40,603 research outputs found
Visually-Aware Personalized Recommendation using Interpretable Image Representations
Visually-aware recommender systems use visual signals present in the
underlying data to model the visual characteristics of items and users'
preferences towards them. In the domain of clothing recommendation,
incorporating items' visual information (e.g., product images) is particularly
important since clothing item appearance is often a critical factor in
influencing the user's purchasing decisions. Current state-of-the-art
visually-aware recommender systems utilize image features extracted from
pre-trained deep convolutional neural networks, however these extremely
high-dimensional representations are difficult to interpret, especially in
relation to the relatively low number of visual properties that may guide
users' decisions.
In this paper we propose a novel approach to personalized clothing
recommendation that models the dynamics of individual users' visual
preferences. By using interpretable image representations generated with a
unique feature learning process, our model learns to explain users' prior
feedback in terms of their affinity towards specific visual attributes and
styles. Our approach achieves state-of-the-art performance on personalized
ranking tasks, and the incorporation of interpretable visual features allows
for powerful model introspection, which we demonstrate by using an interactive
recommendation algorithm and visualizing the rise and fall of fashion trends
over time.Comment: AI for Fashion workshop, held in conjunction with KDD 2018, London. 4
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Maintaining The Humanity of Our Models
Artificial intelligence and machine learning have been major research
interests in computer science for the better part of the last few decades.
However, all too recently, both AI and ML have rapidly grown to be media
frenzies, pressuring companies and researchers to claim they use these
technologies. As ML continues to percolate into daily life, we, as computer
scientists and machine learning researchers, are responsible for ensuring we
clearly convey the extent of our work and the humanity of our models.
Regularizing ML for mass adoption requires a rigorous standard for model
interpretability, a deep consideration for human bias in data, and a
transparent understanding of a model's societal effects.Comment: Accepted into the 2018 AAAI Spring Symposium: AI and Society: Ethics,
Safety and Trustworthiness in Intelligent Agent
Open the Black Box Data-Driven Explanation of Black Box Decision Systems
Black box systems for automated decision making, often based on machine
learning over (big) data, map a user's features into a class or a score without
exposing the reasons why. This is problematic not only for lack of
transparency, but also for possible biases hidden in the algorithms, due to
human prejudices and collection artifacts hidden in the training data, which
may lead to unfair or wrong decisions. We introduce the local-to-global
framework for black box explanation, a novel approach with promising early
results, which paves the road for a wide spectrum of future developments along
three dimensions: (i) the language for expressing explanations in terms of
highly expressive logic-based rules, with a statistical and causal
interpretation; (ii) the inference of local explanations aimed at revealing the
logic of the decision adopted for a specific instance by querying and auditing
the black box in the vicinity of the target instance; (iii), the bottom-up
generalization of the many local explanations into simple global ones, with
algorithms that optimize the quality and comprehensibility of explanations
Try This Instead: Personalized and Interpretable Substitute Recommendation
As a fundamental yet significant process in personalized recommendation,
candidate generation and suggestion effectively help users spot the most
suitable items for them. Consequently, identifying substitutable items that are
interchangeable opens up new opportunities to refine the quality of generated
candidates. When a user is browsing a specific type of product (e.g., a laptop)
to buy, the accurate recommendation of substitutes (e.g., better equipped
laptops) can offer the user more suitable options to choose from, thus
substantially increasing the chance of a successful purchase. However, existing
methods merely treat this problem as mining pairwise item relationships without
the consideration of users' personal preferences. Moreover, the substitutable
relationships are implicitly identified through the learned latent
representations of items, leading to uninterpretable recommendation results. In
this paper, we propose attribute-aware collaborative filtering (A2CF) to
perform substitute recommendation by addressing issues from both
personalization and interpretability perspectives. Instead of directly
modelling user-item interactions, we extract explicit and polarized item
attributes from user reviews with sentiment analysis, whereafter the
representations of attributes, users, and items are simultaneously learned.
