We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing coreference benchmark datasets. Our dataset and code are available at http://winobias.org.

We present QARMA, an efficient novel parallel algorithm for mining all Quantitative Association Rules in large multidimensional datasets where items are required to have at least a single common attribute to be specified in the rules single consequent item. Given a minimum support level and a set of threshold criteria of interestingness measures such as confidence, conviction etc. our algorithm guarantees the generation of all non-dominated Quantitative Association Rules that meet the minimum support and interestingness requirements. Such rules can be of great importance to marketing departments seeking to optimize targeted campaigns, or general market segmentation. They can also be of value in medical applications, financial as well as predictive maintenance domains. We provide computational results showing the scalability of our algorithm, and its capability to produce all rules to be found in large scale synthetic and real world datasets such as Movie Lens, within a few seconds or minutes of computational time on commodity hardware.

The US and China have imposed tariffs on each other's goods. But will a skirmish between the world's two biggest economies turn into a full-on trade war? Perhaps it has already started. Both sides have struck initial blows. The US has imposed tariffs on imports of steel and aluminium.

This paper investigates to what extent do cognitive biases affect human understanding of interpretable machine learning models, in particular of rules discovered from data. Twenty cognitive biases (illusions, effects) are covered, as are possibly effective debiasing techniques that can be adopted by designers of machine learning algorithms and software. While there seems no universal approach for eliminating all the identified cognitive biases, it follows from our analysis that the effect of most biases can be ameliorated by making rule-based models more concise. Due to lack of previous research, our review transfers general results obtained in cognitive psychology to the domain of machine learning. It needs to be succeeded by empirical studies specifically aimed at the machine learning domain.

Many machine learning and artificial intelligence (AI) systems lack the ability to explain how they work and make decisions--and this is a major trust inhibitor. They can find patterns in data that elude us, patterns that might reveal important relationships that improve the accuracy of the algorithm. They can recover patterns and relationships that we as human beings want to ignore. But they can just as easily fail to discover important relationships and produce bad recommendations, even dangerous ones. A well-known example of the latter involved research to see whether machine learning could guide the treatment of pneumonia patients.

In this paper, we present a hybrid model that combines a neural conversational model and a rule-based graph dialogue system that assists users in scheduling reminders through a chat conversation. The graph based system has high precision and provides a grammatically accurate response but has a low recall. The neural conversation model can cater to a variety of requests, as it generates the responses word by word as opposed to using canned responses. The hybrid system shows significant improvements over the existing baseline system of rule based approach and caters to complex queries with a domain-restricted neural model. Restricting the conversation topic and combination of graph based retrieval system with a neural generative model makes the final system robust enough for a real world application.

In this work we study a rational extension $SROEL^R T$ of the low complexity description logic SROEL, which underlies the OWL EL ontology language. The extension involves a typicality operator T, whose semantics is based on Lehmann and Magidor's ranked models and allows for the definition of defeasible inclusions. We consider both rational entailment and minimal entailment. We show that deciding instance checking under minimal entailment is in general $\Pi^P_2$-hard, while, under rational entailment, instance checking can be computed in polynomial time. We develop a Datalog calculus for instance checking under rational entailment and exploit it, with stratified negation, for computing the rational closure of simple KBs in polynomial time.

Note: This is an edited version of an article I wrote for RT Insights. I'm convinced we are entering the Golden Age of artificial intelligence (AI); with so much promise and potential in front of us, I am feeling a little like Neo in The Matrix as he swallows the red pill. However, rather than science fiction, my recent work at FICO to make AI better has drawn upon my background in theoretical physics to create what we call defensive AI. This is needed, because AI-based attacks are not science fiction -- they are happening today. Businesses have relied on AI to fight fraud and financial crime for more than 25 years.

The current era of computer science has seen a significant increase in the application of machine learning (ML) and knowledge representation (KR). The problem with the current situation regarding ethics and AI is the weaknesses of ML and KR when used separately. ML will “learn” ethical behaviour as it is observed and may therefore disagree with human morals. On the other hand, KR is too rigid and can only process scenarios that have been predefined. This paper proposes a solution to the question posed by Rossi (2016) “How to combine bottom-up learning approaches with top-down rule-based approaches in defining ethical principles for AI systems?” This system focuses on potential unethical behaviors that are caused by human nature instead of ethical dilemmas caused by technology insufficiency in the wartime scenarios. Our solution is an architecture that combines a classifier to identify targets in wartime scenarios and a rules-based system in the form of ontologies to guide an AI agent’s behaviour in the given circumstance.

"We will always do what is necessary to defend ourselves, our allies and our values against an attack of this sort, which is an attack not only on the United Kingdom, but upon the international rules-based system on which all countries, including Russia, depend for their safety and security," Bristow told reporters.