As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use decision rules. This paper focuses on decision sets, a type of model with unordered rules, which explains each prediction with a single rule. In order to be easy for humans to understand, these rules must be concise. Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large. Here we consider a better measure, namely the total size of the decision set in terms of literals. So we are not driven to a small set of rules which require a large number of literals. We provide the first approach to determine minimum-size decision sets that achieve minimum empirical risk and then investigate sparse alternatives where we trade accuracy for size. By finding optimal solutions we show we can build decision set classifiers that are almost as accurate as the best heuristic methods, but far more concise, and hence more explainable.

Simply put, for the last 200 years, advisers have worked on the principle of information asymmetry, where they have better information than their clients. Today, we are at the point where machine intelligence is gaining information asymmetry over advisers, and that's only going to get more acute and asymmetrical as time goes on. The only possible hope for human advisers is that they co-opt machine intelligence into their process.

As a data scientist, have you ever been frustrated that your stakeholders don't see the value that you bring to the table? You may ask yourself, "How far should I go in explaining the work I do or what my models are doing?" If that sounds like you, then pay close attention to this post and the next, as they are all about improving collaboration between data scientists and other stakeholders. This is a two-part post: This article covers underlying assumptions and gaps in understanding that cause friction between data scientists and stakeholders; my other article, offers concrete steps for better collaboration. Machine Learning (ML) models are inherently complex and hard to explain.

MACHINE learning is a popular buzzword today, and has been heralded as one of the greatest innovations conceived by man. A branch of artificial intelligence (AI), machine learning is increasingly embedded in daily life, such as automatic email reply predictions, virtual assistants, and chatbots. The technology is also expected to revolutionize the world of finance. While it is slower than other industries in embracing the technology, the impact of ML is already visibly significant. Most recently, HSBC said that the bank was using the technology to combat financial crime.

Debating rules-based systems over machine learning comes down to the complexity of the task at hand. Machine learning dominates complex tasks, but requires more long-term expertise. For organizations creating algorithms and implementing systems, choosing between rules-based vs. machine learning-based systems is critical to the usability, compatibility and lifecycle of the application. Getting outputs from a rules-based system can be a simple and nearly immediate application of AI, but an investment in machine learning can handle complex tasks with great speed. Enterprises must understand the core differences between the two, their individual benefits and the limitations of both before taking advantage of either.

An increasing number of capital markets firms are adopting machine learning and other artificial intelligence techniques to build algorithmic trading systems that learn from data without relying on rules-based systems. With the hiring of data scientists, advances in cloud computing, and access to open source frameworks for training machine learning models, AI is transforming the trading desk. Already the largest banks have rolled out self-learning algorithms for equities trading. "Machine learning is a natural next step of algorithmic trading because machine learning identifies patterns and behaviors in historical data and learns from it," said Robert Hegarty, managing partner, Hegarty Group, a consultancy focusing on financial services, technology, data, and AI/machine learning. While traditional algorithms are created by programmers and quant strategists, these algorithms based on if/then rules do not learn on their own; they need to be updated.

We tackle the challenge of in-session attribution for on-site search engines in eCommerce. We phrase the problem as a causal counterfactual inference, and contrast the approach with rule-based systems from industry settings and prediction models from the multi-touch attribution literature. We approach counterfactuals in analogy with treatments in formal semantics, explicitly modeling possible outcomes through alternative shopper timelines; in particular, we propose to learn a generative browsing model over a target shop, leveraging the latent space induced by prod2vec embeddings; we show how natural language queries can be effectively represented in the same space and how "search intervention" can be performed to assess causal contribution. Finally, we validate the methodology on a synthetic dataset, mimicking important patterns emerged in customer interviews and qualitative analysis, and we present preliminary findings on an industry dataset from a partnering shop.

Chatbots are one of the most famous technologies. Bots are brilliant, and they can answer logically. Chatbots are very fast, so users want to use it in more sectors to avoid human interactions. It was invented to interact with customers and solve their problems without direct communication. These two have their unique way of work.

New York State Troopers PBA President Thomas Mungeer says Mayor de Blasio's new laws are putting officers at risk. The president of a union representing New York State troopers said Friday that New York City's restrictions on police officers are setting the men and women on the force up for failure. "By raising the bar and almost making it impossible for my members to safely arrest, we've had enough. I want them out," New York State Troopers PBA President Thomas Mungeer told "Fox & Friends." "What has me alarmed is that troopers that are trained in certain tactics to arrest violent people can now be arrested for using those tactics within the five counties of New York City. Those tactics are still legal in the other 57 counties that make up New York state," Mungeer said.

In this manuscript, we propose a federated F-score based ensemble tree model for automatic rule extraction, namely Fed-FEARE. Under the premise of data privacy protection, Fed-FEARE enables multiple agencies to jointly extract set of rules both vertically and horizontally. Compared with that without federated learning, measures in evaluating model performance are highly improved. At present, Fed-FEARE has already been applied to multiple business, including anti-fraud and precision marketing, in a China nation-wide financial holdings group.