112 research outputs found
Interpretable and Steerable Sequence Learning via Prototypes
One of the major challenges in machine learning nowadays is to provide
predictions with not only high accuracy but also user-friendly explanations.
Although in recent years we have witnessed increasingly popular use of deep
neural networks for sequence modeling, it is still challenging to explain the
rationales behind the model outputs, which is essential for building trust and
supporting the domain experts to validate, critique and refine the model. We
propose ProSeNet, an interpretable and steerable deep sequence model with
natural explanations derived from case-based reasoning. The prediction is
obtained by comparing the inputs to a few prototypes, which are exemplar cases
in the problem domain. For better interpretability, we define several criteria
for constructing the prototypes, including simplicity, diversity, and sparsity
and propose the learning objective and the optimization procedure. ProSeNet
also provides a user-friendly approach to model steering: domain experts
without any knowledge on the underlying model or parameters can easily
incorporate their intuition and experience by manually refining the prototypes.
We conduct experiments on a wide range of real-world applications, including
predictive diagnostics for automobiles, ECG, and protein sequence
classification and sentiment analysis on texts. The result shows that ProSeNet
can achieve accuracy on par with state-of-the-art deep learning models. We also
evaluate the interpretability of the results with concrete case studies.
Finally, through user study on Amazon Mechanical Turk (MTurk), we demonstrate
that the model selects high-quality prototypes which align well with human
knowledge and can be interactively refined for better interpretability without
loss of performance.Comment: Accepted as a full paper at KDD 2019 on May 8, 201
What Symptoms and How Long? An Interpretable AI Approach for Depression Detection in Social Media
Depression is the most prevalent and serious mental illness, which induces grave financial and societal ramifications. Depression detection is key for early intervention to mitigate those consequences. Such a high-stake decision inherently necessitates interpretability. Although a few depression detection studies attempt to explain the decision, these explanations misalign with the clinical depression diagnosis criterion that is based on depressive symptoms. To fill this gap, we develop a novel Multi-Scale Temporal Prototype Network (MSTPNet). MSTPNet innovatively detects and interprets depressive symptoms as well as how long they last. Extensive empirical analyses show that MSTPNet outperforms state-of-the-art depression detection methods. This result also reveals new symptoms that are unnoted in the survey approach. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study contributes to IS literature with a novel interpretable deep learning model for depression detection in social media
Interpretable Sequence Classification Via Prototype Trajectory
We propose a novel interpretable recurrent neural network (RNN) model, called
ProtoryNet, in which we introduce a new concept of prototype trajectories.
Motivated by the prototype theory in modern linguistics, ProtoryNet makes a
prediction by finding the most similar prototype for each sentence in a text
sequence and feeding an RNN backbone with the proximity of each of the
sentences to the prototypes. The RNN backbone then captures the temporal
pattern of the prototypes, to which we refer as prototype trajectories. The
prototype trajectories enable intuitive, fine-grained interpretation of how the
model reached to the final prediction, resembling the process of how humans
analyze paragraphs. Experiments conducted on multiple public data sets reveal
that the proposed method not only is more interpretable but also is more
accurate than the current state-of-the-art prototype-based method. Furthermore,
we report a survey result indicating that human users find ProtoryNet more
intuitive and easier to understand, compared to the other prototype-based
methods
Understanding and Evaluating Policies for Sequential Decision-Making
Sequential-decision making is a critical component of many complex systems, such as finance, healthcare, and robotics. The long-term goal of a sequential decision-making process is to optimize the policy under which decisions are made. In safety-critical domains, the search for an optimal policy must be based on observational data, as new decision-making strategies need to be carefully evaluated before they can be tested in practice. In this thesis, we highlight the importance of understanding sequential decision-making at different stages of this procedure. For example, to assess which policies can be evaluated with the available data, we need to understand the policy that actually generated the data. And once we are given a policy to evaluate, we need to understand how it differs from current practice.First, we focus on the evaluation process, where a target policy is evaluated using off-policy data collected under a different so-called behavior policy. This problem, commonly referred to as off-policy evaluation, is often solved with importance sampling (IS) techniques. Despite their popularity, IS-based methods suffer from high variance and are hard to diagnose. To address these issues, we propose estimating the behavior policy using prototype learning. Using the learned prototypes, we describe differences between target and behavior policies, allowing for better assessment of the IS estimates.Next, we take a clinical direction and study the sequential treatment of patients with rheumatoid arthritis (RA). The armamentarium of disease-modifying anti-rheumatic drugs (DMARDs) for RA patients has greatly expanded over the past decades. However, it is still unclear which treatment work best for individual patients. To examine how observational data can be used to evaluate new policies, we describe the most common patterns of DMARDs in a large patient registry from the US. We find that the number of unique patterns is large, indicating a significant variation in clinical practice which can be exploited for evaluation purposes. However, additional assumptions may be required to arrive at statistically sound results
