GitHub Copilot dubs itself as an "AI pair programmer" for software developers, automatically suggesting code in real time. According to GitHub, Copilot is "powered by Codex, a generative pretrained AI model created by OpenAI" and has been trained on "natural language text and source code from publicly available sources, including code in public repositories on GitHub." However, a class-action lawsuit filed against GitHub Copilot, its parent company Microsoft, and OpenAI claims open-source software piracy and violations of open-source licenses. "The spirit of open source is not just a space where people want to keep it open," says Sal Kimmich, an open-source developer advocate at Sonatype, machine learning engineer, and open source contributor and maintainer. "We have developed processes in order to keep open source secure, and that requires traceability, observability, and verification. Copilot is obscuring the original provenance of those [code] snippets."

The tech industry might be reeling from a wave of layoffs, a dramatic crypto-crash, and ongoing turmoil at Twitter, but despite those clouds some investors and entrepreneurs are already eyeing a new boom--built on artificial intelligence that can generate coherent text, captivating images, and functional computer code. But that new frontier has a looming cloud of its own. A class-action lawsuit filed in a federal court in California this month takes aim at GitHub Copilot, a powerful tool that automatically writes working code when a programmer starts typing. The lawsuit is at an early stage, and its prospects are unclear because the underlying technology is novel and has not faced much legal scrutiny. But legal experts say it may have a bearing on the broader trend of generative AI tools. AI programs that generate paintings, photographs, and illustrations from a prompt, as well as text for marketing copy, are all built with algorithms trained on previous work produced by humans.

Lawyer and developer Matthew Butterick announced last month that he'd teamed up with the Joseph Saveri Law Firm to investigate Copilot. They wanted to know if and how the software infringed upon the legal rights of coders by scraping and emitting their work without proper attribution under current open-source licenses. Now, the firm has filed a class-action lawsuit in the District Court of Northern California in San Francisco. "We are challenging the legality of GitHub Copilot," Butterick said. "This is the first step in what will be a long journey. As far as we know, this is the first class-action case in the US challenging the training and output of AI systems. It will not be the last. AI systems are not exempt from the law. Those who create and operate these systems must remain accountable," he continued in a statement.

Labeling social-media data for custom dimensions of toxicity and social bias is challenging and labor-intensive. Existing transfer and active learning approaches meant to reduce annotation effort require fine-tuning, which suffers from over-fitting to noise and can cause domain shift with small sample sizes. In this work, we propose a novel Active Transfer Few-shot Instructions (ATF) approach which requires no fine-tuning. ATF leverages the internal linguistic knowledge of pre-trained language models (PLMs) to facilitate the transfer of information from existing pre-labeled datasets (source-domain task) with minimum labeling effort on unlabeled target data (target-domain task). Our strategy can yield positive transfer achieving a mean AUC gain of 10.5% compared to no transfer with a large 22b parameter PLM. We further show that annotation of just a few target-domain samples via active learning can be beneficial for transfer, but the impact diminishes with more annotation effort (26% drop in gain between 100 and 2000 annotated examples). Finally, we find that not all transfer scenarios yield a positive gain, which seems related to the PLMs initial performance on the target-domain task.

We analyze publicly available US Supreme Court documents using automated stance detection. In the first phase of our work, we investigate the extent to which the Court's public-facing language is political. We propose and calculate two distinct ideology metrics of SCOTUS justices using oral argument transcripts. We then compare these language-based metrics to existing social scientific measures of the ideology of the Supreme Court and the public. Through this cross-disciplinary analysis, we find that justices who are more responsive to public opinion tend to express their ideology during oral arguments. This observation provides a new kind of evidence in favor of the attitudinal change hypothesis of Supreme Court justice behavior. As a natural extension of this political stance detection, we propose the more specialized task of legal stance detection with our new dataset SC-stance, which matches written opinions to legal questions. We find competitive performance on this dataset using language adapters trained on legal documents.

Scandinavian countries are perceived as role-models when it comes to gender equality. With the advent of pre-trained language models and their widespread usage, we investigate to what extent gender-based harmful and toxic content exist in selected Scandinavian language models. We examine nine models, covering Danish, Swedish, and Norwegian, by manually creating template-based sentences and probing the models for completion. We evaluate the completions using two methods for measuring harmful and toxic completions and provide a thorough analysis of the results. We show that Scandinavian pre-trained language models contain harmful and gender-based stereotypes with similar values across all languages. This finding goes against the general expectations related to gender equality in Scandinavian countries and shows the possible problematic outcomes of using such models in real-world settings.

Organizations of all sizes, across all industries and domains are leveraging artificial intelligence (AI) technologies to solve some of their biggest challenges around operations, customer experience, and much more. However, due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies. Research has shown that these risks can range anywhere from regulatory, compliance, reputational, and user trust, to financial and even societal risks. Depending on the nature and size of the organization, AI technologies can pose a significant risk, if not used in a responsible way. This position paper seeks to present a brief introduction to AI governance, which is a framework designed to oversee the responsible use of AI with the goal of preventing and mitigating risks. Having such a framework will not only manage risks but also gain maximum value out of AI projects and develop consistency for organization-wide adoption of AI.

Artificial intelligence is driving important developments in technology, from controlling autonomous vehicles, to developing medical diagnoses, to combating climate change. The global AI market was valued at nearly $59.7 billion in 2021, and is estimated to reach $422.4 billion by 2028. In conjunction with the global AI market growth, the number of patents for AI technology are on an upswing, and a general survey of patents for AI technologies shows just how innovative these industries are becoming. The types and variety of patent filings for AI technologies in the fast-growing FinTech, biology and pharma, clean/green technology, and automotive industries further show that the expansion of AI advancements is inevitable, and next year should see a continuation of this trend in filings. There's also been significant cross-technology development, further driving AI's prevalence in a number of fields.

In an op-ed in Next City, Yonah Freemark argues that the United States urgently needs new regulations governing autonomous vehicles. Freemark outlines the potential benefits of AVs: improved safety, more time for commuters, expanded access to transportation. "But there's no guarantee these benefits will be achieved," Freemark continues. A botched AV deployment could result in more pedestrians exposed to traffic crashes. AV camera systems -- essential to allow these vehicles to navigate the streets -- could invade peoples' privacy.

In this paper, we study the problem of distributed estimation with an emphasis on communication-efficiency. The proposed algorithm is based on a windowed maximum a posteriori (MAP) estimation problem, wherein each agent in the network locally computes a Kalman-like filter estimate that approximates the centralized MAP solution. Information sharing among agents is restricted to their neighbors only, with guarantees on overall estimate consistency provided via logarithmic opinion pooling. The problem is efficiently distributed using the alternating direction method of multipliers (ADMM), whose overall communication usage is further reduced by a value of information (VoI) censoring mechanism, wherein agents only transmit their primal-dual iterates when deemed valuable to do so. The proposed censoring mechanism is mission-aware, enabling a globally efficient use of communication resources while guaranteeing possibly different local estimation requirements. To illustrate the validity of the approach we perform simulations in a target tracking scenario.