How much of commonsense and legal reasoning is formalizable? A review of conceptual obstacles

Fifty years of effort in artificial intelligence (AI) and the formalization of legal reasoning have produced both successes and failures. Considerable success in organizing and displaying evidence and its interrelationships has been accompanied by failure to achieve the original ambition of AI as applied to law: fully automated legal decision-making. The obstacles to formalizing legal reasoning have proved to be the same ones that make the formalization of commonsense reasoning so difficult, and are most evident where legal reasoning has to meld with the vast web of ordinary human knowledge of the world. Underlying many of the problems is the mismatch between the discreteness of symbol manipulation and the continuous nature of imprecise natural language, of degrees of similarity and analogy, and of probabilities

Counterfactual Causality from First Principles?

In this position paper we discuss three main shortcomings of existing approaches to counterfactual causality from the computer science perspective, and sketch lines of work to try and overcome these issues: (1) causality definitions should be driven by a set of precisely specified requirements rather than specific examples; (2) causality frameworks should support system dynamics; (3) causality analysis should have a well-understood behavior in presence of abstraction.Comment: In Proceedings CREST 2017, arXiv:1710.0277

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Logic Programming as Constructivism

The features of logic programming that seem unconventional from the viewpoint of classical logic can be explained in terms of constructivistic logic. We motivate and propose a constructivistic proof theory of non-Horn logic programming. Then, we apply this formalization for establishing results of practical interest. First, we show that 'stratification can be motivated in a simple and intuitive way. Relying on similar motivations, we introduce the larger classes of 'loosely stratified' and 'constructively consistent' programs. Second, we give a formal basis for introducing quantifiers into queries and logic programs by defining 'constructively domain independent* formulas. Third, we extend the Generalized Magic Sets procedure to loosely stratified and constructively consistent programs, by relying on a 'conditional fixpoini procedure

Research in progress: report on the ICAIL 2017 doctoral consortium

This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences

Bringing action language C+ to normative contexts: preliminary report

C+ is an action language for specifying and reasoning about the e ects of actions and the persistence of facts over time. Based on it. we present CN+, an operational enhanced form of C+ designed for representing complex normative systems and integrate them easily into the semantics of the causal theory of actions. The proposed system contains a particular formalization of norms using a life-cycle approach to capture the whole normative meaning of a complex normative framework. We discuss this approach and illustrate it with examples.Peer ReviewedPostprint (author’s final draft

Reporting on plagues:Epidemiological reasoning in the early twentieth century

The beginning of modern, twentieth-century epidemiology is usually associated with the introduction of mathematical approaches and formal methods to the field. However, since the late nineteenth century, the nascent field of epidemiology not only developed statistical instruments and stochastic models, but also relied on new forms of narrative to make its claims. This chapter will ask how chronologies of outbreaks, the increasing complexity of causal models and statistical and geographical representations were brought together in epidemiological reasoning. The chapter focuses on three outbreak reports from the third plague pandemic as critical examples. Reports grappled with the unexpected return of a devastating menace from the past, while inadvertently shaping the contours of a modern, scientific argument. Epidemiological reasoning emphasized historical dimensions and temporal structures of epidemics and integrated formalized approaches with empirical descriptions while contributing to the growing rejection of mono-causal explanations for epidemics

Land reform and the formalization of household credit in rural Vietnam

This article evaluates the impact of land-use certificate (LUC) issuance on credit market outcomes of households in rural Vietnam. Given the absence of appropriate data for the creation of a baseline (e.g. for difference-in-difference estimation), we propose an alternative regression-based evaluation procedure hinging on two pivotal steps: Firstly, we express the covariates related to a change in LUC status in terms of the household specific economic, social and geographic environment at the time the change occurred. Secondly, we estimate the propensity score to account for systematic pretreatment differences between households in the observational data. Conditional on the propensity score, we estimate the causal effect of LUC status on borrowing outcomes. We find that LUCs have a strong positive effect on formal borrowing, while households without LUCs collect loans in the informal credit sector. --Credit,consistency,land reform,program evaluation,Vietnam

AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing

Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for "Good". This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. But what is the Common Good, and is it enough to want to be good? Via four lead questions, I will illustrate challenges and pitfalls when determining, from an AI point of view, what the Common Good is and how it can be enhanced by AI. The questions are: What is the problem / What is a problem?, Who defines the problem?, What is the role of knowledge?, and What are important side effects and dynamics? The illustration will use an example from the domain of "AI for Social Good", more specifically "Data Science for Social Good". Even if the importance of these questions may be known at an abstract level, they do not get asked sufficiently in practice, as shown by an exploratory study of 99 contributions to recent conferences in the field. Turning these challenges and pitfalls into a positive recommendation, as a conclusion I will draw on another characteristic of computer-science thinking and practice to make these impediments visible and attenuate them: "attacks" as a method for improving design. This results in the proposal of ethics pen-testing as a method for helping AI designs to better contribute to the Common Good.Comment: to appear in Paladyn. Journal of Behavioral Robotics; accepted on 27-10-201