Collaborating Authors


Creating "Unbiased News" Using Data Science


I scrapped all their webpages categorized under "stories". AllSides is a brilliant initiative that takes a news event and collects articles written on it by a left leaning, right leaning and center leaning media outlet. They write a summary on this event and briefly mention what is being emphasized on by each of the three outlets. An example of this can be viewed here. They publish pre-established metrics for the contemporary political bias of all major media outlets.

China and U.S. to work on climate, Beijing says after rancorous meeting

The Japan Times

Beijing – China and the United States will set up a joint working group on climate change, China's official Xinhua News Agency said, in a potentially positive takeaway from what was an unusually rancorous high-level meeting. The top Chinese and U.S. diplomats, in their first meeting of Joe Biden's presidency on Thursday and Friday, publicly rebuked each other's policies at the start of what Washington called "tough and direct" talks in Alaska. But the Chinese delegation said after the meeting the two sides were "committed to enhancing communication and cooperation in the field of climate change," Xinhua said on Saturday. They would also hold talks to facilitate the activities of diplomats and consular missions, "as well as on issues related to media reporters in the spirit of reciprocity and mutual benefit," the report said. The U.S. Embassy in Beijing did not immediately respond to an email seeking comment on Sunday.

How AI enables reporters, photo-journalists and broadcasters to humanise content


AI has played a role in breaking some of the biggest international news stories of recent years. A key example is the'Panama Papers' exposé, where machine learning helped an international team of researchers to identify loan agreements in more than 13 million records that were leaked to the press. Journalists were able to'follow the money', exposing the practices of offshore tax havens and the businesses taking advantage of tax loopholes. Machine learning also played a valuable role in the Implant Files investigation. Here, it sifted through reports sent to the US Food and Drug Administration (FDA), and helped to uncover patient deaths potentially caused by faulty medical devices.

Is Clover Health Stock a Buy?


The company sells Medicare Advantage plans, focusing on customer experience and leveraging machine learning and artificial intelligence to …

3 Kansas police officers injured by modified shotgun inside vacant home: cops

FOX News

Fox News Flash top headlines are here. Check out what's clicking on Authorities in Wichita, Kan., said Sunday that they are investigating a shooting that injured three police officers this weekend and working to determine if the shotgun was rigged to the door. A "modified, loaded shotgun" discharged as the officers entered a home in the city on Saturday, according to a release by Wichita Police Department spokesman Officer Trevor Macy. "Apparently there were several modifications made to this one," Macy told The Wichita Eagle.

Fact-Finding Mission

Communications of the ACM

Seeking to call into question the mental acuity of his opponent, Donald Trump looked across the presidential debate stage at Joseph Biden and said, "So you said you went to Delaware State, but you forgot the name of your college. Biden chuckled, but viewers may have been left wondering: did the former vice president misstate where he went to school? Those who viewed the debate live on an app from the London-based company Logically were quickly served an answer: the president's assertion was false. A brief write-up posted on the company's website the next morning provided links to other fact-checks from National Public Radio and the Delaware News Journal on the same claim, which explain that Biden actually said his first Senate campaign received a boost from students at the school. Logically is one of a number of efforts, both commercial and academic, to apply techniques of artificial intelligence (AI), including machine learning and natural language processing (NLP), to identify false ...

FTC Chairwoman Slaughter Sets Sights on Big Data and Artificial Intelligence Oversight


Conclusion. In light of these priorities, it is critical that companies managing big data or developing and deploying artificial intelligence (AI) and machine …

Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks Artificial Intelligence

Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on \emph{random walks with erasure} is able to generate more ideologically diverse recommendations. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.

Who Should Stop Unethical A.I.?

The New Yorker

In computer science, the main outlets for peer-reviewed research are not journals but conferences, where accepted papers are presented in the form of talks or posters. In June, 2019, at a large artificial-intelligence conference in Long Beach, California, called Computer Vision and Pattern Recognition, I stopped to look at a poster for a project called Speech2Face. Using machine learning, researchers had developed an algorithm that generated images of faces from recordings of speech. A neat idea, I thought, but one with unimpressive results: at best, the faces matched the speakers' sex, age, and ethnicity--attributes that a casual listener might guess. That December, I saw a similar poster at another large A.I. conference, Neural Information Processing Systems (NeurIPS), in Vancouver, Canada.

Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions Artificial Intelligence

Computational modelling of political discourse tasks has become an increasingly important area of research in natural language processing. Populist rhetoric has risen across the political sphere in recent years; however, computational approaches to it have been scarce due to its complex nature. In this paper, we present the new $\textit{Us vs. Them}$ dataset, consisting of 6861 Reddit comments annotated for populist attitudes and the first large-scale computational models of this phenomenon. We investigate the relationship between populist mindsets and social groups, as well as a range of emotions typically associated with these. We set a baseline for two tasks related to populist attitudes and present a set of multi-task learning models that leverage and demonstrate the importance of emotion and group identification as auxiliary tasks.