Last week, I, alongside my colleague Assembly Environmental Conservation Committee Chair Steve Englebright, held the first-ever NYS Assembly roundtable on artificial intelligence and how we can utilize it to predict and combat climate change. We head into our last three intensive legislative weeks with June with a few thousand bills pending – though only a fraction will end up passing both bodies. Serious work continues and a whole host of important yet controversial bills including on housing and tenant protections, climate control and protection, surrogacy laws and driver licenses to name just a few! The Assembly and Senate this week passed a legislative package aimed at protecting people with disabilities and improving and expanding services available to them. As always, check out community events to see what's happening around the district!

Faculty members Ian Kerr and Teresa Scassa have been appointed to the Government of Canada's new Advisory Council on Artificial Intelligence, joining a prestigious group of leading Canadian researchers and business executives to provide advice on how Canada can become a global leader in artificial intelligence (AI) advancements while ensuring that AI policy and practice reflect Canadian values. As stated in the press release from the Ministry of Innovation, Science and Economic Development Canada, "Artificial intelligence (AI) is a set of complex and powerful technologies that will touch or transform every sector and industry in Canada. It has the power to help us address some of our most challenging problems, from improving Canadians' health to fighting climate change. It will also introduce new sources of job creation and sustainable economic growth." The advisory council will be tasked with ensuring that Canada is approaching the transformative power of AI in an intelligent human-centric way, with attention given to human rights, transparency and openness.

The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit decisioning, hiring, advertising, criminal justice, personalized medicine, and targeted policymaking, where in some cases legislative or regulatory frameworks for fairness exist and define specific protected classes. In this paper we study a fundamental challenge to assessing disparate impacts in practice: protected class membership is often not observed in the data. This is particularly a problem in lending and healthcare. We consider the use of an auxiliary dataset, such as the US census, that includes class labels but not decisions or outcomes. We show that a variety of common disparity measures are generally unidentifiable aside for some unrealistic cases, providing a new perspective on the documented biases of popular proxy-based methods. We provide exact characterizations of the sharpest-possible partial identification set of disparities either under no assumptions or when we incorporate mild smoothness constraints. We further provide optimization-based algorithms for computing and visualizing these sets, which enables reliable and robust assessments -- an important tool when disparity assessment can have far-reaching policy implications. We demonstrate this in two case studies with real data: mortgage lending and personalized medicine dosing.

Artificial intelligence (AI) has become so pervasive in our society today that it is difficult to imagine what we would not use it for. Certainly it would be useful in the legal industry in relation to e-discovery and while the law is not fully at that point yet, pundits indicate they could likely expect to see it being used within the next three years. E-discovery is a mobile and fast-moving target when powered by AI – defined as "technologies that can mimic and enhance human thought processes and capabilities," says senior counsel and co-chair of the E-Discovery & Information Management Group, John Davis with Crowell & Moring. The point of AI, of course, is to automate manual tasks, a bonus that would likely be most welcome when it comes to the discovery process. The massive amount of material that can be generated by discovery could do with a major tweak to expedite the process.

Futurus Group, an affiliate of Gobel Enterprises and a full-service consulting firm focused on artificial intelligence, announced that it is seeking a patent for its proprietary gratitude prediction machine learning model. G2G (Gratitude to Give), Futurus' flagship product, currently utilizes this patent-pending algorithm and is the first artificially intelligent product on the market focused on predicting gratitude specifically in a healthcare environment. Early results have revealed promising insights. Forty-seven percent of the high-gratitude patients became donors, surpassing the ten percent of high-wealth patients who did. "We conceptualized this idea a few years ago," said Chad Gobel, CEO and founder of Gobel Group.

