Dublin, Feb. 16, 2022 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Healthcare: Intellectual Property Landscape" report has been added to ResearchAndMarkets.com's offering. This report features an extensive study of the historical and current collection of granted patents, patent applications and affiliated documents associated with the upcoming suite of intuitive software and automation enabling solutions, which are designed for use within the healthcare industry. The information in this report has been presented across two deliverables, namely an Excel sheet, featuring an interactive dashboard, and a PowerPoint presentation, summarizing the ongoing activity in this domain, and key insights drawn from the available data. The global healthcare sector has been overtly reluctant to embrace technology. This may partially be due to the failure of early digitization efforts, which were fraught with challenges and turned out to be more of a liability rather than a path forward.

In 2018, while the Argentine Congress was hotly debating whether to decriminalize abortion, the Ministry of Early Childhood in the northern province of Salta and the American tech giant Microsoft presented an algorithmic system to predict teenage pregnancy. They called it the Technology Platform for Social Intervention. Diego Jemio is a journalist, educator, and podcaster. He currently writes for the Clarín newspaper (Buenos Aires), Vértice (Mexico), and other media. He is the creator of the podcast Epistolar.

A supervised machine learning algorithm determines a model from a learning sample that will be used to predict new observations. To this end, it aggregates individual characteristics of the observations of the learning sample. But this information aggregation does not consider any potential selection on unobservables and any status-quo biases which may be contained in the training sample. The latter bias has raised concerns around the so-called \textit{fairness} of machine learning algorithms, especially towards disadvantaged groups. In this chapter, we review the issue of fairness in machine learning through the lenses of structural econometrics models in which the unknown index is the solution of a functional equation and issues of endogeneity are explicitly accounted for. We model fairness as a linear operator whose null space contains the set of strictly {\it fair} indexes. A {\it fair} solution is obtained by projecting the unconstrained index into the null space of this operator or by directly finding the closest solution of the functional equation into this null space. We also acknowledge that policymakers may incur a cost when moving away from the status quo. Achieving \textit{approximate fairness} is obtained by introducing a fairness penalty in the learning procedure and balancing more or less heavily the influence between the status quo and a full fair solution.

FILE photo - Texas sued Meta on Monday over misuse of biometric data, the latest round of litigation between governments and the company over privacy. FILE photo - Texas sued Meta on Monday over misuse of biometric data, the latest round of litigation between governments and the company over privacy. Texas sued Facebook parent company Meta for exploiting the biometric data of millions of people in the state - including those who used the platform and those who did not. The company, according to a suit filed by state Attorney General Ken Paxton, violated state privacy laws and should be responsible for billions of dollars in damages. The suit involves Facebook's "tag suggestions" feature, which the company ended last year, that used facial recognition to encourage users to link the photo to a friend's profile.

Case law retrieval is the retrieval of judicial decisions relevant to a legal question. Case law retrieval comprises a significant amount of a lawyer's time, and is important to ensure accurate advice and reduce workload. We survey methods for case law retrieval from the past 20 years and outline the problems and challenges facing evaluation of case law retrieval systems going forward. Limited published work has focused on improving ranking in ad-hoc case law retrieval. But there has been significant work in other areas of case law retrieval, and legal information retrieval generally. This is likely due to legal search providers being unwilling to give up the secrets of their success to competitors. Most evaluations of case law retrieval have been undertaken on small collections and focus on related tasks such as question-answer systems or recommender systems. Work has not focused on Cranfield style evaluations and baselines of methods for case law retrieval on publicly available test collections are not present. This presents a major challenge going forward. But there are reasons to question the extent of this problem, at least in a commercial setting. Without test collections to baseline approaches it cannot be known whether methods are promising. Works by commercial legal search providers show the effectiveness of natural language systems as well as query expansion for case law retrieval. Machine learning is being applied to more and more legal search tasks, and undoubtedly this represents the future of case law retrieval.

The Texas attorney general is suing Facebook parent Meta, saying the United States company has unlawfully collected biometric data on Texans for commercial purposes, without their informed consent. Attorney General Ken Paxton filed the lawsuit Monday in state district court, claiming Meta has been "storing millions of biometric identifiers" -- identified as retina or iris scans, voiceprints, or a record of hand and face geometry -- contained in photos and videos people upload to its services, including Facebook and Instagram. "Facebook will no longer take advantage of people and their children with the intent to turn a profit at the expense of one's safety and well-being," Paxton said in a statement. "This is yet another example of Big Tech's deceitful business practices and it must stop. I will continue to fight for Texans' privacy and security."

The Texas Attorney General is suing Facebook parent Meta, saying the company has unlawfully collected biometric data on Texans for commercial purposes, without their informed consent. Attorney General Ken Paxton filed the lawsuit Monday a state district court claiming Meta has been "storing millions of biometric identifiers" -- identified as retina or iris scans, voice prints, or a record of hand and face geometry -- contained in photos and videos people upload to its services, including Facebook and Instagram. "Facebook will no longer take advantage of people and their children with the intent to turn a profit at the expense of one's safety and well-being," Paxton said in a statement. "This is yet another example of Big Tech's deceitful business practices and it must stop. I will continue to fight for Texans' privacy and security."

Meta's past use of facial recognition technology has once again landed the company in potential legal trouble. On Monday, Texas Attorney General Ken Paxton filed a lawsuit against the company, alleging it had collected the biometric data of millions of Texans without obtaining their informed consent to do so. At the center of the case is Facebook's now discontinued use of facial recognition technology. The platform previously employed the technology as part of its "tag suggestions" feature, which used image recognition to scan photos and automatically tag users in them. Last November, Meta shut down that system, citing, among other reasons, " uncertainty" about how the technology would be regulated in the future.

The Texas attorney general filed a suit against Facebook parent Meta Platforms Inc. on Monday, charging that the social-media giant's longstanding and now discontinued use of facial-recognition technology violated that state's privacy protections for personal biometric data. The lawsuit, filed in state district court in Marshall by Texas Attorney General Ken Paxton, seeks civil penalties in the hundreds of billions of dollars, according to a person familiar with the matter.

Carnegie Mellon University researchers are challenging a long-held assumption that there is a trade-off between accuracy and fairness when using machine learning to make public policy decisions. As the use of machine learning has increased in areas such as criminal justice, hiring, health care delivery and social service interventions, concerns have grown over whether such applications introduce new or amplify existing inequities, especially among racial minorities and people with economic disadvantages. To guard against this bias, adjustments are made to the data, labels, model training, scoring systems and other aspects of the machine learning system. The underlying theoretical assumption is that these adjustments make the system less accurate. A CMU team aims to dispel that assumption in a new study, Rayid Ghani, a professor in the School of Computer Science's Machine Learning Department and the Heinz College of Information Systems and Public Policy; Kit Rodolfa, a research scientist in ML; and Hemank Lamba, a post-doctoral researcher in SCS, tested that assumption in real-world applications and found the trade-off was negligible in practice across a range of policy domains.