Goto

Collaborating Authors

Results


The Future of Artificial Intelligence

#artificialintelligence

"[AI] is going to change the world more than anything in the history of mankind. AI oracle and venture capitalist Dr. Kai-Fu Lee, 2018 In a nondescript building close to downtown Chicago, Marc Gyongyosi and the small but growing crew of IFM/Onetrack.AI have one rule that rules them all: think simple. The words are written in simple font on a simple sheet of paper that's stuck to a rear upstairs wall of their industrial two-story workspace. Sitting at his cluttered desk, located near an oft-used ping-pong table and prototypes of drones from his college days suspended overhead, Gyongyosi punches some keys on a laptop to pull up grainy video footage of a forklift driver operating his vehicle in a warehouse. It was captured from overhead courtesy of a Onetrack.AI "forklift vision system." Employing machine learning and computer vision for detection and classification of various "safety events," the shoebox-sized device doesn't see all, but it sees plenty. Like which way the driver is looking as he operates the vehicle, how fast he's driving, where he's driving, locations of the people around him and how other forklift operators are maneuvering their vehicles. IFM's software automatically detects safety violations (for example, cell phone use) and notifies warehouse managers so they can take immediate action. The main goals are to prevent accidents and increase efficiency. The mere knowledge that one of IFM's devices is watching, Gyongyosi claims, has had "a huge effect." "If you think about a camera, it really is the richest sensor available to us today at a very interesting price point," he says. "Because of smartphones, camera and image sensors have become incredibly inexpensive, yet we capture a lot of information.


Iterative Effect-Size Bias in Ridehailing: Measuring Social Bias in Dynamic Pricing of 100 Million Rides

arXiv.org Artificial Intelligence

Algorithmic bias is the systematic preferential or discriminatory treatment of a group of people by an artificial intelligence system. In this work we develop a random-effects based metric for the analysis of social bias in supervised machine learning prediction models where model outputs depend on U.S. locations. We define a methodology for using U.S. Census data to measure social bias on user attributes legally protected against discrimination, such as ethnicity, sex, and religion, also known as protected attributes. We evaluate our method on the Strategic Subject List (SSL) gun-violence prediction dataset, where we have access to both U.S. Census data as well as ground truth protected attributes for 224,235 individuals in Chicago being assessed for participation in future gun-violence incidents. Our results indicate that quantifying social bias using U.S. Census data provides a valid approach to auditing a supervised algorithmic decision-making system. Using our methodology, we then quantify the potential social biases of 100 million ridehailing samples in the city of Chicago. This work is the first large-scale fairness analysis of the dynamic pricing algorithms used by ridehailing applications. An analysis of Chicago ridehailing samples in conjunction with American Community Survey data indicates possible disparate impact due to social bias based on age, house pricing, education, and ethnicity in the dynamic fare pricing models used by ridehailing applications, with effect-sizes of 0.74, 0.70, 0.34, and -0.31 (using Cohen's d) for each demographic respectively. Further, our methodology provides a principled approach to quantifying algorithmic bias on datasets where protected attributes are unavailable, given that U.S. geolocations and algorithmic decisions are provided.


Chicago Police Drop Clearview Facial Recognition Technology

U.S. News

A plaintiff in the lawsuit is the Chicago Alliance Against Sexual Exploitation, a nonprofit that advocates for the rights of survivors of sexual violence and exploitation. The group's legal director, Mallory Littlejohn, said Clearview's technology makes survivors fear being tracked by abusers.


DFIN Partners with Galvanize to Extend Audit and Compliance Offerings to Global Clients

#artificialintelligence

CHICAGO--(BUSINESS WIRE)--Donnelley Financial Solutions (NYSE: DFIN), a leading risk and compliance company, today announced a strategic partnership with Galvanize, an award-winning, cloud-based security, risk management, compliance and audit software company. Galvanize was recently named a leader in The Forrester Wave: Governance, Risk, And Compliance Platforms for the first-quarter 2020. "The partnership with Galvanize accelerates DFIN's position as a leading risk and compliance company with a comprehensive portfolio of technology-enabled solutions that meet the needs of clients globally," said Craig Clay, president of Global Capital Markets at DFIN. "In these unprecedented times, our partnership with Galvanize gives us the ability to help our clients identify areas of revenue leakage, using Galvanize's analytics engine powered by ACL Robotics. The combination of cloud-based tools and automation with ACL Robotics has revolutionized internal control and audit management. Our clients will benefit from the HighBond software platform, a complete GRC solution that includes industry-leading internal controls / SOX compliance, operational audit and enterprise risk management software solutions."


