biased against black people, and analysis of word embeddings has been shown to reaffirm gender stereotypes due to biased training data. While a general consensus seems to exist that such biases are almost inevitable, solutions range from embracing the bias as a factual representation of an unfair society to mathematical approaches trying to determine and combat bias in machine learning training data and the resulting algorithms.  Besides producing biased results, many machine learning methods and applications raise complex ethical questions. Should governments use such methods to determine the trustworthiness of their citizens? Should the use of systems known to have biases be tolerated to benefit some while disadvantaging others? Is it ethical to develop AI technologies that might soon replace many jobs currently performed by humans? And how do we keep AI and automation technologies from widening society's divides, such as the digital divide or income inequality?  This event provides a platform for multidisciplinary debate in the form of keynotes and a panel discussion with international experts from diverse fields:
 
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Keynotes:
 
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- Prof. Moshe Vardi: "Deep Learning and the Crisis of Trust in Computing"
 
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- Prof. Sarah Spiekermann-Hoff: “The Big Data Illusion and its Impact on Flourishing with General AI”
 
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Panelists: Ethics and Bias in AI
 
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- Prof. Moshe Vardi, Karen Ostrum George Distinguished Service Professor in Computational Engineering, Rice University
 
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- Prof. Peter Purgathofer, Centre for Informatics and Society / Institute for Visual Computing & Human-Centered Technology, TU Wien
 
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- Prof. Sarah Spiekermann-Hoff, Institute for Management Information Systems, WU Vienna
 
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- Prof. Mark Coeckelbergh, Professor of Philosophy of Media and Technology, Department of Philosophy, University of Vienna
 
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- Dr. Christof Tschohl, Scientific Director at Research Institute AG & Co KG
 
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Moderator: Markus Mooslechner, Terra Mater Factual Studios
 
 
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−
<youtube>WxcpQW3afkw</youtube>
+
<youtube>_zwBCDmlvv8</youtube>
−
<b>Yi Zeng on "Brain-inspired Artificial Intelligence and Ethics of Artificial Intelligence"
+
<b>Ethics and Bias in Artificial Intelligence - 18th Vienna Deep Learning Meetup
−
</b><br>Yi Zeng of the Institute of Automation of the Chinese Academy of Sciences on "Brain-inspired Artificial Intelligence and Ethics of Artificial Intelligence" at a LASER/LAst Dialogues www.scaruffi.com/leonardo/sep2020.html
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</b><br>The Vienna Deep Learning Meetup and the Centre for Informatics and Society of TU Wien jointly organized an evening of discussion on the topic of Ethics and Bias in AI. As promising as machine learning techniques are in terms of their potential to do good, the technologies raise a number of ethical questions and are prone to biases that can subvert their well-intentioned goals. 
 +
Machine learning systems, from simple spam filtering or recommender systems to Deep Learning and AI, have already arrived at many different parts of society. Which web search results, job offers, product ads and social media posts we see online, even what we pay for food, mobility or insurance - all these decisions are already being made or supported by algorithms, many of which rely on statistical and machine learning methods. As they permeate society more and more, we also discover the real world impact of these systems due to inherent biases they carry. For instance, criminal risk scoring to determine bail for defendants in US district courts has been found to be biased against black people, and analysis of word embeddings has been shown to reaffirm gender stereotypes due to biased training data. While a general consensus seems to exist that such biases are almost inevitable, solutions range from embracing the bias as a factual representation of an unfair society to mathematical approaches trying to determine and combat bias in machine learning training data and the resulting algorithms.  Besides producing biased results, many machine learning methods and applications raise complex ethical questions. Should governments use such methods to determine the trustworthiness of their citizens? Should the use of systems known to have biases be tolerated to benefit some while disadvantaging others? Is it ethical to develop AI technologies that might soon replace many jobs currently performed by humans? And how do we keep AI and automation technologies from widening society's divides, such as the digital divide or income inequality?  This event provides a platform for multidisciplinary debate in the form of keynotes and a panel discussion with international experts from diverse fields:  Keynotes:  - Prof. Moshe Vardi: "Deep Learning and the Crisis of Trust in Computing"  - Prof. Sarah Spiekermann-Hoff: “The Big Data Illusion and its Impact on Flourishing with General AI”  Panelists: Ethics and Bias in AI  - Prof. Moshe Vardi, Karen Ostrum George Distinguished Service Professor in Computational Engineering, Rice University  - Prof. Peter Purgathofer, Centre for Informatics and Society / Institute for Visual Computing & Human-Centered Technology, TU Wien  - Prof. Sarah Spiekermann-Hoff, Institute for Management Information Systems, WU Vienna  - Prof. Mark Coeckelbergh, Professor of Philosophy of Media and Technology, Department of Philosophy, University of Vienna  - Dr. Christof Tschohl, Scientific Director at Research Institute AG & Co KG  Moderator: Markus Mooslechner, Terra Mater Factual Studios
 
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Revision as of 17:10, 8 October 2020

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Leading institutes and companies have published a set of ethical standards for AI research Europe is making AI rules now to avoid a new tech crisis

The Ethical Side of Data Usage | Veritone
Machine learning requires data, and many companies have lots of data that is useful for many very important tasks. However there are many questions about how this data should be used, shared and applied. Additionally, companies walk a fine line with just how much they want to let customers and users know about the data they have on them. This panel will explore the ethical side of data usage from an industry perspective. For more details, visit us at http://Veritone.com Veritone is a leading provider of artificial intelligence technology and solutions. The company’s proprietary operating system, aiWARE™, orchestrates an expanding ecosystem of machine learning models to transform audio, video and other data sources into actionable intelligence. Its open architecture enables customers in the media and entertainment, legal and compliance, and government sectors to easily deploy applications that leverage the power of AI to dramatically improve operational efficiency and effectiveness.

