The U.S. Equal Employment Opportunity Commission (EEOC) recently released a draft of its new Strategic Enforcement Plan (SEP), outlining its priorities in tackling workplace discrimination over the next four years. The playbook, published in the Federal Register in January, indicates that the agency will be on the lookout for discrimination caused by artificial intelligence (AI) tools. "The EEOC is signaling in its draft SEP that it intends to enforce federal nondiscrimination laws equally, whether the discrimination takes place through traditional recruiting or through the use of modern and automated tools," said Andrew M. Gordon, an attorney with the law firm Hinshaw & Culbertson LLP in Fort Lauderdale, Fla. Over the last decade, AI use in the workplace has skyrocketed. Nearly 1 in 4 organizations uses AI to support HR-related activities, according to a 2022 survey by the Society for Human Resource Management (SHRM).

Adobe chief product officer Scott Belsky has responded to criticism of the company's content analysis policies by saying it has never used customers' creations to train generative AI models. Artists were furious to learn Adobe could automatically analyze their audio, video or text documents stored on its cloud servers to develop and improve its AI products and services unless they opted out earlier this month. Fears that their work would be used to train generative text-to-image models capable of copying their style sparked an outcry. "We have never, ever used anything in our storage to train a generative AI model," Belsky insisted in an interview with Bloomberg this week. Belsky said the content analysis policy was geared towards improving existing features for its graphics software rather than for developing new AI image generation tools.

If you haven't yet heard about ChatGPT, a natural language processing artificial intelligence tool that was released at the end of November 2022, now's the time to learn about it. All signs indicate that this cutting-edge technology and other tools like it will have a significant impact on the practice of law. ChatGPT has the potential to disrupt the way that work gets done across industries, with an impact on print and online publishing, internet search, education, the creation of business and legal documents, and much more. At its most basic, ChatGPT is a chatbot. But it's also much more than that and represents the next phase of information gathering and distribution.

The rise of artificial intelligence (AI) technology has significant implications for state and local governments. One of the main implications is the potential for AI to improve the efficiency and effectiveness of government services. For example, AI-powered chatbots can provide 24/7 customer service for citizens, while machine learning algorithms can analyze large amounts of data to identify patterns and insights that can inform decision-making. Additionally, AI can be used to automate routine tasks, such as processing paperwork and data entry, freeing up government employees to focus on more complex and value-added tasks. However, there are also concerns about the impact of AI on jobs and privacy, and governments will need to consider these issues as they implement AI-based solutions.

Artificial intelligence (AI), which enables machines to learn to perform a task by training on diverse datasets, is one of the most revolutionary developments in scientific history. Although AI and especially deep learning is relatively new, it has already had transformative impact on medicine, biology, transportation, entertainment, and beyond. As AI changes our daily lives at an increasingly fast pace, we are challenged with preparing our society for an AI-driven future. To this end, a critical step is to ensure an AI-ready workforce through education. Advocates of beginning instruction of AI basics at the K-12 level typically note benefits to the workforce, economy, and national security. In this complementary perspective, we discuss why learning AI is beneficial for motivating students and promoting creative thinking, and how to develop a module-based approach that optimizes learning outcomes. We hope to excite and engage more members of the education community to join the effort to advance K-12 AI education in the USA and worldwide.

We describe a method and new no-code software tools enabling domain experts to build custom structured, labeled datasets from the unstructured text of documents and build niche machine learning text classification models traceable to expert-written rules. The Context Rule Assisted Machine Learning (CRAML) method allows accurate and reproducible labeling of massive volumes of unstructured text. CRAML enables domain experts to access uncommon constructs buried within a document corpus, and avoids limitations of current computational approaches that often lack context, transparency, and interpetability. In this research methods paper, we present three use cases for CRAML: we analyze recent management literature that draws from text data, describe and release new machine learning models from an analysis of proprietary job advertisement text, and present findings of social and economic interest from a public corpus of franchise documents. CRAML produces document-level coded tabular datasets that can be used for quantitative academic research, and allows qualitative researchers to scale niche classification schemes over massive text data. CRAML is a low-resource, flexible, and scalable methodology for building training data for supervised ML. We make available as open-source resources: the software, job advertisement text classifiers, a novel corpus of franchise documents, and a fully replicable start-to-finish trained example in the context of no poach clauses.

If business wants to use AI at scale, adhering to the technical guidelines for responsible AI development isn't enough. It must obtain society's explicit approval to deploy the technology. Six years ago, in March 2016, Microsoft Corporation launched an experimental AI-based chatbot, TayTweets, whose Twitter handle was @TayandYou. Tay, an acronym for "thinking about you," mimicked a 19-year-old American girl online, so the digital giant could showcase the speed at which AI can learn when it interacts with human beings. Living up to its description as "AI with zero chill," Tay started off replying cheekily to Twitter users and turning photographs into memes. Some topics were off limits, though; Microsoft had trained Tay not to comment on societal issues such as Black Lives Matter. Soon enough, a group of Twitter users targeted Tay with a barrage of tweets about controversial issues such as the Holocaust and Gamergate. They goaded the chatbot into replying with racist and sexually charged responses, exploiting its repeat-after-me capability. Realizing that Tay was reacting like IBM's Watson, which started using profanity after perusing the online Urban Dictionary, Microsoft was quick to delete the first inflammatory tweets. Less than 16 hours and more than 100,000 tweets later, the digital giant shut down Tay.

Generative AI systems fall under the broad category of machine learning, and here's how one such system--ChatGPT--describes what it can do: Ready to take your creativity to the next level? Look no further than generative AI! This nifty form of machine learning allows computers to generate all sorts of new and exciting content, from music and art to entire virtual worlds. And it's not just for fun--generative AI has plenty of practical uses too, like creating new product designs and optimizing business processes. Unleash the power of generative AI and see what amazing creations you can come up with! Did anything in that paragraph seem off to you?

This may be the year when artificial intelligence transforms daily life. So said Brad Smith, president and vice chairman of Microsoft, at a Vatican-organised event on AI last week. But Smith's statement was less a prediction than a call to action: the event--attended by industry leaders and representatives of the three Abrahamic religions--sought to promote an ethical, human-centred approach to the development of AI. There is no doubt that AI poses a daunting set of operational, ethical and regulatory challenges. And addressing them will be far from straightforward.

As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework to address when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and "replaced by more well-behaved notions". As harm is generally something that is caused, most of these definitions have involved causality at some level. Yet surprisingly, none of them makes use of causal models and the definitions of actual causality that they can express. In this paper we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality (Halpern, 2016). The key novelty of our definition is that it is based on contrastive causation and uses a default utility to which the utility of actual outcomes is compared. We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems.