Toward AI We Can Count On
A consortium of top AI experts proposed concrete steps to help machine learning engineers secure the public's trust.
What’s new: Dozens of researchers and technologists recommended actions to counter public skepticism toward artificial intelligence, fueled by issues like data privacy and accidents caused by autonomous vehicles. The co-authors include scholars at universities like Cambridge and Stanford; researchers at companies including Intel, Google, and OpenAI; and representatives of nonprofits such as the Partnership on AI and Center for Security and Emerging Technology.
Recommendations: Lofty pronouncements about ethics aren’t enough, the authors declare. Like the airline industry, machine learning engineers must build an “infrastructure of technologies, norms, laws, and institutions” the public can depend on. The authors suggest 10 trust-building moves that fall into three categories.
• Institutional mechanisms such as third-party auditing to verify the accuracy of company claims and bounties to researchers who discover flaws in AI systems.
• Software mechanisms that make it easier to understand how a given algorithm works or capture information about a program’s development and deployment for subsequent auditing.
• Hardware mechanisms that protect data privacy, along with subsidies for computing power for academic researchers who may lack resources to evaluate what large-scale AI systems are doing.
Behind the news: The AI community is searching for ways to boost public trust amid rising worries about surveillance, the impact of automation on human labor, autonomous weapons, and computer-generated disinformation. Dozens of organizations have published their own principles, from Google and Microsoft to the European Commission and the Vatican. Even the U.S. Department of Defense published guidelines on using AI during warfare.
Why it matters: Widespread distrust in AI could undermine the great good this technology can do, frightening people away or prompting politicians to hamstring research and deployment.
We’re thinking: Setting clear standards and processes to verify claims about AI systems offers a path for regulators and users to demand evidence before they will trust an AI system. This document’s emphasis on auditing, explainability, and access to hardware makes a solid cornerstone for further efforts.
A consortium of top AI experts proposed concrete steps to help machine learning engineers secure the public's trust.
What’s new: Dozens of researchers and technologists recommended actions to counter public skepticism toward artificial intelligence, fueled by issues like data privacy and accidents caused by autonomous vehicles. The co-authors include scholars at universities like Cambridge and Stanford; researchers at companies including Intel, Google, and OpenAI; and representatives of nonprofits such as the Partnership on AI and Center for Security and Emerging Technology.
Recommendations: Lofty pronouncements about ethics aren’t enough, the authors declare. Like the airline industry, machine learning engineers must build an “infrastructure of technologies, norms, laws, and institutions” the public can depend on. The authors suggest 10 trust-building moves that fall into three categories.
• Institutional mechanisms such as third-party auditing to verify the accuracy of company claims and bounties to researchers who discover flaws in AI systems.
• Software mechanisms that make it easier to understand how a given algorithm works or capture information about a program’s development and deployment for subsequent auditing.
• Hardware mechanisms that protect data privacy, along with subsidies for computing power for academic researchers who may lack resources to evaluate what large-scale AI systems are doing.
Behind the news: The AI community is searching for ways to boost public trust amid rising worries about surveillance, the impact of automation on human labor, autonomous weapons, and computer-generated disinformation. Dozens of organizations have published their own principles, from Google and Microsoft to the European Commission and the Vatican. Even the U.S. Department of Defense published guidelines on using AI during warfare.
Why it matters: Widespread distrust in AI could undermine the great good this technology can do, frightening people away or prompting politicians to hamstring research and deployment.
We’re thinking: Setting clear standards and processes to verify claims about AI systems offers a path for regulators and users to demand evidence before they will trust an AI system. This document’s emphasis on auditing, explainability, and access to hardware makes a solid cornerstone for further efforts.
AI Explorables
Big ideas in machine learning, simply explained
The rapidly increasing usage of machine learning raises complicated questions: How can we tell if models are fair? Why do models make the predictions that they do? What are the privacy implications of feeding enormous amounts of data into models?
This ongoing series of interactive, formula-free essays will walk you through these important concepts.
https://pair.withgoogle.com/explorables
Big ideas in machine learning, simply explained
The rapidly increasing usage of machine learning raises complicated questions: How can we tell if models are fair? Why do models make the predictions that they do? What are the privacy implications of feeding enormous amounts of data into models?
This ongoing series of interactive, formula-free essays will walk you through these important concepts.
https://pair.withgoogle.com/explorables
Withgoogle
People + AI Research
People + AI Research (PAIR) is a multidisciplinary team at Google that explores the human side of AI.
Optimizing AI for Teamwork
Authors: University of Washington and Microsoft Research
https://twitter.com/bansalg_/status/1257413861348651013
https://arxiv.org/abs/2004.13102
Authors: University of Washington and Microsoft Research
We propose training AI systems in a human-centered manner and directly optimizing for team performance. We study this proposal for a specific type of human-AI team, where the human overseer chooses to accept the AI recommendation or solve the task themselves. To optimize the team performance we maximize the team’s expected utility, expressed in terms of quality of the final decision, cost of verifying, and individual accuracies.
https://twitter.com/bansalg_/status/1257413861348651013
https://arxiv.org/abs/2004.13102
Twitter
Gagan Bansal
Excited to share a draft of our new work on human-centered AI! https://t.co/OSP8wnRM7J w/ @besanushi @ecekamar @erichorvitz @dsweld When an AI assists human decision-makers, e.g, by recommending its predictions, is the most accurate AI necessarily the best…
Measuring user experience (UX) is an important part of the design process, yet there are few methods to evaluate UX in the early phases of product development. We introduce Triptech, a method used to quickly explore novel product ideas. We present how it was used to gauge the frequency and importance of user needs, to assess the desirability and perceived usefulness of design concepts, and to draft UX requirements for Now Playing-an on-device music recognition system for the Pixel 2. We discuss the merits and limitations of the Triptech method and its applicability to tech-driven innovation practices.
https://dl.acm.org/doi/10.1145/3290607.3299061
https://dl.acm.org/doi/10.1145/3290607.3299061
https://akilian.com/2019/12/30/worker-in-the-loop-retrospective
A service is considered human-in-the-loop if it organizes its workflows with the intent to introduce models or heuristics that learn from the work of the humans executing the workflows. In this post, I will make reference to two common forms of human-in-the-loop:
• User-in-the-loop (UITL): The end-user is interacting with suggestions from a software heuristic/ML system.
• Worker-in-the-loop (WITL): A worker is paid to monitor suggestions from a software heuristic/ML system developed by the same company that pays the worker, but for the ultimate benefit of an end-user.
A service is considered human-in-the-loop if it organizes its workflows with the intent to introduce models or heuristics that learn from the work of the humans executing the workflows. In this post, I will make reference to two common forms of human-in-the-loop:
• User-in-the-loop (UITL): The end-user is interacting with suggestions from a software heuristic/ML system.
• Worker-in-the-loop (WITL): A worker is paid to monitor suggestions from a software heuristic/ML system developed by the same company that pays the worker, but for the ultimate benefit of an end-user.