During the pandemic, technology companies have been pitching their emotion-recognition software for monitoring workers and even children remotely. Take, for example, a system named 4 Little Trees. Developed in Hong Kong, the program claims to assess children's emotions while they do classwork. It maps facial features to assign each pupil's emotional state into a category such as happiness, sadness, anger, disgust, surprise and fear. It also gauges'motivation' and forecasts grades.
Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity. However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and high-quality annotated dataset of tweets written by US politicians. Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly. We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
Media bias can lead to increased political polarization, and thus, the need for automatic mitigation methods is growing. Existing mitigation work displays articles from multiple news outlets to provide diverse news coverage, but without neutralizing the bias inherent in each of the displayed articles. Therefore, we propose a new task, a single neutralized article generation out of multiple biased articles, to facilitate more efficient access to balanced and unbiased information. In this paper, we compile a new dataset NeuWS, define an automatic evaluation metric, and provide baselines and multiple analyses to serve as a solid starting point for the proposed task. Lastly, we obtain a human evaluation to demonstrate the alignment between our metric and human judgment.
The year is 2029, and you wake up one morning living in a community called Hope, a dystopian dictatorship. "Everyone here wears the same outfit, lives the same repetitive routine, and is happy … For many, Hope is their entire universe. They are uninterested in the outside world. However, you are different--you have the ability to choose." This is how you are introduced to the game Name of the Will on Kickstarter.
President Trump reacts to the media and Big Tech's role in politics in a'Sunday Morning Futures' exclusive. The year 2020 proved to be a pivotal one in tech, as companies provided essential services during the coronavirus pandemic and unveiled 5G telecom technology while facing unprecedented antitrust scrutiny and accusations of censorship amid an intense election and social justice movement. "I think sometimes we hear that … U.S. innovation is slowing down, and I think the last year has shown that that's not really the case," Neil Chilson, senior research fellow for tech and innovation at the Charles Koch Institute, told Fox News. Chilson gave examples of the country's rapid COVID-19 vaccine development, SpaceX's astronaut launch in May and autonomous driving company Waymo's recent announcement that its self-driving cars will be completely autonomous in trials in Phoenix. "I'm pretty excited about the future. I think 2020 shows that the U.S. is still the world leader in tech and innovation, and we should continue to maintain our cultural appreciation for innovation and a regulatory environment that enables it," he said.
There is an increasing interest in the entwining of the field of antitrust with the field of Artificial Intelligence (AI), frequently referred to jointly as Antitrust and AI (AAI) in the research literature. This study focuses on the synergies entangling antitrust and AI, doing so to extend the literature by proffering the primary ways that these two fields intersect, consisting of: (1) the application of antitrust to AI, and (2) the application of AI to antitrust. To date, most of the existing research on this intermixing has concentrated on the former, namely the application of antitrust to AI, entailing how the marketplace will be altered by the advent of AI and the potential for adverse antitrust behaviors arising accordingly. Opting to explore more deeply the other side of this coin, this research closely examines the application of AI to antitrust and establishes an antitrust vigilance lifecycle to which AI is predicted to be substantively infused for purposes of enabling and bolstering antitrust detection, enforcement, and post-enforcement monitoring. Furthermore, a gradual and incremental injection of AI into antitrust vigilance is anticipated to occur as significant advances emerge amidst the Levels of Autonomy (LoA) for AI Legal Reasoning (AILR).
We propose a Distributional Approach to address Controlled Text Generation from pre-trained Language Models (LMs). This view permits to define, in a single formal framework, "pointwise" and "distributional" constraints over the target LM -- to our knowledge, this is the first approach with such generality -- while minimizing KL divergence with the initial LM distribution. The optimal target distribution is then uniquely determined as an explicit EBM (Energy-Based Model) representation. From that optimal representation we then train the target controlled autoregressive LM through an adaptive distributional variant of Policy Gradient. We conduct a first set of experiments over pointwise constraints showing the advantages of our approach over a set of baselines, in terms of obtaining a controlled LM balancing constraint satisfaction with divergence from the initial LM (GPT-2). We then perform experiments over distributional constraints, a unique feature of our approach, demonstrating its potential as a remedy to the problem of Bias in Language Models. Through an ablation study we show the effectiveness of our adaptive technique for obtaining faster convergence.
In an ideal world, the Supreme Court would provide stability in the run-up to a presidential election, imposing uniform rules based on long-accepted principles of election law. We do not live in that world. One week out from the 2020 election, four Supreme Court justices have launched a scorched-earth mission against voting rights. They teed up a Bush v. Gore reprise that could hand Donald Trump an unearned victory. These justices are in open revolt against voting rights, abandoning the pretense of "voter fraud" and embracing state legislatures' right to disenfranchise their constituents.