Use of Artificial Intelligence by the U.S. and Its Adversaries
After discussing the state of artificial intelligence expertise, technologies, and applications in the United States, China, and Russia, experts will evaluate the ways in which Beijing and Moscow can use AI improve their influence operations, cyberattacks, and battlefield capabilities. Speakers will also consider how the United States can counter any advantages that AI provides Russia and China in the propaganda, cyber, and military domains. Speakers include: Brian Drake, Director of Artificial Intelligence and Machine Learning, DIA Future Capabilities and Innovation Office; Elsa Kania, Adjunct Senior Fellow, Technology and National Security Program, CNAS; Dr. Margarita Konaev, Research Fellow, CSET; Colonel P.J. Maykish, USAF, Director of Analysis, National Security Commission on Artificial Intelligence; and Moderator, Charles Clancy, Chief Futurist and Senior Vice President/General Manager, MITRE

Kathryn Hume, Ethical Algorithms: Bias and Explainability in Machine Learning
Ethics of AI Lab Centre for Ethics, University of Toronto, March 20, 2018 http://ethics.utoronto.ca Kathryn Hume intergrate.ai

Yi Zeng on "Brain-inspired Artificial Intelligence and Ethics of Artificial Intelligence"
Yi Zeng of the Institute of Automation of the Chinese Academy of Sciences on "Brain-inspired Artificial Intelligence and Ethics of Artificial Intelligence" at a LASER/LAst Dialogues www.scaruffi.com/leonardo/sep2020.html

CS-E3210 Machine Learning: Basic Principles - Ethics and the GDPR
Alexander Jung Guest talk by Maria Rehbinder Senior Legal Counsel in Aalto University and Certified Information Privacy Professional (CIPP/E) Richard Darst Aalto Science-IT Coordinator

Ethics and Bias in Artificial Intelligence - 18th Vienna Deep Learning Meetup
The Vienna Deep Learning Meetup and the Centre for Informatics and Society of TU Wien jointly organized an evening of discussion on the topic of Ethics and Bias in AI. As promising as machine learning techniques are in terms of their potential to do good, the technologies raise a number of ethical questions and are prone to biases that can subvert their well-intentioned goals. Machine learning systems, from simple spam filtering or recommender systems to Deep Learning and AI, have already arrived at many different parts of society. Which web search results, job offers, product ads and social media posts we see online, even what we pay for food, mobility or insurance - all these decisions are already being made or supported by algorithms, many of which rely on statistical and machine learning methods. As they permeate society more and more, we also discover the real world impact of these systems due to inherent biases they carry. For instance, criminal risk scoring to determine bail for defendants in US district courts has been found to be biased against black people, and analysis of word embeddings has been shown to reaffirm gender stereotypes due to biased training data. While a general consensus seems to exist that such biases are almost inevitable, solutions range from embracing the bias as a factual representation of an unfair society to mathematical approaches trying to determine and combat bias in machine learning training data and the resulting algorithms. Besides producing biased results, many machine learning methods and applications raise complex ethical questions. Should governments use such methods to determine the trustworthiness of their citizens? Should the use of systems known to have biases be tolerated to benefit some while disadvantaging others? Is it ethical to develop AI technologies that might soon replace many jobs currently performed by humans? And how do we keep AI and automation technologies from widening society's divides, such as the digital divide or income inequality? This event provides a platform for multidisciplinary debate in the form of keynotes and a panel discussion with international experts from diverse fields: Keynotes: - Prof. Moshe Vardi: "Deep Learning and the Crisis of Trust in Computing" - Prof. Sarah Spiekermann-Hoff: “The Big Data Illusion and its Impact on Flourishing with General AI” Panelists: Ethics and Bias in AI - Prof. Moshe Vardi, Karen Ostrum George Distinguished Service Professor in Computational Engineering, Rice University - Prof. Peter Purgathofer, Centre for Informatics and Society / Institute for Visual Computing & Human-Centered Technology, TU Wien - Prof. Sarah Spiekermann-Hoff, Institute for Management Information Systems, WU Vienna - Prof. Mark Coeckelbergh, Professor of Philosophy of Media and Technology, Department of Philosophy, University of Vienna - Dr. Christof Tschohl, Scientific Director at Research Institute AG & Co KG Moderator: Markus Mooslechner, Terra Mater Factual Studios

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