The White House's Office of Science and Technology Policy (OSTP) released a blueprint for an Artificial Intelligence (AI) Bill of Rights, a set of five principles and associated practices to help guide the design, use and deployment of automated system, according to a release. The goal of the document and its accompanying handbook is to provide a roadmap for governments, corporations, academics, researchers and other stakeholders to better develop technologies to help rather than harm millions of consumers in housing, financial services, education and other public sectors. "Technology is the new civil rights frontier," President and CEO of the National Fair Housing Alliance (NFHA) Lisa Rice said in a release. "We are learning more each day about how existing technologies harm people and communities. This plan will serve as a guide that encourages developers and marketers of AI tools to search for least discriminatory alternative (LDA) solutions in credit scoring, underwriting, pricing, tenant screening, health management, employee screening and other systems. "It will also continue to position the United States as a global leader in advancing policies and techniques for the development of fair, transparent, explainable, responsible AI." The need for such a document arose from recognized issues with AI and the algorithms used. Tools created with this technology to assist with hiring and credit decisions have been found to reproduce undesired inequities or even create harmful bias and discrimination. When it comes to housing, NFHA stated race-based laws and policies, segregation, private prejudices, real estate agent steering, bank redlining, appraisal bias and restrictive zoning ordinances have contributed to biased data pools, resulting in discriminatory algorithms. "Already, communities of color are facing disproportionate adverse effects from the rapid proliferation of AI technologies in the United States," David Brody, managing attorney of the digital justice initiative at the Lawyers' Committee, said. "The Bill of Rights establishes a new generation of fair information practices.

Abakar Saidov is co-founder and CEO of Beamery, a leader in talent lifecycle management. In the ongoing effort to help businesses address talent shortages and close skills gaps by developing personalized career paths for employees, artificial intelligence (AI) is being increasingly relied upon by time-pressured HR managers and recruiters. With new legislation and regulations being introduced around the use of AI in recruitment, explainability becomes key. By focusing on explainability, the business user can discuss, explain, review and audit the decision-making process, ensuring that their use of AI in talent and recruitment remains ethical. And whilst AI can analyze information and return recommendations to the user, it's important that the human in the loop takes responsibility for making the final decision.

The result of eight years of reporting, this deft chronicle delves into the story of Bobby Johnson, a sixteen-year-old from New Haven, who, in 2006, was coerced into confessing to a brutal murder he didn't commit. Dawidoff presents portraits of the individuals involved, juxtaposed with research on segregation, the Great Migration, and mass incarceration. Bobby, though widely considered innocent, was convicted because he "fit a false stereotype about how things worked in poor neighborhoods." This musical study charts the rise of Romanticism, in the nineteenth century, as composers came to see individual voice as the key to emotional expression, and began to assert their "existential being through a recognizable, even idiosyncratic musical language." Walsh provides biographical sketches of composers and assessments of their work, and weaves in subplots across decades and geography--the impact of nationalism, the development of program music, the ubiquitous spectre of Beethoven.

In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Peter Stone of the University of Texas at Austin. The report, entitled "Artificial Intelligence and Life in 2030," examines eight domains of typical urban settings on which AI is likely to have impact over the coming years: transportation, home and service robots, healthcare, education, public safety and security, low-resource communities, employment and workplace, and entertainment. It aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential and to help guide decisions in industry and governments, as well as to inform research and development in the field. The charge for this report was given to the panel by the AI100 Standing Committee, chaired by Barbara Grosz of Harvard University.

Theorem proving in natural mathematical language - the mixture of symbolic and natural language used by humans - plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence. Yet it has remained underexplored with modern generative models. We study large-scale language models on two new generation tasks: suggesting the next step in a mathematical proof, and full proof generation. We develop NaturalProver, a language model that generates proofs by conditioning on background references (e.g. theorems and definitions that are either retrieved or human-provided), and optionally enforces their presence with constrained decoding. On theorems from the NaturalProofs benchmark, NaturalProver improves the quality of next-step suggestions and generated proofs over fine-tuned GPT-3, according to human evaluations from university-level mathematics students. NaturalProver is capable of proving some theorems that require short (2-6 step) proofs, and providing next-step suggestions that are rated as correct and useful over 40% of the time, which is to our knowledge the first demonstration of these capabilities using neural language models.

