The TCPD-IPD dataset is a collection of questions and answers discussed in the Lower House of the Parliament of India during the Question Hour between 1999 and 2019. Although it is difficult to analyze such a huge collection manually, modern text analysis tools can provide a powerful means to navigate it. In this paper, we perform an exploratory analysis of the dataset. In particular, we present insightful corpus-level statistics and a detailed analysis of three subsets of the dataset. In the latter analysis, the focus is on understanding the temporal evolution of topics using a dynamic topic model. We observe that the parliamentary conversation indeed mirrors the political and socio-economic tensions of each period.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. The Federal Trade Commission (FTC) received a new complaint today from the Center for AI and Digital Policy (CAIDP), which calls for an investigation of OpenAI and its product GPT-4. The complaint argues that the FTC has declared that the use of AI should be "transparent, explainable, fair, and empirically sound while fostering accountability," but claims that OpenAI's GPT-4 "satisfies none of these requirements" and is "biased, deceptive, and a risk to privacy and public safety." CAIDP is a Washington, D.C.-based independent, nonprofit research organization that "assesses national AI policies and practices, trains AI policy leaders, and promotes democratic values for AI." It is headed by president and founder Marc Rotenberg and senior research director Merve Hickok.

It is pretty important to consider the legal implications of artificial intelligence (AI) and its use in various industries. Data Privacy and Security: AI systems generate and store large amounts of data, which can contain sensitive personal information. As such, data privacy and security are major concerns in the development and deployment of AI. Manufacturers, developers, and third-party vendors could all potentially be held liable in the case of an accident or injury caused by an AI system. This raises legal and ethical concerns.

In the last 24 hours alone, we've had hoaxes, FTC complaints and… ads. We'll get into how Microsoft is bringing ads to its Bing chatbot – bound to happen – while OpenAI may have to halt ChatGPT releases in the face of FTC complaints. The nonprofit research organization, Center for AI and Digital Policy (CAIDP), says OpenAI's models are "biased, deceptive" and threaten privacy and public safety. The CAIDP says OpenAI also fails to meet Commission guidelines calling for AI to be transparent, fair and easy to explain. There's no guarantee the FTC will act on the complaint.

The open letter calling for an immediate six-month pause in the AI development arms race and signed by more than 1600 tech luminaries, researchers and responsible technology advocates under the umbrella of the Future of Life Institute is stunning on its face. Self-reflection and caution have never been defining qualities of technology sector leaders. Outside of nuclear technology, it's hard to identify another time when so many have publicly rallied to slow the pace of technology development down, much less call for government regulation and intervention. "Advanced AI could represent a profound change in the history of life on Earth and should be planned for and managed with commensurate care and resources," the letter states. "Unfortunately, this level of planning and management is not happening, even though recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control. "Therefore, we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than (Open AI's) GPT-4.

As tech experts sound the alarm on advanced artificial intelligence, congressional lawmakers were split on the extent to which the federal government is capable of regulating AI platforms. WASHINGTON, D.C. – As tech experts sound the alarm on advanced artificial intelligence, congressional lawmakers were split on the extent to which the federal government is capable of regulating AI platforms. "I think it's important that the government regulate these platforms," Democratic Rep. Maxwell Frost said. "That's one of the major functions of the federal government, to help protect consumers and data and privacy of our citizens." Rep. Maxwell Frost said it's important that the government regulate artificial intelligence platforms, though he also acknowledged he's not "super briefed" on the platforms.

This paper introduces RiskCards, a framework for structured assessment and documentation of risks associated with an application of language models. As with all language, text generated by language models can be harmful, or used to bring about harm. Automating language generation adds both an element of scale and also more subtle or emergent undesirable tendencies to the generated text. Prior work establishes a wide variety of language model harms to many different actors: existing taxonomies identify categories of harms posed by language models; benchmarks establish automated tests of these harms; and documentation standards for models, tasks and datasets encourage transparent reporting. However, there is no risk-centric framework for documenting the complexity of a landscape in which some risks are shared across models and contexts, while others are specific, and where certain conditions may be required for risks to manifest as harms. RiskCards address this methodological gap by providing a generic framework for assessing the use of a given language model in a given scenario. Each RiskCard makes clear the routes for the risk to manifest harm, their placement in harm taxonomies, and example prompt-output pairs. While RiskCards are designed to be open-source, dynamic and participatory, we present a "starter set" of RiskCards taken from a broad literature survey, each of which details a concrete risk presentation. Language model RiskCards initiate a community knowledge base which permits the mapping of risks and harms to a specific model or its application scenario, ultimately contributing to a better, safer and shared understanding of the risk landscape.

This paper discusses OpenAIs ChatGPT, a generative pre-trained transformer, which uses natural language processing to fulfill text-based user requests (i.e., a chatbot). The history and principles behind ChatGPT and similar models are discussed. This technology is then discussed in relation to its potential impact on academia and scholarly research and publishing. ChatGPT is seen as a potential model for the automated preparation of essays and other types of scholarly manuscripts. Potential ethical issues that could arise with the emergence of large language models like GPT-3, the underlying technology behind ChatGPT, and its usage by academics and researchers, are discussed and situated within the context of broader advancements in artificial intelligence, machine learning, and natural language processing for research and scholarly publishing.

Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice. A promising technique for this still-open problem is to train models on the encoded data. Our approach, called Privately Encoded Open Datasets with Public Labels (PEOPL), uses a certain class of randomly constructed transforms to encode sensitive data. Organizations publish their randomly encoded data and associated raw labels for ML training, where training is done without knowledge of the encoding realization. We investigate several important aspects of this problem: We introduce information-theoretic scores for privacy and utility, which quantify the average performance of an unfaithful user (e.g., adversary) and a faithful user (e.g., model developer) that have access to the published encoded data. We then theoretically characterize primitives in building families of encoding schemes that motivate the use of random deep neural networks. Empirically, we compare the performance of our randomized encoding scheme and a linear scheme to a suite of computational attacks, and we also show that our scheme achieves competitive prediction accuracy to raw-sample baselines. Moreover, we demonstrate that multiple institutions, using independent random encoders, can collaborate to train improved ML models.

Incorporating machine learning (ML) components into software products raises new software-engineering challenges and exacerbates existing challenges. Many researchers have invested significant effort in understanding the challenges of industry practitioners working on building products with ML components, through interviews and surveys with practitioners. With the intention to aggregate and present their collective findings, we conduct a meta-summary study: We collect 50 relevant papers that together interacted with over 4758 practitioners using guidelines for systematic literature reviews. We then collected, grouped, and organized the over 500 mentions of challenges within those papers. We highlight the most commonly reported challenges and hope this meta-summary will be a useful resource for the research community to prioritize research and education in this field.