In recent years, academics and investigative journalists have criticized certain commercial risk assessments for their black-box nature and failure to satisfy competing notions of fairness. Since then, the field of interpretable machine learning has created simple yet effective algorithms, while the field of fair machine learning has proposed various mathematical definitions of fairness. However, studies from these fields are largely independent, despite the fact that many applications of machine learning to social issues require both fairness and interpretability. We explore the intersection by revisiting the recidivism prediction problem using state-of-the-art tools from interpretable machine learning, and assessing the models for performance, interpretability, and fairness. Unlike previous works, we compare against two existing risk assessments (COMPAS and the Arnold Public Safety Assessment) and train models that output probabilities rather than binary predictions. We present multiple models that beat these risk assessments in performance, and provide a fairness analysis of these models. Our results imply that machine learning models should be trained separately for separate locations, and updated over time.

Forgetting is removing variables from a logical formula while preserving the constraints on the other variables. In spite of being a form of reduction, it does not always decrease the size of the formula and may sometimes increase it. This article discusses the implications of such an increase and analyzes the computational properties of the phenomenon. Given a propositional Horn formula, a set of variables and a maximum allowed size, deciding whether forgetting the variables from the formula can be expressed in that size is $D^p$-hard in $\Sigma^p_2$. The same problem for unrestricted propositional formulae is $D^p_2$-hard in $\Sigma^p_3$. The hardness results employ superredundancy: a superirredundant clause is in all formulae of minimal size equivalent to a given one. This concept may be useful outside forgetting.

New York-based tech consultancy ABI Research has stated in a press release that, whilst biometric investment struggles, AI has been gaining traction. The reason, the firm explains, is a logical one: government regulations seeking to limit contact and cross-contamination have made biometric equipment unappealing to the tech market, whilst the need AI-powered automation to maintain business continuity is growing. Dimitrios Pavlakis, Digital Security Analyst at ABI, considers that the biometric sector's loss could reach approximately US$2bn, which could have significant implications for the public sector. "Fingerprint biometrics vendors are struggling to uphold the new stringent hygiene and infection control protocols. These regulations have been correctly introduced for the safety of users and personnel, but they have also affected sales in certain verticals," he said.

We provide end-to-end image analysis and vision AI expertise on different business verticals. Your simple data can be turned into a working AI. We will clean and analyze the data you'll provide. We will provide and maintain an AI-powered interface you can use. From trademark searches based on your logo (rather than the traditional text-based searches others offer), AI-powered image search solution for your enterprise, to our generative image-building solution that takes the images you input and develops new iterations.

Artificial intelligence is still in its infancy. But it may well prove to be the most powerful technology ever invented. It has the potential to improve health, supercharge intellects, multiply productivity, save the environment and enhance both freedom and democracy. But as that intelligence continues to climb, the danger from using AI in an irresponsible way also brings the potential for AI to become a social and cultural H-bomb. It's a technology that can deprive us of our liberty, power autocracies and genocides, program our behavior, turn us into human machines and, ultimately, turn us into slaves.

Legal matter intake in a law firm refers to the process of initial client interaction, ensuring strong client relationships as well as help to make well-informed business decisions. It evaluates the merits of new business (matter) as well as new clients to propose further plans. The legal intake process establishes the first impression of lawyers on clients aiming to convert a lead into a paying client. Successful legal firms, as well as sole practitioners, are already following the legal intake process to organize their marketing efforts and provide enhanced service to the clients reaching them. New client matter intake is considered the most cumbersome activity for attorneys, which shifts their focus from other challenging legal issues.

In this paper, we study the problem of fair classification in the presence of prior probability shifts, where the training set distribution differs from the test set. This phenomenon can be observed in the yearly records of several real-world datasets, such as recidivism records and medical expenditure surveys. If unaccounted for, such shifts can cause the predictions of a classifier to become unfair towards specific population subgroups. While the fairness notion called Proportional Equality (PE) accounts for such shifts, a procedure to ensure PE-fairness was unknown. In this work, we propose a method, called CAPE, which provides a comprehensive solution to the aforementioned problem. CAPE makes novel use of prevalence estimation techniques, sampling and an ensemble of classifiers to ensure fair predictions under prior probability shifts. We introduce a metric, called prevalence difference (PD), which CAPE attempts to minimize in order to ensure PE-fairness. We theoretically establish that this metric exhibits several desirable properties. We evaluate the efficacy of CAPE via a thorough empirical evaluation on synthetic datasets. We also compare the performance of CAPE with several popular fair classifiers on real-world datasets like COMPAS (criminal risk assessment) and MEPS (medical expenditure panel survey). The results indicate that CAPE ensures PE-fair predictions, while performing well on other performance metrics.

To build Sounding Board, we develop a system architecture that is capable of accommodating dialog strategies that we designed for socialbot conversations. The architecture consists of a multi-dimensional language understanding module for analyzing user utterances, a hierarchical dialog management framework for dialog context tracking and complex dialog control, and a language generation process that realizes the response plan and makes adjustments for speech synthesis. Additionally, we construct a new knowledge base to power the socialbot by collecting social chat content from a variety of sources. An important contribution of the system is the synergy between the knowledge base and the dialog management, i.e., the use of a graph structure to organize the knowledge base that makes dialog control very efficient in bringing related content to the discussion. Using the data collected from Sounding Board during the competition, we carry out in-depth analyses of socialbot conversations and user ratings which provide valuable insights in evaluation methods for socialbots. We additionally investigate a new approach for system evaluation and diagnosis that allows scoring individual dialog segments in the conversation. Finally, observing that socialbots suffer from the issue of shallow conversations about topics associated with unstructured data, we study the problem of enabling extended socialbot conversations grounded on a document. To bring together machine reading and dialog control techniques, a graph-based document representation is proposed, together with methods for automatically constructing the graph. Using the graph-based representation, dialog control can be carried out by retrieving nodes or moving along edges in the graph. To illustrate the usage, a mixed-initiative dialog strategy is designed for socialbot conversations on news articles.

Income inequality is one of the overarching problems of economics. One of the most effective tools policymakers have to address it is taxation: governments collect money from people according to what they earn and redistribute it either directly, via welfare schemes, or indirectly, by using it to pay for public projects. But though more taxation can lead to greater equality, taxing people too much can discourage them from working or motivate them to find ways to avoid paying--which reduces the overall pot. Getting the balance right is not easy. Economists typically rely on assumptions that are hard to validate.

Section 101 states "[w]hoever invents or discovers…may obtain a patent therefore…" According to 35 U.S.C. § 100, an inventor is defined as an individual or individuals. As technology has advanced and the possibility that AI would invent something became a probability, the question has arisen whether AI can be an inventor under United States law.