Robust Text Classification: Analyzing Prototype-Based Networks
Downstream applications often require text classification models to be
accurate, robust, and interpretable. While the accuracy of the stateof-the-art
language models approximates human performance, they are not designed to be
interpretable and often exhibit a drop in performance on noisy data. The family
of PrototypeBased Networks (PBNs) that classify examples based on their
similarity to prototypical examples of a class (prototypes) is natively
interpretable and shown to be robust to noise, which enabled its wide usage for
computer vision tasks. In this paper, we study whether the robustness
properties of PBNs transfer to text classification tasks. We design a modular
and comprehensive framework for studying PBNs, which includes different
backbone architectures, backbone sizes, and objective functions. Our evaluation
protocol assesses the robustness of models against character-, word-, and
sentence-level perturbations. Our experiments on three benchmarks show that the
robustness of PBNs transfers to NLP classification tasks facing realistic
perturbations. Moreover, the robustness of PBNs is supported mostly by the
objective function that keeps prototypes interpretable, while the robustness
superiority of PBNs over vanilla models becomes more salient as datasets get
more complex
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
While deep reinforcement learning has proven to be successful in solving
control tasks, the "black-box" nature of an agent has received increasing
concerns. We propose a prototype-based post-hoc policy explainer, ProtoX, that
explains a blackbox agent by prototyping the agent's behaviors into scenarios,
each represented by a prototypical state. When learning prototypes, ProtoX
considers both visual similarity and scenario similarity. The latter is unique
to the reinforcement learning context, since it explains why the same action is
taken in visually different states. To teach ProtoX about visual similarity, we
pre-train an encoder using contrastive learning via self-supervised learning to
recognize states as similar if they occur close together in time and receive
the same action from the black-box agent. We then add an isometry layer to
allow ProtoX to adapt scenario similarity to the downstream task. ProtoX is
trained via imitation learning using behavior cloning, and thus requires no
access to the environment or agent. In addition to explanation fidelity, we
design different prototype shaping terms in the objective function to encourage
better interpretability. We conduct various experiments to test ProtoX. Results
show that ProtoX achieved high fidelity to the original black-box agent while
providing meaningful and understandable explanations
PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction
Email is a widely used tool for business communication, and email marketing
has emerged as a cost-effective strategy for enterprises. While previous
studies have examined factors affecting email marketing performance, limited
research has focused on understanding email response behavior by considering
email content and metadata. This study proposes a Prototype-based Multi-view
Network (PROMINET) that incorporates semantic and structural information from
email data. By utilizing prototype learning, the PROMINET model generates
latent exemplars, enabling interpretable email response prediction. The model
maps learned semantic and structural exemplars to observed samples in the
training data at different levels of granularity, such as document, sentence,
or phrase. The approach is evaluated on two real-world email datasets: the
Enron corpus and an in-house Email Marketing corpus. Experimental results
demonstrate that the PROMINET model outperforms baseline models, achieving a
~3% improvement in F1 score on both datasets. Additionally, the model provides
interpretability through prototypes at different granularity levels while
maintaining comparable performance to non-interpretable models. The learned
prototypes also show potential for generating suggestions to enhance email text
editing and improve the likelihood of effective email responses. This research
contributes to enhancing sender-receiver communication and customer engagement
in email interactions.Comment: Accepted at EMNLP 2023 (industry
Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT
Health sensing for chronic disease management creates immense benefits for
social welfare. Existing health sensing studies primarily focus on the
prediction of physical chronic diseases. Depression, a widespread complication
of chronic diseases, is however understudied. We draw on the medical literature
to support depression prediction using motion sensor data. To connect human
expertise in the decision-making, safeguard trust for this high-stake
prediction, and ensure algorithm transparency, we develop an interpretable deep
learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon
the emergent prototype learning models. To accommodate the temporal
characteristic of sensor data and the progressive property of depression,
TempPNet differs from existing prototype learning models in its capability of
capturing the temporal progression of depression. Extensive empirical analyses
using real-world motion sensor data show that TempPNet outperforms
state-of-the-art benchmarks in depression prediction. Moreover, TempPNet
interprets its predictions by visualizing the temporal progression of
depression and its corresponding symptoms detected from sensor data. We further
conduct a user study to demonstrate its superiority over the benchmarks in
interpretability. This study offers an algorithmic solution for impactful
social good - collaborative care of chronic diseases and depression in health
sensing. Methodologically, it contributes to extant literature with a novel
interpretable deep learning model for depression prediction from sensor data.
Patients, doctors, and caregivers can deploy our model on mobile devices to
monitor patients' depression risks in real-time. Our model's interpretability
also allows human experts to participate in the decision-making by reviewing
the interpretation of prediction outcomes and making informed interventions.Comment: 39 pages, 12 figure
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