Then, by treating attributes as the bridge between users and items, we can
thoroughly model the user-item preferences (i.e., personalization) and
item-item relationships (i.e., substitution) for recommendation. In addition,
A2CF is capable of generating intuitive interpretations by analyzing which
attributes a user currently cares the most and comparing the recommended
substitutes with her/his currently browsed items at an attribute level. The
recommendation effectiveness and interpretation quality of A2CF are
demonstrated via extensive experiments on three real datasets.Comment: To appear in SIGIR'2
Techniques for Interpretable Machine Learning
Interpretable machine learning tackles the important problem that humans
cannot understand the behaviors of complex machine learning models and how
these models arrive at a particular decision. Although many approaches have
been proposed, a comprehensive understanding of the achievements and challenges
is still lacking. We provide a survey covering existing techniques to increase
the interpretability of machine learning models. We also discuss crucial issues
that the community should consider in future work such as designing
user-friendly explanations and developing comprehensive evaluation metrics to
further push forward the area of interpretable machine learning.Comment: Accepted by Communications of the ACM (CACM), Review Articl
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
Black box machine learning models are currently being used for high stakes
decision-making throughout society, causing problems throughout healthcare,
criminal justice, and in other domains. People have hoped that creating methods
for explaining these black box models will alleviate some of these problems,
but trying to \textit{explain} black box models, rather than creating models
that are \textit{interpretable} in the first place, is likely to perpetuate bad
practices and can potentially cause catastrophic harm to society. There is a
way forward -- it is to design models that are inherently interpretable. This
manuscript clarifies the chasm between explaining black boxes and using
inherently interpretable models, outlines several key reasons why explainable
black boxes should be avoided in high-stakes decisions, identifies challenges
to interpretable machine learning, and provides several example applications
where interpretable models could potentially replace black box models in
criminal justice, healthcare, and computer vision.Comment: Author's pre-publication version of a 2019 Nature Machine
Intelligence article. Shorter Version was published in NIPS 2018 Workshop on
Critiquing and Correcting Trends in Machine Learning. Expands also on NSF
Statistics at a Crossroads Webina
Explainability in Human-Agent Systems
This paper presents a taxonomy of explainability in Human-Agent Systems. We
consider fundamental questions about the Why, Who, What, When and How of
explainability. First, we define explainability, and its relationship to the
related terms of interpretability, transparency, explicitness, and
faithfulness. These definitions allow us to answer why explainability is needed
in the system, whom it is geared to and what explanations can be generated to
meet this need. We then consider when the user should be presented with this
information. Last, we consider how objective and subjective measures can be
used to evaluate the entire system. This last question is the most encompassing
as it will need to evaluate all other issues regarding explainability
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
Translating machine learning (ML) models effectively to clinical practice
requires establishing clinicians' trust. Explainability, or the ability of an
ML model to justify its outcomes and assist clinicians in rationalizing the
model prediction, has been generally understood to be critical to establishing
trust. However, the field suffers from the lack of concrete definitions for
usable explanations in different settings. To identify specific aspects of
explainability that may catalyze building trust in ML models, we surveyed
clinicians from two distinct acute care specialties (Intenstive Care Unit and
Emergency Department). We use their feedback to characterize when
explainability helps to improve clinicians' trust in ML models. We further
identify the classes of explanations that clinicians identified as most
relevant and crucial for effective translation to clinical practice. Finally,
we discern concrete metrics for rigorous evaluation of clinical explainability
methods. By integrating perceptions of explainability between clinicians and ML
researchers we hope to facilitate the endorsement and broader adoption and
sustained use of ML systems in healthcare
Explainable Machine Learning for Scientific Insights and Discoveries
Machine learning methods have been remarkably successful for a wide range of
application areas in the extraction of essential information from data. An
exciting and relatively recent development is the uptake of machine learning in
the natural sciences, where the major goal is to obtain novel scientific
insights and discoveries from observational or simulated data. A prerequisite
for obtaining a scientific outcome is domain knowledge, which is needed to gain
explainability, but also to enhance scientific consistency. In this article we
review explainable machine learning in view of applications in the natural
sciences and discuss three core elements which we identified as relevant in
this context: transparency, interpretability, and explainability. With respect
to these core elements, we provide a survey of recent scientific works that
incorporate machine learning and the way that explainable machine learning is
used in combination with domain knowledge from the application areas
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning
This work develops a methodology for creating a data-driven digital twin from
a library of physics-based models representing various asset states. The
digital twin is updated using interpretable machine learning. Specifically, we
use optimal trees---a recently developed scalable machine learning method---to
train an interpretable data-driven classifier. Training data for the classifier
are generated offline using simulated scenarios solved by the library of
physics-based models. These data can be further augmented using experimental or
other historical data. In operation, the classifier uses observational data
from the asset to infer which physics-based models in the model library are the
best candidates for the updated digital twin. The approach is demonstrated
through the development of a structural digital twin for a 12ft wingspan
unmanned aerial vehicle. This digital twin is built from a library of
reduced-order models of the vehicle in a range of structural states. The
data-driven digital twin dynamically updates in response to structural damage
or degradation and enables the aircraft to replan a safe mission accordingly.
Within this context, we study the performance of the optimal tree classifiers
and demonstrate how their interpretability enables explainable structural
assessments from sparse sensor measurements, and also informs optimal sensor
placement.Comment: 20 pages, 13 figures, submitted to AIAA Journa
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