BEIJING - Thirty years after the crackdown on Tiananmen protesters, the tanks that lined Beijing's central avenue have been replaced by countless surveillance cameras perched like hawks on lampposts to keep the population in check. The Chinese Communist Party has gone to great lengths to prevent another pro-democracy movement, clamping down on student activists, labor movements and lawyers with the help of high-tech surveillance. But the party has also pushed economic reforms that have made millions of people wealthier -- and less interested in rebelling like the students whose protest ended with hundreds killed on June 4, 1989. Over the past decade, small police booths have been set up block by block across the country to monitor neighborhood disputes, prevent crime, and keep tabs on anyone suspected of disturbing social order. Now China's obsession with artificial intelligence and facial recognition adds another layer of sophistication to this intricate surveillance web, allowing police to pound on the door of any perceived troublemaker, several activists have said.

On the other hand, AM has rapidlyfrom a given document (Lippi 2016). Recent years have seen the development evolved by exploiting state-of-the-art neural architectures of a large number of techniques in this area, on coming from deep learning. So far, the wake of the advancements produced by deep these two worlds have progressed largely independently learning on the whole research field of natural of each other. Only recently, a few works language processing (NLP). Yet, it is widely recognized have taken some steps towards the integration of that the existing AM systems still have such methods, by applying techniques combining a large margin of improvement, as good results sub-symbolic classifiers with knowledge expressed have been obtained with some genres where prior in the form of rules and constraints to AM. knowledge on the structure of the text eases some Niculae et al. (2017) adopted structuredFor instance, AM tasks, but other genres such as legal cases support vector machines and recurrent neural and social media documents still require more networks to collectively classify argument components work (Cabrio and Villata, 2018). Performing and and their relations in short documents, understanding argumentation requires advanced by hard-coding contextual dependencies and constraints reasoning capabilities that are natural skills for humans, of the argument model in a factor graph. but which are difficult to learn for a machine. A joint inference approach for argument component Understanding whether a given piece of classification and relation identification was evidence supports a given claim, or whether two Persing and Ng (2016), followinginstead proposed by claims attack each other, are complex problems a pipeline scheme where integer linear programming that humans are able to address thanks to their is used to enforce mathematical constraints ability to exploit commonsense knowledge, and to on the outcomes of a first-stage set of classifiers.

San Francisco supervisors approved a ban on police using facial recognition technology, making it the first city in the U.S. with such a restriction. Facial recognition has enrolled in school. On Monday, a New York school district became one of the first in the U.S. to roll out facial recognition technology on campus using its students' faces as an added layer of security. The system of cameras can also be used to identify guns or flagged persons, such as expelled students and sex offenders, according to the school district. The Lockport City School District will pilot its Aegis system over the summer and will expand the technology to each of its eight schools before classes resume in the fall.

World leaders in technology are uniting to establish a common set of guidelines on the use of artificial intelligence and reel in the potential for misuse. The Global AI Council, which was created as part of a summit by the World Economic Forum in San Francisco, will focus not just on establishing standards for how AI should and shouldn't be applied across fields, but in making those standards mesh among world powers, particularly the U.S. and China. The goal of connecting disparate governments is arguably best exemplified through the council's leaders -- Microsoft President Brad Smith and Chinese AI expert Kai-Fu Lee. According to a statement from the World Economic Forum, specifically, the council hopes to establish channels of communication between partners of the council on best practices and case studies as well as addressing what it calls'governance gaps' -- presumably areas where regulation has yet to keep up with potentially harmful technology. As noted by MIT Technology Review, one particular area that will likely be a flashpoint for regulatory and ethical guidelines surrounding AI is surveillance.

On Tuesday, news broke that Microsoft refused to sell its facial recognition software to law enforcement in California and an unnamed country. The move led to some praise for the company for being consistent with its policy to oppose questionable human rights applications, but a broader examination of Microsoft's actions in the past year indicates that the company has been saying one thing and doing another. Last week, the Financial Times reported that Microsoft Research Asia worked with a university associated with the Chinese military on facial recognition tech that is being used to monitor the nation's population of Uighur Muslims. Up to 500,000 members of the group, primarily in western China, were monitored over the course of a month, according to a New York Times report. Microsoft defended the work as helpful to advance the technology, but U.S. Senator Marco Rubio called the company complicit in human rights abuses.