7,432 research outputs found
TRACE: A Stigmergic Crowdsourcing Platform for Intelligence Analysis
Crowdsourcing has become a frequently adopted approach to solving various tasks from conducting surveys to designing products. In the field of reasoning-support, however, crowdsourcing-related research and application have not been extensively implemented. Reasoning-support is essential in intelligence analysis to help analysts mitigate various cognitive biases, enhance deliberation, and improve report writing. In this paper, we propose a novel approach to designing a crowdsourcing platform that facilitates stigmergic coordination, awareness, and communication for intelligence analysis. We have partly materialized our proposal in the form of a crowdsourcing system which supports intelligence analysis: TRACE (Trackable Reasoning and Analysis for Collaboration and Evaluation). We introduce several stigmergic approaches integrated into TRACE and discuss the potential experimentation of these approaches. We also explain the design implications for further development of TRACE and similar crowdsourcing systems to support reasoning
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
Quantifying the Impact of Cognitive Biases in Question-Answering Systems
Crowdsourcing can identify high-quality solutions to problems; however,
individual decisions are constrained by cognitive biases. We investigate some
of these biases in an experimental model of a question-answering system. In
both natural and controlled experiments, we observe a strong position bias in
favor of answers appearing earlier in a list of choices. This effect is
enhanced by three cognitive factors: the attention an answer receives, its
perceived popularity, and cognitive load, measured by the number of choices a
user has to process. While separately weak, these effects synergistically
amplify position bias and decouple user choices of best answers from their
intrinsic quality. We end our paper by discussing the novel ways we can apply
these findings to substantially improve how high-quality answers are found in
question-answering systems.Comment: 9 pages, 5 figure
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification
Crowdsourcing has become widely used in supervised scenarios where training
sets are scarce and difficult to obtain. Most crowdsourcing models in the
literature assume labelers can provide answers to full questions. In
classification contexts, full questions require a labeler to discern among all
possible classes. Unfortunately, discernment is not always easy in realistic
scenarios. Labelers may not be experts in differentiating all classes. In this
work, we provide a full probabilistic model for a shorter type of queries. Our
shorter queries only require "yes" or "no" responses. Our model estimates a
joint posterior distribution of matrices related to labelers' confusions and
the posterior probability of the class of every object. We developed an
approximate inference approach, using Monte Carlo Sampling and Black Box
Variational Inference, which provides the derivation of the necessary
gradients. We built two realistic crowdsourcing scenarios to test our model.
The first scenario queries for irregular astronomical time-series. The second
scenario relies on the image classification of animals. We achieved results
that are comparable with those of full query crowdsourcing. Furthermore, we
show that modeling labelers' failures plays an important role in estimating
true classes. Finally, we provide the community with two real datasets obtained
from our crowdsourcing experiments. All our code is publicly available.Comment: SIAM International Conference on Data Mining (SDM19), 9 official
pages, 5 supplementary page
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