Ministers are facing calls for greater transparency about public data that may be shared with the US tech company OpenAI after the government signed a wide-ranging agreement with the 300m ( 222m) company that critics compared to letting a fox into a henhouse. Chi Onwurah, the chair of the House of Commons select committee on science, innovation and technology, warned that Monday's sweeping memorandum of understanding between OpenAI's chief executive, Sam Altman, and the technology secretary, Peter Kyle, was "very thin on detail" and called for guarantees that public data would remain in the UK and clarity about how much of it OpenAI would have access to. The deal paves the way for the Silicon Valley firm behind ChatGPT to explore deploying advanced AI technology in areas including justice, defence and security, and education. It includes OpenAI and the government "partnering to develop safeguards that protect the public and uphold democratic values". Kyle said he wanted Britain to be "front and centre when it comes to developing and deploying AI" and "this can't be achieved without companies like OpenAI".

Data is an increasingly vital component of decision making processes across industries. However, data access raises privacy concerns motivating the need for privacy-preserving techniques such as differential privacy. Data markets provide a means to enable wider access as well as determine the appropriate privacy-utility trade-off. Existing data market frameworks either require a trusted third party to perform computationally expensive valuations or are unable to capture the combinatorial nature of data value and do not endogenously model the effect of differential privacy. This paper addresses these shortcomings by proposing a valuation mechanism based on the Wasserstein distance for differentially-private data, and corresponding procurement mechanisms by leveraging incentive mechanism design theory, for task-agnostic data procurement, and task-specific procurement co-optimisation. The mechanisms are reformulated into tractable mixed-integer second-order cone programs, which are validated with numerical studies.

Thus, most public-sector AI systems used today are developed by and acquired from private vendors. A growing number of academic and advocacy efforts have pointed out how AI systems procured in the public sector have predominantly targeted narrowly defined notions of efficiency and performance enhancements, resulting in adverse effects that disparately impact marginalized communities[18, 37, 46, 50, 86, 96]. While such incidents have exposed flaws in individual AI systems, they highlight deeper issues in how AI is acquired, used, and governed in the public sector. The AI procurement process encompasses decisions of which AI tools to ask for, adopt or reject, and the manner in which they are developed and deployed: decisions of critical importance for communities who may be harmed by AI. Such decisions not only influence the performance and risks posed by AI systems, but also play a significant role in shaping broader governance practices and ethical standards by which AI operates in the public sector. Interestingly, there is a long history of governments adapting their public procurement practices to enact social change, e.g., by creating processes that prioritize minority-owned businesses [62],

In an October 2023 executive order (EO), President Biden issued a detailed but largely aspirational road map for the safe and responsible development and use of artificial intelligence (AI). The challenge for the January 24-25, 2024 workshop was to transform those aspirations regarding one specific but crucial issue -- the ability of individuals to challenge government decisions made about themselves -- into actionable guidance enabling agencies to develop, procure, and use genuinely contestable advanced automated decision-making systems. While the Administration has taken important steps since the October 2023 EO, the insights garnered from our workshop remain highly relevant, as the requirements for contestability of advanced decision-making systems are not yet fully defined or implemented. The workshop brought together technologists, members of government agencies and civil society organizations, litigators, and researchers in an intensive two-day meeting that examined the challenges that users, developers, and agencies faced in enabling contestability in light of advanced automated decision-making systems. To ensure a free and open flow of discussion, the meeting was held under a modified version of the Chatham House rule. Participants were free to use any information or details that they learned, but they may not attribute any remarks made at the meeting by the identity or the affiliation of the speaker. Thus, the workshop summary that follows anonymizes speakers and their affiliation. Where an identification of an agency, company, or organization is made, it is done from a public, identified resource and does not necessarily reflect statements made by participants at the workshop. This document is a report of that workshop, along with recommendations and explanatory material.

