Unbiased data collection is essential to guaranteeing fairness in artificial intelligence models. Implicit bias, a form of behavioral conditioning that leads us to attribute predetermined characteristics to members of certain groups and informs the data collection process. This paper quantifies implicit bias in viewer ratings of TEDTalks, a diverse social platform assessing social and professional performance, in order to present the correlations of different kinds of bias across sensitive attributes. Although the viewer ratings of these videos should purely reflect the speaker's competence and skill, our analysis of the ratings demonstrates the presence of overwhelming and predominant implicit bias with respect to race and gender. In our paper, we present strategies to detect and mitigate bias that are critical to removing unfairness in AI.

Rehabilitation assessment is critical to determine an adequate intervention for a patient. However, the current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. In this paper, we identified the needs of therapists to assess patient's functional abilities (e.g. alternative perspective on assessment with quantitative information on patient's exercise motions). As a result, we developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning to assess the quality of motion and summarize patient specific analysis. We evaluated this system with seven therapists using the dataset from 15 patient performing three exercises. The evaluation demonstrates that our system is preferred over a traditional system without analysis while presenting more useful information and significantly increasing the agreement over therapists' evaluation from 0.6600 to 0.7108 F1-scores ($p <0.05$). We discuss the importance of presenting contextually relevant and salient information and adaptation to develop a human and machine collaborative decision making system.

In this paper, we study correlation clustering under fairness constraints. Fair variants of $k$-median and $k$-center clustering have been studied recently, and approximation algorithms using a notion called fairlet decomposition have been proposed. We obtain approximation algorithms for fair correlation clustering under several important types of fairness constraints. Our results hinge on obtaining a fairlet decomposition for correlation clustering by introducing a novel combinatorial optimization problem. We define a fairlet decomposition with cost similar to the $k$-median cost and this allows us to obtain approximation algorithms for a wide range of fairness constraints. We complement our theoretical results with an in-depth analysis of our algorithms on real graphs where we show that fair solutions to correlation clustering can be obtained with limited increase in cost compared to the state-of-the-art (unfair) algorithms.

So I think that those three areas that separates us from far from the crowd. The first one is about a model of safety. We are we published a paper two years ago about how to formalize a common sense of human driving how to define what a dangerous situation is in the context of decision making on merging into traffic . And since then we have been working with regulatory bodies to standardize this model . We are the only company that is really transparent about its safety safety model.

The most progressive, the most cutting-edge, the most exciting… Data science and machine learning are those areas nowadays that are enormously appealing and hot, hot, super-hot topics. But to stay tuned with all the advances and movements in these fields, you need to put lots of effort -- researching, reading, checking all the information, news, guides, and other stuff. This task is far away from being an easy solution. Right now, you can stumble upon a bunch of places with vivid titles and promising headlines, but are they useful enough? Every day I see a crazy flow of information, and, unfortunately, there are lots of false or worthless stuff, and especially on data science and ML.

F.A.C.P. is a fulltime, practicing gastroenterologist and internal medicine physician. As an active holistic health practitioner in the field, Dr. Nandi is also the Chief Health Editor at WXYZ ABC Detroit. At the age of 16, he completed his high school education in Columbus, Ohio where he was awarded a full academic scholarship to The Ohio State University and University of Notre Dame. To remain closer to his family, he chose Ohio State. Partha graduated summa cum laude (Top 1% of the class), a member of Phi Beta Kappa honor society, with a Bachelors degree in chemistry and a minor in classical Greek civilization.

A year ago, Lyft submitted a report to the California Department of Motor Vehicles that summed up its 2018 autonomous vehicle testing activity in a single, short paragraph. "Lyft Inc. did not operate any vehicles in autonomous mode on California public roads during the reporting period," the letter read. "As such, Lyft Inc. has no autonomous mode disengagements to report." The 2019 data tells a different story. Lyft had 19 autonomous vehicles testing on public roads in California in 2019, according to data released earlier this week by the CA DMV.

In his latest ecosystem column, Antony Savvas charts a blockbuster first couple months of the year, involving both new companies and well-established ones. Internet of Things (IoT) network provider, Sigfox has launched the second edition of its Hacking House event in Paris. For six months, participants from seven different countries will bring IoT-based projects to life addressing issues as diverse as car theft prevention and bird protection. Microsoft and Amosense are the sponsors of the latest Hacking House, which will also be supported by technology partners such as LITE-ON, Wisebatt and STMicroelectronics. The participants are divided into four teams to develop their project at Sigfox in Paris from this month to early August 2020.

Would you be surprised to learn a 120-year-old company is transforming its business with artificial intelligence and technology? Akron, Ohio-based tire maker Goodyear might not be the first company you think of when discussing technological innovation, but they continue to announce intriguing developments and offer proof via new initiatives and products that they are altering operations to be competitive in the future. Regardless if it's an autonomous, electric, or a traditional vehicle, they all need a solid foundation of the right tire for the specific demands of the vehicle. Goodyear uses internet of things technology in its Eagle 360 Urban tire. The tire is 3D printed with super-elastic polymer and embedded with sensors.

AI Transcription has been all the rage recently. Several companies have been coming out with new products to meet this requirement. How do you evaluate these products? We take a look and try to think behind the hype. First we look at some of the incoming offerings.