Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be meaningful to the consumer. In contrast, this paper suggests a new approach to this problem. It introduces a simple, practical framework, called Teaching Explanations for Decisions (TED), that provides meaningful explanations that match the mental model of the consumer. We illustrate the generality and effectiveness of this approach with two different examples, resulting in highly accurate explanations with no loss of prediction accuracy for these two examples.
Creating a second, simpler-to-understand model, such as a small number of logical expressions, that mostly matches the decisions of the deployed model (e.g., @cite_39 @cite_8 ).
@cite_8 @cite_39
49730540 1523041988 2250701144 2073471108
Recently Ferraris, Lee and Lifschitz proposed a new definition of stable models that does not refer to grounding, which applies to the syntax of arbitrary first-order sentences. We show its relation to the idea of loop formulas with variables by Chen, Lin, Wang and Zhang, and generalize their loop formulas to disjunctive programs and to arbitrary first-order sentences. We also extend the syntax of logic programs to allow explicit quantifiers, and define its semantics as a subclass of the new language of stable models by Such programs inherit from the general language the ability to handle nonmonotonic reasoning under the stable model semantics even in the absence of the unique name and the domain closure assumptions, while yielding more succinct loop formulas than the general language due to the restricted syntax. We also show certain syntactic conditions under which query answering for an extended program can be reduced to entailment checking in first-order logic, providing a way to apply first-order theorem provers to reasoning about non-Herbrand stable models. A traditional counterexample to a linear-time safety property shows the values of all signals at all times prior to the error. However, some signals may not be critical to causing the failure. A succinct explanation may help human understanding as well as speed up algorithms that have to analyze many such traces. In Bounded Model Checking (BMC), a counterexample is constructed from a satisfying assignment to a Boolean formula, typically in CNF. Modern SAT solvers usually assign values to all variables when the input formula is satisfiable. Deriving minimal satisfying assignments from such complete assignments does not lead to concise explanations of counterexamples because of how CNF formulae are derived from the models. Hence, we formulate the extraction of a succinct counterexample as the problem of finding a minimal assignment that, together with the Boolean formula describing the model, implies an objective. We present a two-stage algorithm for this problem, such that the result of each stage contributes to identify the “interesting” events that cause the failure. We demonstrate the effectiveness of our approach with an example and with experimental results. In this paper, we define models for automatically translating a factoid question in natural language to an SQL query that retrieves the correct answer from a target relational database (DB). We exploit the DB structure to generate a set of candidate SQL queries, which we rerank with an SVM-ranker based on tree kernels. In particular, in the generation phase, we use (i) lexical dependencies in the question and (ii) the DB metadata, to build a set of plausible SELECT, WHERE and FROM clauses enriched with meaningful joins. We combine the clauses by means of rules and a heuristic weighting scheme, which allows for generating a ranked list of candidate SQL queries. This approach can be recursively applied to deal with complex questions, requiring nested SELECT instructions. Finally, we apply the reranker to reorder the list of question and SQL candidate pairs, whose members are represented as syntactic trees. The F1 of our model derived on standard benchmarks, 87 on the first question, is in line with the best models using external and expensive hand-crafted resources such as the question meaning interpretation. Moreover, our system shows a Recall of the correct answer of about 94 and 98 on the first 2 and 5 candidates, respectively. This is an interesting outcome considering that we only need pairs of questions and answers concerning a target DB (no SQL query is needed) to train our model. Abstract A mathematical model is developed to provide a theoretical framework for a computer-oriented solution to the problem of recognizing those records in two files which represent identical persons, objects or events (said to be matched). A comparison is to be made between the recorded characteristics and values in two records (one from each file) and a decision made as to whether or not the members of the comparison-pair represent the same person or event, or whether there is insufficient evidence to justify either of these decisions at stipulated levels of error. These three decisions are referred to as link (A 1), a non-link (A 3), and a possible link (A 2). The first two decisions are called positive dispositions. The two types of error are defined as the error of the decision A 1 when the members of the comparison pair are in fact unmatched, and the error of the decision A 3 when the members of the comparison pair are, in fact matched. The probabilities of these errors are defined as and respecti...