88 research outputs found
Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals
Recent studies have shown that information disclosed on social network sites
(such as Facebook) can be used to predict personal characteristics with
surprisingly high accuracy. In this paper we examine a method to give online
users transparency into why certain inferences are made about them by
statistical models, and control to inhibit those inferences by hiding
("cloaking") certain personal information from inference. We use this method to
examine whether such transparency and control would be a reasonable goal by
assessing how difficult it would be for users to actually inhibit inferences.
Applying the method to data from a large collection of real users on Facebook,
we show that a user must cloak only a small portion of her Facebook Likes in
order to inhibit inferences about their personal characteristics. However, we
also show that in response a firm could change its modeling of users to make
cloaking more difficult.Comment: presented at 2016 ICML Workshop on Human Interpretability in Machine
Learning (WHI 2016), New York, N
Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding
In today's legal environment, lawsuits and regulatory investigations require
companies to embark upon increasingly intensive data-focused engagements to
identify, collect and analyze large quantities of data. When documents are
staged for review the process can require companies to dedicate an
extraordinary level of resources, both with respect to human resources, but
also with respect to the use of technology-based techniques to intelligently
sift through data. For several years, attorneys have been using a variety of
tools to conduct this exercise, and most recently, they are accepting the use
of machine learning techniques like text classification to efficiently cull
massive volumes of data to identify responsive documents for use in these
matters. In recent years, a group of AI and Machine Learning researchers have
been actively researching Explainable AI. In an explainable AI system, actions
or decisions are human understandable. In typical legal `document review'
scenarios, a document can be identified as responsive, as long as one or more
of the text snippets in a document are deemed responsive. In these scenarios,
if predictive coding can be used to locate these responsive snippets, then
attorneys could easily evaluate the model's document classification decision.
When deployed with defined and explainable results, predictive coding can
drastically enhance the overall quality and speed of the document review
process by reducing the time it takes to review documents. The authors of this
paper propose the concept of explainable predictive coding and simple
explainable predictive coding methods to locate responsive snippets within
responsive documents. We also report our preliminary experimental results using
the data from an actual legal matter that entailed this type of document
review.Comment: 2018 IEEE International Conference on Big Dat
Inverse Classification for Comparison-based Interpretability in Machine Learning
In the context of post-hoc interpretability, this paper addresses the task of
explaining the prediction of a classifier, considering the case where no
information is available, neither on the classifier itself, nor on the
processed data (neither the training nor the test data). It proposes an
instance-based approach whose principle consists in determining the minimal
changes needed to alter a prediction: given a data point whose classification
must be explained, the proposed method consists in identifying a close
neighbour classified differently, where the closeness definition integrates a
sparsity constraint. This principle is implemented using observation generation
in the Growing Spheres algorithm. Experimental results on two datasets
illustrate the relevance of the proposed approach that can be used to gain
knowledge about the classifier.Comment: preprin
Cody: An Interactive Machine Learning System for Qualitative Coding
Qualitative coding, the process of assigning labels to text as part of qualitative analysis, is time-consuming and repetitive, especially for large datasets. While available QDAS sometimes allows the semi-automated extension of annotations to unseen data, recent user studies revealed critical issues. In particular, the integration of automated code suggestions into the coding process is not transparent and interactive. In this work, we present Cody, a system for semi-automated qualitative coding that suggests codes based on human-defined coding rules and supervised machine learning (ML). Suggestions and rules can be revised iteratively by users in a lean interface that provides explanations for code suggestions. In a preliminary evaluation, 42% of all documents could be coded automatically based on code rules. Cody is the first coding system to allow users to define query-style code rules in combination with supervised ML. Thereby, users can extend manual annotations to unseen data to improve coding speed and quality
Knowledge-based Transfer Learning Explanation
Machine learning explanation can significantly boost machine learning's
application in decision making, but the usability of current methods is limited
in human-centric explanation, especially for transfer learning, an important
machine learning branch that aims at utilizing knowledge from one learning
domain (i.e., a pair of dataset and prediction task) to enhance prediction
model training in another learning domain. In this paper, we propose an
ontology-based approach for human-centric explanation of transfer learning.
Three kinds of knowledge-based explanatory evidence, with different
granularities, including general factors, particular narrators and core
contexts are first proposed and then inferred with both local ontologies and
external knowledge bases. The evaluation with US flight data and DBpedia has
presented their confidence and availability in explaining the transferability
of feature representation in flight departure delay forecasting.Comment: Accepted by International Conference on Principles of Knowledge
Representation and Reasoning, 201
Towards a model- and data-focused taxonomy of XAI systems
Explainable Artificial Intelligence (XAI) is currently an important topic for the application of Machine Learning (ML) in high-stakes decision scenarios. Related research focuses on evaluating ML algorithms in terms of interpretability. However, providing a human understandable explanation of an intelligent system does not only relate to the used ML algorithm. The data and features used also have a considerable impact on interpretability. In this paper, we develop a taxonomy for describing XAI systems based on aspects about the algorithm and data. The proposed taxonomy gives researchers and practitioners opportunities to describe and evaluate current XAI systems with respect to interpretability and guides the future development of this class of systems
A Communicative Action Framework for Discourse Strategies for AI-based Systems: The MetTrains Application Case
Increasing attention is being paid to the challenges of how artificial intelligence (AI)-based systems offer explanations to users. Explanation capabilities developed for older logic-based systems still have relevance, but new thinking is needed in designing explanations and other discourse strategies for new forms of AI that include machine learning. In this work-in-progress paper we show how a communicative action design framework can be used to design an AI-based system’s interface to achieve desired goals. The applicability of the framework is demonstrated with an interface for an intelligent video surveillance system for reducing railway suicide. The communicative action framework is an important step in theory development for human-computer interaction with AI as used in the information systems domain
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach
We examine counterfactual explanations for explaining the decisions made by
model-based AI systems. The counterfactual approach we consider defines an
explanation as a set of the system's data inputs that causally drives the
decision (i.e., changing the inputs in the set changes the decision) and is
irreducible (i.e., changing any subset of the inputs does not change the
decision). We (1) demonstrate how this framework may be used to provide
explanations for decisions made by general, data-driven AI systems that may
incorporate features with arbitrary data types and multiple predictive models,
and (2) propose a heuristic procedure to find the most useful explanations
depending on the context. We then contrast counterfactual explanations with
methods that explain model predictions by weighting features according to their
importance (e.g., SHAP, LIME) and present two fundamental reasons why we should
carefully consider whether importance-weight explanations are well-suited to
explain system decisions. Specifically, we show that (i) features that have a
large importance weight for a model prediction may not affect the corresponding
decision, and (ii) importance weights are insufficient to communicate whether
and how features influence decisions. We demonstrate this with several concise
examples and three detailed case studies that compare the counterfactual
approach with SHAP to illustrate various conditions under which counterfactual
explanations explain data-driven decisions better than importance weights
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