Results 51 to 60 of about 6,437 (212)

Why Should We Study the Foreign Language Effect: Debiasing through Affecting Metacognition?

Journal of Intelligence, 2023
Debiasing is a method of improving people’s decisions by reducing their reliance on salient intuitions causing them to behave suboptimally or biasedly.
Michał Białek
doaj   +1 more source

Towards Debiasing Sentence Representations [PDF]

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020
ACL 2020, code available at https://github.com/pliang279 ...
Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, Louis-Philippe Morency   +5 more
openaire   +2 more sources

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Applied Sciences, 2019
Deep neural networks (DNNs) have been quite successful in solving many complex learning problems. However, DNNs tend to have a large number of learning parameters, leading to a large memory and computation requirement.
Sangkyun Lee, Jeonghyun Lee
doaj   +1 more source

Debiasing System 1: Training favours logical over stereotypical intuiting

Judgment and Decision Making, 2022
Whereas people’s reasoning is often biased by intuitive stereotypical associations, recent debiasing studies suggest that performance can be boosted by short training interventions that stress the underlying problem logic.
Esther Boissin, Serge Caparos, Aikaterini Voudouri, Wim De Neys   +3 more
doaj   +1 more source

Debiasing isn't enough! -- On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Tasks

CoRR, 2022
COLING ...
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki   +2 more
openaire   +3 more sources

Enhancing misinformation correction: New variants and a combination of awareness training and counter-speech to mitigate belief perseverance bias.

PLoS ONE
Belief perseverance bias refers to individuals' tendency to persevere in biased opinions even after the misinformation that initially shaped those opinions has been retracted.
Jana Siebert, Johannes Ulrich Siebert
doaj   +1 more source

Cognitive Biases in Critical Decisions Facing SME Entrepreneurs: An External Accountants’ Perspective

Administrative Sciences, 2020
Decisions by small and medium enterprise (SME) entrepreneurs are plagued by a variety of cognitive biases. Extant literature has mainly focused on a limited number of important biases (e.g., overconfidence) in a handful of important entrepreneurial ...
Arno Nuijten, Nick Benschop, Antoinette Rijsenbilt, Kristinka Wilmink   +3 more
doaj   +1 more source

Can AI debias the news? LLM interventions improve cross-partisan receptivity but LLMs overestimate their own effectiveness

Computers in Human Behavior Reports
Partisan news media erode cross-partisan trust, but large language models (LLMs) offer a potential means of debiasing such content at scale. Across two pre-registered experiments, we tested whether LLM-generated debiasing of liberal news headlines could ...
Faisal Feroz, Jonas R. Kunst
doaj   +1 more source

Is it time for studying real-life debiasing? Evaluation of the effectiveness of an analogical intervention technique

Frontiers in Psychology, 2015
The aim of this study was to initiate the exploration of debiasing methods applicable in real-life settings for achieving lasting improvement in decision-making competence regarding multiple decision biases.
Balazs eAczel, Bence eBago, Aba eSzollosi, Andrei eFoldes, Bence eLukacs   +4 more
doaj   +1 more source

Debiasing Conditional Stochastic Optimization

Advances in Neural Information Processing Systems 36, 2023
In this paper, we study the conditional stochastic optimization (CSO) problem which covers a variety of applications including portfolio selection, reinforcement learning, robust learning, causal inference, etc. The sample-averaged gradient of the CSO objective is biased due to its nested structure, and therefore requires a high sample complexity for ...
Lie He, Shiva Prasad Kasiviswanathan
openaire   +3 more sources