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.
Forwarded from addmeto
Вы наверняка пропустили, но фейсбук запустил очередной сумасшедший проект - про коллективные предсказания. Идея прямо неплохая, но думаю как обычно, увязнет в том что денег из этого не добыть https://npe.fb.com/2020/06/23/forecast-a-community-for-crowdsourced-predictions-and-collective-insights/?ref=producthunt