Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models. However, current discussions primarily treat gender as binary, which can perpetuate harms such as the cyclical erasure of non-binary gender identities. These harms are driven by model and dataset biases, which are consequences of the non-recognition and lack of understanding of non-binary genders in society. In this paper, we explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies. We also detail how current language representations (e.g., GloVe, BERT) capture and perpetuate these harms and related challenges that need to be acknowledged and addressed for representations to equitably encode gender information.

First came the statements from reproductive organizations. Then came the tech companies. The day after the Supreme Court decided not to block a law in Texas banning most abortions after six weeks, Dallas-based Match Group, which owns Tinder, OkCupid, and Hinge, sent a memo to its employees. "The company generally does not take political stands unless it is relevant to our business," CEO Shar Dubey wrote. "But in this instance, I personally, as a woman in Texas, could not keep silent."

AI is neither artificial, nor is it intelligent. AI cannot recognize things without extensive human training. AI exhibits completely different logic from humans in terms of recognizing, understanding and classifying objects or scenes. The label implies that AI is analogous to human intelligence. AI often lacks any semblance of common sense, can be easily fooled or corrupted and can fail in unexpected and unpredictable ways.

This is the first part of a 2-part series on the growing importance of teaching Data and AI literacy to our students. This will be included in a module I am teaching at Menlo College but wanted to share the blog to help validate the content before presenting to my students. Apple plans to introduce new iPhone software that uses artificial intelligence (AI) to churn through the vast collection of photos that people have taken with their iPhones to detect and report child sexual abuse. See the Wall Street article "Apple Plans to Have iPhones Detect Child Pornography, Fueling Priva..." for more details on Apple's plan. Apple has a strong history of working to protect its customers' privacy.

While I do think it presents an existential threat to some lawyer jobs -- specifically those doing low-skill tasks as part of Biglaw behemoths -- when a company told me several years ago that they would license AI based off the brains of famous attorneys within the decade I went right ahead and laughed. But then we started talking about some of the cool technology Casepoint is bringing to the party and discussed how the system's ability to break down all the data and map out the connections for itself and build a real story of events. Hurt feelings aren't necessarily a fraud claim and keeping the presumption in favor of the four corners of the document can dissuade cases that -- even if they're true -- would be difficult to prove because we can't reliably muster the whole life cycle to sort out in litigation. Would judges eyeing a motion to dismiss start to balk at the risk of missing a fraud when the costs of getting at the whole story in discovery isn't prohibitive? Transactional lawyers might need to think about the well-worn language to avoid an influx of fights if this is the sort of material that a party could easily compile in a dispute.

Artificial Intelligence (AI) has come a long way since 2016, when AlphaGO, a computer program, first defeated an 18-time world champion at the game of GO. Artificial intelligence is profoundly increasing value across a range of industries. The banking and finance industry is no exception. There is a transformative impact to fully adopting AI in banking and finance. According to a study by Mckinsey, AI can add up to $1 Trillion of additional value to the global banking industry annually.

A study in which machine-learning models were trained to assess over 1 million companies has shown that artificial intelligence (AI) can accurately determine whether a startup firm will fail or become successful. The outcome is a tool, Venhound, that has the potential to help investors identify the next unicorn. It is well known that around 90% of startups are unsuccessful: Between 10% and 22% fail within their first year, and this presents a significant risk to venture capitalists and other investors in early-stage companies. In a bid to identify which companies are more likely to succeed, researchers have developed machine-learning models trained on the historical performance of over 1 million companies. Their results, published in KeAi's The Journal of Finance and Data Science, show that these models can predict the outcome of a company with up to 90% accuracy.

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.

Eugene Xiong is the Founder and Chairman of the Board of Foxit, a leading provider of innovative PDF products and services. Artificial intelligence (AI) is changing the way work is performed and data is analyzed in virtually every facet of work today. From algorithms that scour millions of court records to determine the value of personal injuries to artificial intelligence software used in the military to predict an enemy's next move to automation of service and support using intelligent virtual agents, artificial intelligence is reinventing how we process and utilize vast volumes of information in our digital world. Consider the fact that the AI-created painting "Portrait of Edmond de Belamy" was sold by Christie's for a handsome sum of $432,500, and the ART AI Gallery was founded in late 2019. Artificial intelligence is clearly a new source of value creation in many walks of life.

Streaming this week are shows full of transformations: Kesha is a robot sex worker in the podcast “Electric Easy,” Sarah Paulson becomes Linda Tripp in “Impeachment,” and now Doogie Howser is a teenage girl in Hawaii.