The Intuitive Appeal of Explainable Machines

Algorithmic decision-making has become synonymous with inexplicable decision-making, but what makes algorithms so difficult to explain? This Article examines what sets machine learning apart from other ways of developing rules for decision-making and the problem these properties pose for explanation. We show that machine learning models can be both inscrutable and nonintuitive and that these are related, but distinct, properties. Calls for explanation have treated these problems as one and the same, but disentangling the two reveals that they demand very different responses. Dealing with inscrutability requires providing a sensible description of the rules; addressing nonintuitiveness requires providing a satisfying explanation for why the rules are what they are. Existing laws like the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA), and the General Data Protection Regulation (GDPR), as well as techniques within machine learning, are focused almost entirely on the problem of inscrutability. While such techniques could allow a machine learning system to comply with existing law, doing so may not help if the goal is to assess whether the basis for decision-making is normatively defensible. In most cases, intuition serves as the unacknowledged bridge between a descriptive account and a normative evaluation. But because machine learning is often valued for its ability to uncover statistical relationships that defy intuition, relying on intuition is not a satisfying approach. This Article thus argues for other mechanisms for normative evaluation. To know why the rules are what they are, one must seek explanations of the process behind a model’s development, not just explanations of the model itself

Explainable Text Classification Techniques in Legal Document Review: Locating Rationales without Using Human Annotated Training Text Snippets

US corporations regularly spend millions of dollars reviewing electronically-stored documents in legal matters. Recently, attorneys apply text classification to efficiently cull massive volumes of data to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs of legal matters, it also faces a perception challenge: amongst lawyers, this technology is sometimes looked upon as a "black box". Put simply, no extra information is provided for attorneys to understand why documents are classified as responsive. In recent years, explainable machine learning has emerged as an active research area. In an explainable machine learning system, predictions or decisions made by a machine learning model are human understandable. In legal 'document review' scenarios, a document is responsive, because one or more of its small text snippets are deemed responsive. In these scenarios, if these responsive snippets can be located, then attorneys could easily evaluate the model's document classification decisions - this is especially important in the field of responsible AI. Our prior research identified that predictive models created using annotated training text snippets improved the precision of a model when compared to a model created using all of a set of documents' text as training. While interesting, manually annotating training text snippets is not generally practical during a legal document review. However, small increases in precision can drastically decrease the cost of large document reviews. Automating the identification of training text snippets without human review could then make the application of training text snippet-based models a practical approach.Comment: arXiv admin note: text overlap with arXiv:1912.0950

OBOE: an Explainable Text Classification Framework

Explainable Artificial Intelligence (XAI) has recently gained visibility as one of the main topics of Artificial Intelligence research due to, among others, the need to provide a meaningful justification of the reasons behind the decision of black-box algorithms. Current approaches are based on model agnostic or ad-hoc solutions and, although there are frameworks that define workflows to generate meaningful explanations, a text classification framework that provides such explanations considering the different ingredients involved in the classification process (data, model, explanations, and users) is still missing. With the intention of covering this research gap, in this paper we present a text classification framework called OBOE (explanatiOns Based On concEpts), in which such ingredients play an active role to open the black-box. OBOE defines different components whose implementation can be customized and, thus, explanations are adapted to specific contexts. We also provide a tailored implementation to show the customization capability of OBOE. Additionally, we performed (a) a validation of the implemented framework to evaluate the performance using different corpora and (b) a user-based evaluation of the explanations provided by OBOE. The latter evaluation shows that the explanations generated in natural language express the reason for the classification results in a way that is comprehensible to non-technical users

Technology Assisted Review of Legal Documents

A legal prediction-based approach will help judges and solicitors to take judicial decisions on current cases, which are going on in courts, and make predictions on new cases on the basis of existing references and judgments. This model also helps law students learn about legal references. This application was developed specifically for the “Supreme Court of Pakistan (SCP)” and the “Pakistan Bar Council (PBC)” to expedite their judgments and provide legal guidance to lawyers based on historical data and constitutions

Explainable and Discourse Topic-aware Neural Language Understanding

Marrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics. While introducing topical semantics in language models, existing approaches incorporate latent document topic proportions and ignore topical discourse in sentences of the document. This work extends the line of research by additionally introducing an explainable topic representation in language understanding, obtained from a set of key terms correspondingly for each latent topic of the proportion. Moreover, we retain sentence-topic associations along with document-topic association by modeling topical discourse for every sentence in the document. We present a novel neural composite language model that exploits both the latent and explainable topics along with topical discourse at sentence-level in a joint learning framework of topic and language models. Experiments over a range of tasks such as language modeling, word sense disambiguation, document classification, retrieval and text generation demonstrate ability of the proposed model in improving language understanding.Comment: Accepted at ICML2020 (13 pages, 2 figures), acknowledgements adde

Explainable Risk Classification in Financial Reports

Every publicly traded company in the US is required to file an annual 10-K financial report, which contains a wealth of information about the company. In this paper, we propose an explainable deep-learning model, called FinBERT-XRC, that takes a 10-K report as input, and automatically assesses the post-event return volatility risk of its associated company. In contrast to previous systems, our proposed model simultaneously offers explanations of its classification decision at three different levels: the word, sentence, and corpus levels. By doing so, our model provides a comprehensive interpretation of its prediction to end users. This is particularly important in financial domains, where the transparency and accountability of algorithmic predictions play a vital role in their application to decision-making processes. Aside from its novel interpretability, our model surpasses the state of the art in predictive accuracy in experiments on a large real-world dataset of 10-K reports spanning six years

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security