Declarative Reasoning on Explanations Using Constraint Logic Programming

Explaining opaque Machine Learning (ML) models is an increasingly relevant problem. Current explanation in AI (XAI) methods suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of abstraction and interactivity with the user. We propose REASONX, an explanation method based on Constraint Logic Programming (CLP). REASONX can provide declarative, interactive explanations for decision trees, which can be the ML models under analysis or global/local surrogate models of any black-box model. Users can express background or common sense knowledge using linear constraints and MILP optimization over features of factual and contrastive instances, and interact with the answer constraints at different levels of abstraction through constraint projection. We present here the architecture of REASONX, which consists of a Python layer, closer to the user, and a CLP layer. REASONX's core execution engine is a Prolog meta-program with declarative semantics in terms of logic theories.Comment: European Conference on Logics in Artificial Intelligence (JELIA 2023

Reason to explain: Interactive contrastive explanations (REASONX)

Many high-performing machine learning models are not interpretable. As they are increasingly used in decision scenarios that can critically affect individuals, it is necessary to develop tools to better understand their outputs. Popular explanation methods include contrastive explanations. However, they suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of interactivity. While (dialogue-like) interactivity is important to better communicate an explanation, background knowledge has the potential to significantly improve their quality, e.g., by adapting the explanation to the needs of the end-user. To close this gap, we present REASONX, an explanation tool based on Constraint Logic Programming (CLP). REASONX provides interactive contrastive explanations that can be augmented by background knowledge, and allows to operate under a setting of under-specified information, leading to increased flexibility in the provided explanations. REASONX computes factual and constrative decision rules, as well as closest constrative examples. It provides explanations for decision trees, which can be the ML models under analysis, or global/local surrogate models of any ML model. While the core part of REASONX is built on CLP, we also provide a program layer that allows to compute the explanations via Python, making the tool accessible to a wider audience. We illustrate the capability of REASONX on a synthetic data set, and on a a well-developed example in the credit domain. In both cases, we can show how REASONX can be flexibly used and tailored to the needs of the user.Comment: The 1st World Conference on eXplainable Artificial Intelligence (xAI 2023

Laura Moorby Tooke Correspondence

Entries include brief biographical information, a typed biographical letter of correspondence on plain typing paper from Tooke in reply to a request for information about Tooke for the Maine Library Bulletin sent with her recent book and notice of another title Dixie of the North sent to press, and a typed letter from the Maine State Library on receipt of Betty of New England for the Maine Author Collection

Janet Laura Scott Correspondence

Entries include correspondence with the Maine State Library concerning illustrations for an upcoming book on Scott\u27s artistic personal stationery

Adolescents In Crisis: A Geographic Exploration Of Help-Seeking Behavior Using Data From Crisis Text Line

Prior research has demonstrated that a variety of contextual factors, including age, gender, and socioeconomic status, influence the prevalence and severity of mental distress and help-seeking behaviors. Behavioral health research has been limited to survey data, but advances in technology have provided increased opportunities to continuously capture data and learn about the help-seeking habits of its contributors. In response to the growing prevalence of technologically mediated crisis counseling services, this study evaluated whether these same disparities exist for help-seeking via Crisis Text Line, a free, ubiquitous, technology-based counseling service. To date, this is the first national study to examine text-based help-seeking behavior among adolescents. Results identify several factors associated with increased or reduced help-seeking behavior among adolescents in the U.S. Increased CTL usage rates occur in counties with higher mean household incomes, higher divorce rates, and lower residential stability. Rurality was the strongest predictor for reduced help-seeking, and this finding is particularly concerning in light of elevated rates of suicide among rural counties. Low rates of help-seeking compound ongoing rural-urban disparities in traditional mental health services, and this finding suggests that increased suicide risk in rural areas cannot be explained by mental health professional shortages alone

Environmental Urgency: Apocalyptic Undercurrents In Appalachian Literature, Including All Places Thou: An Experimental Novella

Apocalyptic imagery and rhetoric appears across a variety of Appalachian literature and literature with Appalachian settings; however, comparatively little scholarly attention has been dedicated to exploring this trend, despite its provocative ecological implications. Using an ecocritical lens, I will first examine the apocalyptic undercurrent in Appalachian literature by analyzing its thematic significance to Ann Pancake's Strange as this Weather Has Been (2007) and Louise McNeill's Paradox Hill: From Appalachia to Lunar Shore (1972). I will then apply original narrative, verse, and selected artwork to a creative examination of these same thematic and symbolic trends. Ultimately, both critical and creative methodologies will indicate that apocalypticism - particularly in its contextualization of crisis in past, present, and future - provides a way for Appalachian literature to negotiate the ecological destruction and exploitation so prevalent in many parts of the region

Where We Have Gone Before: Star Trek Into and Out of Darkness

Star Trek functions as a religion though its universe is explicitly humanistic and secular. Star Trek Into Darkness offers an interpretation of 9/11 and the wars in Iraq and Afghanistan. While the creators may not have intended the film as a religious text, it offers an analysis of what happened, a set of responses, pointing to a path forward, incorporating those events into the Star Trek (and ultimately our own) universe. I will offer a close reading of Star Trek Into Darkness that explores the negotiation of what it means to be human and our place in the post- 9/11 world. My thesis is that the film can be read as implicitly religious in two senses. First, it offers a vision of what is human in the face of questions of terrorism and preemptive strikes, duty and honor, life and death. Second, it offers viewers a reflection on possible responses to 9/11 and the aftermath, pointing forward. It is a secular homily on being human in the past, present, and future

Bernardino de Sahagún, Jose de Acosta and the Sixteenth-Century Theology of Sacrifice in New Spain

No abstract availabl

Examination Of Gender And Age Differences In Disgust Sensitivity

Disgust sensitivity (DS) is the strength of response an individual has to a disgust-eliciting stimulus, such as feces or a bloody injury. It is believed that DS evolved as a way to prevent illness and to protect an individual from potentially harmful substances. Previous research suggests a relationship between gender and DS, as women tend to be higher in DS than men. The purpose of this study was to replicate this finding using a larger, representative sample, and to investigate levels of DS across the lifespan. Additionally, we hypothesized that younger women would have higher levels of DS than older women, particularly for the contamination and core DS subscales. Participants in this study included US citizens who were online Amazon MTurk workers. There were 1,339 participants, 804 women and 535 men, ages 18 to 83 years old. Participants completed the Disgust Scale, which included the Core Disgust, Animal-Reminder, and Contamination subscales. Multiple regression analysis demonstrated that gender more than age reliably predicted several different types of disgust sensitivity. In addition to several main effects, there was one interaction demonstrating that younger women were more likely to report higher levels the Contamination component of DS

Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal

Explainable artificial intelligence (XAI) provides explanations for not interpretable machine learning (ML) models. While many technical approaches exist, there is a lack of validation of these techniques on real-world datasets. In this work, we present a use-case of XAI: an ML model which is trained to estimate electrification rates based on mobile phone data in Senegal. The data originate from the Data for Development challenge by Orange in 2014/15. We apply two model-agnostic, local explanation techniques and find that while the model can be verified, it is biased with respect to the population density. We conclude our paper by pointing to the two main challenges we encountered during our work: data processing and model design that might be restricted by currently available XAI methods, and the importance of domain knowledge to interpret explanations.Comment: The 1st World Conference on eXplainable Artificial Intelligence (xAI 2023