Built In Chicago's 50 Startups to Watch in 2020

#artificialintelligence

Technology is Chicago's fastest-growing industry sector, having grown more 270 percent over the last decade, according to World Business Chicago. And 2019 was a model year that not only encapsulated the growth of technology in the city but also positioned Chicago for further success in 2020 and beyond. Influential leaders in tech launched Chicago's Plan for 2033, or P33, to enhance the city's viability as a global tech hub with a strong and diverse workforce through the next decade. Mayor Lori E. Lightfoot said on Chicago Tech Day that 15 local tech companies have added or will be adding 2,000 jobs through 2020. Uber announced it would be bringing that same number of jobs to Chicago over the next three years and spending more than $200 million annually on the city. But it isn't just major initiatives and companies with household names that will be bringing continued success to Chicago tech. Smaller startups entering the city's tech scene are shaping everything from mental health care to cryptocurrency trading to vehicle leasing. We found 50 such companies -- all less than three years old -- that are poised for growth in the coming year. Brett Quillen contributed in writing this report. Interested in Chicago tech?See all open roles on Built In CHI Arturo wants to take property risk management to the skies by using drones and satellite, aerial and ground imagery to assess residential and commercial property characteristics. The data it collects is powered by predictive analytics to give clients that lend, insure or invest in properties the ability to minimize risk and determine market patterns.


How accurate is AI in legal research?

#artificialintelligence

Humans can make mistakes, but so can machines. If we use artificial intelligence for legal work, what sort of quality is needed? A Friday ABA Techshow panel at the Hyatt Regency Chicago titled "Open the Pod Bay Doors: Problems with AI" discussed this question in detail. Is the goal to do something quickly? If the goal is to do something better than a human, which human?


AI for Policy Implementation

#artificialintelligence

Crystal Cody is the Public Safety Technology Director for the City of Charlotte responsible for all technology related to Police, Fire, and the regional Radio Network. She has been in this role for six months and previously held the role of Computer Technology Solutions Manager for the Charlotte-Mecklenburg Police Department. In her twenty-year career with the City of Charlotte, she has been responsible for the selection, design, implementation, and management of all software applications used by the Police Department, and more recently, Public Safety. Her most notable accomplishments during this time are the implementation of CMPD's custom Records Management System, Computer Aided Dispatch system, the CRISS NC-LInX regional information sharing system, Predictive Analytics Business Intelligence Dashboards, and the Earl Intervention System along with a myriad of technology projects supporting the daily operations for Public Safety in Charlotte. Crystal worked closely with the staff at University of Chicago's Institute for Data Science and Public Policy in the development of the machine learning model used to identify officers at risk of adverse interactions with citizens and the CMPD has implemented use of the model through the development of an automated workflow for alerts and assessment dashboard.


"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans

arXiv.org Artificial Intelligence

To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans. While existing approaches focus on explaining machine predictions with real-time assistance, we explore model-driven tutorials to help humans understand these patterns in a training phase. We consider both tutorials with guidelines from scientific papers, analogous to current practices of science communication, and automatically selected examples from training data with explanations. We use deceptive review detection as a testbed and conduct large-scale, randomized human-subject experiments to examine the effectiveness of such tutorials. We find that tutorials indeed improve human performance, with and without real-time assistance. In particular, although deep learning provides superior predictive performance than simple models, tutorials and explanations from simple models are more useful to humans. Our work suggests future directions for human-centered tutorials and explanations towards a synergy between humans and AI.


Rise of #MeTooBots: scientists develop AI to detect harassment in emails

The Guardian

Artificial intelligence programmers are developing bots that can identify digital bullying and sexual harassment. Known as "#MeTooBots" after the high-profile movement that arose after allegations against the Hollywood producer Harvey Weinstein, the bots can monitor and flag communications between colleagues and are being introduced by companies around the world. Bot-makers say it is not easy to teach computers what harassment looks like, with its linguistic subtleties and grey lines. Jay Leib, the chief executive of the Chicago-based AI firm NexLP, said: "I wasn't aware of all the forms of harassment. I thought it was just talking dirty. It comes in so many different ways. It might be 15 messages … it could be racy photos."


Accenture will acquire Clarity Insights to boost AI capabilities - AI ML Community India's Fastest Growing Data Science, AI and ML Community

#artificialintelligence

Accenture said the acquisition will further equip its clients with capabilities to meet the growing demand for enterprise-scale AI, analytics and automation solutions. On Friday (13 December), professional services company Accenture announced that it has entered into an agreement to acquire Clarity Insights. Clarity Insights, a Chicago-based data consultancy with deep data science, AI and machine learning expertise, will bring its 350 employees to Accenture's Applied Intelligence business in North America. Founded in 2008, Clarity Insights focuses on serving clients' needs from end to end, aiming to transform business processes to embed and scale AI with deeper insights from data. Accenture did not disclose the terms of the deal.