Results 101 to 110 of about 1,603 (207)

Causal Models as a Scientific Framework for Next‐Generation Ecosystem and Climate‐Linked Stock Assessments

open access: yesFish and Fisheries, EarlyView.
ABSTRACT Rapid changes in marine ecosystems highlight the need to account for time‐varying productivity in stock assessments used to support fisheries management. Common approaches incorporate annual variation or regressing processes such as recruitment, natural mortality, or growth on environmental variables.
J. Champagnat   +6 more
wiley   +1 more source

Towards a unified search: Improving PubMed retrieval with full text. [PDF]

open access: yesJ Biomed Inform, 2022
Kim W   +4 more
europepmc   +1 more source

The Legacy of Policy Inaction in Climate‐Growth Models

open access: yesInternational Economic Review, EarlyView.
ABSTRACT To better understand the structure and core mechanisms of a broad class of climate‐growth models, we study a simplified version of the dynamic integrated model of climate and the economy (DICE) through the lens of growth theory. We analytically show that this model features a continuum of saddle‐point stable steady states.
Thomas Steger, Timo Trimborn
wiley   +1 more source

Conversational AI Agents: The Effect of Process and Outcome Variation on Anthropomorphism and Trust

open access: yesInformation Systems Journal, EarlyView.
ABSTRACT Organisations increasingly deploy conversational AI agents (CAs) in agentic roles where behavioural variations are inevitable. Prior work often conflates two distinct forms of variation: outcome variation (where success fluctuates) and process variation (where the path to completion varies).
Kambiz Saffarizadeh, Mark Keil
wiley   +1 more source

How to Demonstrate Trustworthy Use of AI in Public Services: A Case Study

open access: yesInformation Systems Journal, EarlyView.
ABSTRACT Government leaders across the globe are grappling with how to harness and integrate artificial intelligence (AI) to enhance public service delivery and efficiency. Yet, a key challenge faced is how to build and maintain the trust of stakeholders. Trust is critical for the acceptance and sustained adoption of AI technologies, as well as to gain
Natalie Smith   +5 more
wiley   +1 more source

Catch Me If You Can: The Dynamic Nature of Bias in Machine Learning Applications

open access: yesInformation Systems Journal, EarlyView.
ABSTRACT Bias in machine learning (ML) applications represents systematic differences between expected and actual values of the predicted outputs, such that certain individuals or groups are systematically and disproportionately (dis)advantaged. This paper investigates the dynamic nature of bias in ML applications.
Monideepa Tarafdar, Irina Rets, Yang Hu
wiley   +1 more source

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