Science consists on conceiving hypotheses, confronting them with empirical evidence, and keeping only hypotheses which have not yet been falsified. Under deductive reasoning they are conceived in view of a theory and confronted with empirical evidence in an attempt to falsify it, and under inductive reasoning they are conceived based on observation, confronted with empirical evidence and a theory is established based on the not falsified hypotheses. When the hypotheses testing can be performed with quantitative data, the confrontation can be achieved with Machine Learning methods, whose quality is highly dependent on the hypotheses' complexity, hence on the proper insertion of prior information into the set of hypotheses seeking to decrease its complexity without loosing good hypotheses. However, Machine Learning tools have been applied under the pragmatic view of instrumentalism, which is concerned only with the performance of the methods and not with the understanding of their behavior, leading to methods which are not fully understood. In this context, we discuss how prior information and computational power can be employed to solve a learning problem, but while prior information and a careful design of the hypotheses space has as advantage the interpretability of the results, employing high computational power has the advantage of a higher performance. We discuss why learning methods which combine both should work better from an understanding and performance perspective, arguing in favor of basic theoretical research on Machine Learning, in special about how properties of classifiers may be identified in parameters of modern learning models.

The charge prediction task aims to predict the charge for a case given its fact description. Recent models have already achieved impressive accuracy in this task, however, little is understood about the mechanisms they use to perform the judgment.For practical applications, a charge prediction model should conform to the certain legal theory in civil law countries, as under the framework of civil law, all cases are judged according to certain local legal theories. In China, for example, nearly all criminal judges make decisions based on the Four Elements Theory (FET).In this paper, we argue that trustworthy charge prediction models should take legal theories into consideration, and standing on prior studies in model interpretation, we propose three principles for trustworthy models should follow in this task, which are sensitive, selective, and presumption of innocence.We further design a new framework to evaluate whether existing charge prediction models learn legal theories. Our findings indicate that, while existing charge prediction models meet the selective principle on a benchmark dataset, most of them are still not sensitive enough and do not satisfy the presumption of innocence. Our code and dataset are released at https://github.com/ZhenweiAn/EXP_LJP.

TuSimple Holdings Inc., a U.S.-based self-driving trucking company, faces federal investigations into whether it improperly financed and transferred technology to a Chinese startup, according to people with knowledge of the matter. The people said the concurrent probes by the Federal Bureau of Investigation, Securities and Exchange Commission and Committee on Foreign Investment in the U.S., known as Cfius, are examining TuSimple's relationship with Hydron Inc., a startup that says it is developing autonomous hydrogen-powered trucks and is led by one of TuSimple's co-founders. Investigators at the FBI and SEC are looking at whether TuSimple and its executives--principally Chief Executive Xiaodi Hou--breached fiduciary duties and securities laws by failing to properly disclose the relationship, the people familiar with the matter said. They are also probing whether TuSimple shared with Hydron intellectual property developed in the U.S. and whether that action defrauded TuSimple investors by sending valuable technology to an overseas adversary, the people said. A personal, guided tour to the best scoops and stories every day in The Wall Street Journal.

It's a scenario that would have been unimaginable a few years ago. Earlier this month, a popular Korean-language artist who goes by @ato1004fd on Twitch livestreamed an 11-hour sketch session, letting their 22,000 followers watch as they built up an image of a popular character from the video game Genshin Impact. But by the time @ato1004fd had completed the digital painting, a rogue viewer had already grabbed a picture of the work in process from the stream, used A.I. to "complete" it, and posted their own version to social media--before turning around and accusing @ato1004fd of being the copycat. "Bro, when you ask your fans to cry about art stealing, [be] reasonable," the forger wrote. "So you took as reference an AI image but at least admit it."

Women have faced gender-based discrimination in the workforce throughout history, denied employment in all but a handful of subservient roles, regularly ignored for promotions and pay raises -- and rarely ever compensated at the same rates as their male peers. This long and storied socioeconomic tradition of financially screwing over half the population continues largely unabated into the 21st century where women still make 84 cents on the dollar that men do. In her new book, The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future, Professor of Law and founding member of the Center for Intellectual Property Law and Markets at the University of San Diego, Dr. Orly Lobel, explores how digital technologies, often maligned for their roles in exacerbating societal ills, can be harnessed to undo the damage they've caused. This article has been excerpted from The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future by Orly Lobel. For years, the double standard was glaring: employers demanded secrecy about salaries while asking prospective employees for their salary histories.