Public sector use of AI has been quietly on the rise for the past decade, but only recently have efforts to regulate it entered the cultural zeitgeist. While simple to articulate, promoting ethical and effective roll outs of AI systems in government is a notoriously elusive task. On the one hand there are hard-to-address pitfalls associated with AI-based tools, including concerns about bias towards marginalized communities, safety, and gameability. On the other, there is pressure not to make it too difficult to adopt AI, especially in the public sector which typically has fewer resources than the private sector$\unicode{x2014}$conserving scarce government resources is often the draw of using AI-based tools in the first place. These tensions create a real risk that procedures built to ensure marginalized groups are not hurt by government use of AI will, in practice, be performative and ineffective. To inform the latest wave of regulatory efforts in the United States, we look to jurisdictions with mature regulations around government AI use. We report on lessons learned by officials in Brazil, Singapore and Canada, who have collectively implemented risk categories, disclosure requirements and assessments into the way they procure AI tools. In particular, we investigate two implemented checklists: the Canadian Directive on Automated Decision-Making (CDADM) and the World Economic Forum's AI Procurement in a Box (WEF). We detail three key pitfalls around expertise, risk frameworks and transparency, that can decrease the efficacy of regulations aimed at government AI use and suggest avenues for improvement.

Supply chain optimization is key to a healthy and profitable business. Many companies use online procurement systems to agree contracts with suppliers. It is vital that the most competitive suppliers are invited to bid for such contracts. In this work, we propose a recommender system to assist with supplier discovery in road freight online procurement. Our system is able to provide personalized supplier recommendations, taking into account customer needs and preferences. This is a novel application of recommender systems, calling for design choices that fit the unique requirements of online procurement. Our preliminary results, using real-world data, are promising.

Although procurement fraud is always a critical problem in almost every free market, audit departments still have a strong reliance on reporting from informed sources when detecting them. With our generous cooperator, SF Express, sharing the access to the database related with procurements took place from 2015 to 2017 in their company, our team studies how machine learning techniques could help with the audition of one of the most profound crime among current chinese market, namely procurement frauds. By representing each procurement event as 9 specific features, we construct neural network models to identify suspicious procurements and classify their fraud types. Through testing our models over 50000 samples collected from the procurement database, we have proven that such models -- despite having space for improvements -- are useful in detecting procurement frauds.

As artificial intelligence grows more ubiquitous, its potential and the challenges it presents are coming increasingly into focus. How we balance the risks and opportunities is shaping up as one of the defining questions of our era. In much the same way that cities have emerged as hubs of innovation in culture, politics, and commerce, so they are defining the frontiers of AI governance. Some examples of how cities have been taking the lead include the Cities Coalition for Digital Rights, the Montreal Declaration for Responsible AI, and the Open Dialogue on AI Ethics. Others can be found in San Francisco's ban of facial-recognition technology, and New York City's push for regulating the sale of automated hiring systems and creation of an algorithms management and policy officer.

The sky is no longer the limit for cloud computing giants, as the slowing economy strikes at one of tech's biggest growth stories of the past decade. Two of the top cloud providers, Amazon Web Services Inc. and Google LLC, today revealed revenues in their cloud units, which have boosted their growth and profits for years now. The results weren't bad -- but cloud is no longer on the kind of rocket ride that can overcome big slowdowns in e-commerce and ad spending. Alphabet's Google Cloud unit posted a 33% rise in its fourth quarter, to $7.315 billion, with an operating loss of $480 million, down significantly from a loss of $890 million a year ago. Zacks Consensus Forecast had Google Cloud revenue rising 32% from a year ago, to $7.3 billion, so it managed to meet expectations but was still down from 38% growth in the third quarter.

The costs and impacts of government corruption range from impairing a country's economic growth to affecting its citizens' well-being and safety. Public contracting between government dependencies and private sector instances, referred to as public procurement, is a fertile land of opportunity for corrupt practices, generating substantial monetary losses worldwide. Thus, identifying and deterring corrupt activities between the government and the private sector is paramount. However, due to several factors, corruption in public procurement is challenging to identify and track, leading to corrupt practices going unnoticed. This paper proposes a machine learning model based on an ensemble of random forest classifiers, which we call hyper-forest, to identify and predict corrupt contracts in M\'exico's public procurement data. This method's results correctly detect most of the corrupt and non-corrupt contracts evaluated in the dataset. Furthermore, we found that the most critical predictors considered in the model are those related to the relationship between buyers and suppliers rather than those related to features of individual contracts. Also, the method proposed here is general enough to be trained with data from other countries. Overall, our work presents a tool that can help in the decision-making process to identify, predict and analyze corruption in public procurement contracts.