That was quick: Artificial intelligence has gone from science fiction to novelty to Thing We Are Sure Is the Future. One easy way to measure the change is via headlines -- like the ones announcing Microsoft's $10 billion investment in OpenAI, the company behind the dazzling ChatGPT text generator, followed by other AI startups looking for big money. Or the ones about school districts frantically trying to cope with students using ChatGPT to write their term papers. Or the ones about digital publishers like CNET and BuzzFeed admitting or bragging that they're using AI to make some of their content -- and investors rewarding them for it. "Up until very recently, these were science experiments nobody cared about," says Mathew Dryhurst, co-founder of the AI startup Spawning.ai.

It all started with an obscure article in an obscure journal, published just as the last AI winter was beginning to thaw. In 2004, Andreas Matthias wrote an article with the enigmatic title, "The responsibility gap: Ascribing responsibility for the actions of learning automata." In it, he highlighted a new problem with modern AI systems based on machine learning principles. Once, it made sense to hold the manufacturer or operator of a machine responsible if the machine caused harm, but with the advent of machines that could learn from their interactions with the world, this practice made less sense. Learning automata (to use Matthias' terminology) could do things that were neither predictable nor reasonably foreseeable by their human overseers.

Last week, the New York Attorney General's office sent Madison Square Garden Entertainment a letter demanding answers. The state's top law enforcement agency wants to know more about how the company operating Radio City Music Hall and the storied arena where the NBA's Knicks play uses a face recognition system to deny entry to certain people, and in particular lawyers representing clients in dispute with Madison Square Garden. The letter says that because the ban is thought to cover staff at 90 law firms, it may exclude thousands of people and deter them from taking on cases "including sexual harassment or employment discrimination claims." Since the face recognition system became widely known in recent weeks, MSG's management has stood squarely behind the idea of checking faces at the door with algorithms. In an unsigned statement, the company says its system is not an attack on lawyers, though some are "ambulance chasers and money grabbers."

biPart One of a Two-Part Articlebip Despite the steady growth of global AI adoption there is no comprehensive federal legislation on AI in the United States. Instead the U.S. has a patchwork of various current and proposed AI regulatory frameworks. It is critical for organizations looking to harness this novel technology to understand these frameworks and to prepare to operate in compliance with them.

INan unremarkable conference room inside OpenAI's office, insulated from the mid-January rain pelting San Francisco, company president Greg Brockman surveys the "energy levels" of the team overseeing the company's new artificial intelligence model, ChatGPT. "How are we doing between'everything's on fire and everyone's burned out' to'everyone's just back from the holidays and everything's good'? What's the spectrum?" he asks. "I would say the holidays came at just the right time," replies one lieutenant. Within five days of ChatGPT's November launch, 1 million users overloaded its servers with trivia questions, poetry prompts and recipe requests. Open-AI quietly routed some of the load to its training supercomputer, thousands of interconnected graphics processing units (GPUs) custom-built with allies Microsoft and Nvidia, while long-term work on its next models, like the highly anticipated GPT-4, took a back seat. As the group huddles, ChatGPT's at-capacity servers still turn away users.

California licensed attorneys with international legal expertise in the field of artificial intelligence. We are proud to offer our clients the highest level of expertise at rates that are unmatched in the industry and a guarantee to always stay within your budget. Book a free consultation today to learn more. From chatbots and NLP (natural language processing) to classifiers and intelligent robotics, governments around the world are trying to keep up with a rapidly changing technological climate. Laws around regulatory compliance, liability risk, privacy, transparency and ethics are on the forefront of legislative action.

The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and data-driven approaches with dynamical modeling to assess long-term fairness effects of labor market interventions. Specifically, we develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job-seekers through targeted help. The selection of who receives what help is based on a data-driven intervention model that estimates an individual's chances of finding a job in a timely manner and rests upon data that describes a population in which skills relevant to the labor market are unevenly distributed between two groups (e.g., males and females). The intervention model has incomplete access to the individual's actual skills and can augment this with knowledge of the individual's group affiliation, thus using a protected attribute to increase predictive accuracy. We assess this intervention model's dynamics -- especially fairness-related issues and trade-offs between different fairness goals -- over time and compare it to an intervention model that does not use group affiliation as a predictive feature. We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable.

Data science is an interdisciplinary research area where scientists are typically working with data coming from different fields. When using and analyzing data, the scientists implicitly agree to follow standards, procedures, and rules set in these fields. However, guidance on the responsibilities of the data scientists and the other involved actors in a data science project is typically missing. While literature shows that novel frameworks and tools are being proposed in support of open-science, data reuse, and research data management, there are currently no frameworks that can fully express responsibilities of a data science project. In this paper, we describe the Transparency, Accountability, Privacy, and Societal Responsibility Matrix (TAPS-RM) as framework to explore social, legal, and ethical aspects of data science projects. TAPS-RM acts as a tool to provide users with a holistic view of their project beyond key outcomes and clarifies the responsibilities of actors. We map the developed model of TAPS-RM with well-known initiatives for open data (such as FACT, FAIR and Datasheets for datasets). We conclude that TAPS-RM is a tool to reflect on responsibilities at a data science project level and can be used to advance responsible data science by design.

Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g., hospitals, banks) without sharing the data among parties. Motivated by the practical use cases of federated analytics, we follow a systematic discussion on federated analytics in this article. In particular, we discuss the unique characteristics of federated analytics and how it differs from federated learning. We also explore a wide range of FA queries and discuss various existing solutions and potential use case applications for different FA queries.

"AI as a Service" (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools. AIaaS enables its customers (users) - who may lack the expertise, data, and/or resources to develop their own systems - to easily build and integrate AI capabilities into their applications. Yet, it is known that AI systems can encapsulate biases and inequalities that can have societal impact. This paper argues that the context-sensitive nature of fairness is often incompatible with AIaaS' 'one-size-fits-all' approach, leading to issues and tensions. Specifically, we review and systematise the AIaaS space by proposing a taxonomy of AI services based on the levels of autonomy afforded to the user. We then critically examine the different categories of AIaaS, outlining how these services can lead to biases or be otherwise harmful in the context of end-user applications. In doing so, we seek to draw research attention to the challenges of this emerging area.