We develop a Random Forest model to estimate the species distribution of Asian elephants in India and study the inter and intra-annual spatiotemporal variability of habitats suitable for them. Climatic, topographic variables and satellite-derived Land Use/Land Cover (LULC), Net Primary Productivity (NPP), Leaf Area Index (LAI), and Normalized Difference Vegetation Index (NDVI) are used as predictors, and the species sighting data of Asian elephants from Global Biodiversity Information Reserve is used to develop the Random Forest model. A careful hyper-parameter tuning and training-validation-testing cycle are completed to identify the significant predictors and develop a final model that gives precision and recall of 0.78 and 0.77. The model is applied to estimate the spatial and temporal variability of suitable habitats. We observe that seasonal reduction in the suitable habitat may explain the migration patterns of Asian elephants and the increasing human-elephant conflict. Further, the total available suitable habitat area is observed to have reduced, which exacerbates the problem. This machine learning model is intended to serve as an input to the Agent-Based Model that we are building as part of our Artificial Intelligence-driven decision support tool to reduce human-wildlife conflict.

The neural ordinary differential equation (neural ODE) model has attracted increasing attention in time series analysis for its capability to process irregular time steps, i.e., data are not observed over equally-spaced time intervals. In multi-dimensional time series analysis, a task is to conduct evolutionary subspace clustering, aiming at clustering temporal data according to their evolving low-dimensional subspace structures. Many existing methods can only process time series with regular time steps while time series are unevenly sampled in many situations such as missing data. In this paper, we propose a neural ODE model for evolutionary subspace clustering to overcome this limitation and a new objective function with subspace self-expressiveness constraint is introduced. We demonstrate that this method can not only interpolate data at any time step for the evolutionary subspace clustering task, but also achieve higher accuracy than other state-of-the-art evolutionary subspace clustering methods. Both synthetic and real-world data are used to illustrate the efficacy of our proposed method.

These are some of the outcomes that AI developers fear will come from their work, according to a new report issued today by the Deloitte AI Institute and the U.S. Chamber of Commerce. Titled "Investing in trustworthy AI," the 82-page report from Deloitte and the Chamber Technology Engagement Center sought to identify the concerns that technology experts have when it comes to the adoption of AI, as well as highlight the impact that government investment in AI can have on the emerging technology. Algorithmic bias and a lack of humans in decision loops are concerns for about two-thirds of the 250 people who participated in the survey. Another 60% identified "rogue or unanticipated behavior" of autonomous agents as a threat, while 56% said the lack of explainability of algorithms was a concern. "Perceived, and actual, discrimination by AI systems undermines the confidence individuals have in whether they are being given a fair opportunity when AI is involved," the report stated.

Your ability to land your next job could depend on how well you play one of the AI-powered games that companies like AstraZeneca and Postmates are increasingly using in the hiring process. Some companies that create these games, like Pymetrics and Arctic Shores, claim that they limit bias in hiring. But AI hiring games can be especially difficult to navigate for job seekers with disabilities. In the latest episode of MIT Technology Review's podcast "In Machines We Trust," we explore how AI-powered hiring games and other tools may exclude people with disabilities. And while many people in the US are looking to the federal commission responsible for employment discrimination to regulate these technologies, the agency has yet to act.

Increasingly, job seekers need to pass a series of tests in the form of artificial-intelligence games just to be seen by a hiring manager. In this third of a four-part miniseries on AI and hiring, we speak to someone who helped create these tests, and we ask who might get left behind in the process and why there isn't more policy in place. We also try out some of these tools ourselves. This miniseries on hiring was reported by Hilke Schellmann and produced by Jennifer Strong, Emma Cillekens, Anthony Green, and Karen Hao. Jennifer: Often in life … you have to "play the metaphorical game"… to get the win you might be chasing. It's just a complicated game.. Gh - ah.. Game.." Jennifer: But what if that game… was literal? And what if winning at it could mean the difference between landing a job you've been dreaming of… or not. Increasingly job seekers need to pass a series of "tests" in the form of artificial-intelligence games… just to be seen by a hiring manager. Anonymous job seeker: For me, being a military veteran being able to take tests and quizzes or being under pressure is nothing for me, but I don't know why the cognitive tests gave me anxiety, but I think it's because I knew that it had nothing to do with software engineering that's what really got me. She asked us to call her Sally because she's criticizing the hiring methods of potential employers and she's concerned about publishing her real name. She has a graduate degree in information from Rutgers University in New Jersey, with specialties in data science and interaction design. And Sally fails to see how solving a timed puzzle... or playing video games like Tetris... have any real bearing on her potential to succeed in her field. So companies want to do diversity and inclusion, but you're not doing diversity and inclusion when it comes to thinking, not everyone thinks the same. So how are you inputting that diversity and inclusion when you're only selecting the people that can figure out a puzzle within 60 seconds.

All the sessions from Transform 2021 are available on-demand now. In early June, border officials "quietly deployed" the mobile app CBP One at the U.S.-Mexico border to "streamline the processing" of asylum seekers. While the app will reduce manual data entry and speed up the process, it also relies on controversial facial recognition technologies and stores sensitive information on asylum seekers prior to their entry to the U.S. The issue here is not the use of artificial intelligence per se, but what it means in relation to the Biden administration's pre-election promise of civil rights in technology, including AI bias and data privacy. When the Democrats took control of both House and Senate in January, onlookers were optimistic that there was an appetite for a federal privacy bill and legislation to stem bias in algorithmic decision-making systems. This is long overdue, said Ben Winters, Equal Justice Works Fellow of the Electronic Privacy Information Center (EPIC), who works on matters related to AI and the criminal justice system.

AI ethics expert Joanna J Bryson spoke to Siliconrepublic.com about the challenges of regulating AI and why more work needs to be done. As AI becomes a bigger part of society, the ethics around the technology require more discussion, with everything from privacy and discrimination to human safety needing consideration. There have been several examples in recent years highlighting ethical problems with AI, including an MIT image library to train AI that contained racist and misogynistic terms and the controversial credit score system in China. In recent years, the EU has made conscious steps towards addressing some of these issues, laying the groundwork for proper regulation for the technology. Its most recent proposals revealed plans to classify different AI applications depending on their risks.

We describe how answer-set programs can be used to declaratively specify counterfactual interventions on entities under classification, and reason about them. In particular, they can be used to define and compute responsibility scores as attribution-based explanations for outcomes from classification models. The approach allows for the inclusion of domain knowledge and supports query answering. A detailed example with a naive-Bayes classifier is presented.

'Gutfeld!' panel debates whether CNN will change their coverage This is a rush transcript from "Gutfeld!," This copy may not be in its final form and may be updated. I want to protect free speech. No, we want people to be protected from disinformation, to be protected from dying in this country, to be protected from people like Donald Trump who spread this information for -- who love to make sure that the division and the death continues. That was a rough weekend, and not just for Kat. But at least she kept her clothes on unlike our other guests, Jimmy Failla. But it was a far worse weekend for CNN. First let's go to our roly-poly guacamole gossip goalie. See how bad it got unreliable fart noises. Here's Michael Wolff delivering that smack to the hack. You know, you become part of -- one of the parts of the problem of the media. You know, you come on here and you -- and you have a, you know, a monopoly on truth. You know, you know exactly how things are supposed to be done. You know, you are why one of the reasons people can't stand the media. You should see the rest of the world, buddy. Can I hear that chuckle again? But if that was a heavyweight fight, and it is because, you know, Stelter, it would have been stopped in the first 25 seconds. It got worse, meaning better, lots better. STELTER: It's -- how -- so what should I do differently, Michael? WOLFF: You know, don't talk so much. Listen more, you know, people have genuine problems with the media. The media doesn't get the story right.

In the realm of international cybersecurity, "dual use" technologies are capable of both affirming and eroding human rights. Facial recognition may identify a missing child, or make anonymity impossible. Hacking may save lives by revealing key intel on a terrorist attack, or empower dictators to identify and imprison political dissidents. The same is true for gadgets. Your smart speaker makes it easier to order pizza and listen to music, but also helps tech giants track you even more intimately and target you with more ads.