In recent weeks, three of the leading American artificial intelligence firms have announced partnerships with the US government, promising the use of their services to federal workers for a paltry sum. Elon Musk's xAI was supposed to be part of the initiative, but a planned partnership fell apart after the Grok chatbot spouted antisemitic conspiracy theories on X in early July, WIRED has learned. The chaos surrounding the Grok deal reflects the Trump administration's current focus on speed and its disregard, at times, of preexisting norms surrounding government tech procurement. On May 15, fresh off a whirlwind trip to the Middle East with President Donald Trump, OpenAI CEO Sam Altman sent an email to the leadership team at the General Services Administration (GSA), the federal agency that manages government technology. He was inspired by Trump's desire to "go big," he said.

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],

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.

Contestability -- the ability to effectively challenge a decision -- is critical to the implementation of fairness. In the context of governmental decision making about individuals, contestability is often constitutionally required as an element of due process; specific procedures may be required by state or federal law relevant to a particular program. In addition, contestability can be a valuable way to discover systemic errors, contributing to ongoing assessments and system improvement. On January 24-25, 2024, with support from the National Science Foundation and the William and Flora Hewlett Foundation, we convened a diverse group of government officials, representatives of leading technology companies, technology and policy experts from academia and the non-profit sector, advocates, and stakeholders for a workshop on advanced automated decision making, contestability, and the law. Informed by the workshop's rich and wide-ranging discussion, we offer these recommendations. A full report summarizing the discussion is in preparation.

Long, manual processes still dominate procurement today. Oftentimes, monotonous and effortful labor-intensive transactional activities increase the size of the procurement time window – resulting in an inefficient service. In order to reinvent procurement and adapt to the evolving business needs of today and tomorrow, many companies have now digitized procurement in an attempt to expedite these cumbersome processes. But is speed enough in today's uncertain business climate? Machine Learning: Implementing machine learning technology could speed up procurement processes, assist procurement executives in pushing tasks forward, identifying errors and inconsistencies.

Artificial Intelligence comes loaded with the ability to develop a deep understanding of a range of different industries and customer bases. By gathering and analyzing the humungous amount of data that floats about a business and market helps businesses research issues and build solutions that weren't thought of before. In addition to automating tasks, AI can open avenues for new discoveries, methods of product improvement, and finding ways to accomplish a task better. The most common answer to how is artificial intelligence used in a business environment lies in Chatbots. Deep Learning powered AI-powered Chatbots allows businesses to access the layers of data from the neural networks such as customer data and information, which have been built up over time.

Artificial intelligence is being deployed in many different areas. Within higher education, it is used for college admissions and financial aid decisions. Health researchers employ it to scan the scientific literature for chemical compounds that may generate new medical treatments. E-commerce sites deploy algorithms to make product recommendations for consumers based on their areas of interest.1 But one of the most important growth areas lies in finance and operations. Both public and private sector organizations have large budgets to manage and it is important to operate efficiently and effectively. Accusations of budget inefficiencies or wasteful spending decrease public confidence and make it important to figure out how to manage resources in fair ways. To help with budgetary oversight, AI is being used for financial management and fraud detection. Advanced algorithms can spot abnormalities and outliers that can be referred to human investigators to determine if fraud actually has taken place. It is a way to use technology to improve budget audits, personnel performance, and organizational activities. Yet is it crucial to overcome several problems that plague public sector innovation: procurement obstacles, insufficiently trained workers, data limitations, a lack of technical standards, cultural barriers to organizational change, and making sure anti-fraud applications adhere to responsible AI principles.

Artificial Intelligence (AI) systems are increasingly deployed in the public sector. Existing public procurement processes and standards are in urgent need of innovation to address potential risks and harms to citizens. Read our primer based on our research and on input from leading experts in the public sector, data science, civil society, policy, social science, and the law to learn about pathways forward. The COVID-19 pandemic has underlined how biases can manifest in many different aspects of public use technology. For example, federal COVID-19 funding allocation algorithms have favored high-income communities over low-income communities due to historical biases prevalent in the training data. AI solutions that can be implemented fast are typically provided by private companies. As more and more aspects of public service are infused with AI systems and other technologies provided by private companies, we see a growing network of privately owned infrastructure. As government entities outsource critical technological infrastructure (such as data storage and cloud-based systems for data sharing and analysis) to private companies under the guise of modernizing public services, we see a trend towards losing control over critical infrastructure and decreasing accountability to the public that relies on it.

Among the many trends happening in procurement today, artificial intelligence and machine learning are two of the biggest topics. A recent industry conference, Bid Ops' Optimal AI event, brought professionals together to discuss the often confusing, but definitely complementary, role that AI can play in a procurement process. Bid Ops combines sourcing and artificial intelligence under one umbrella solution. By inviting hundreds of thought leaders, professionals and executives to attend the daylong virtual conference, event attendees learned about AI, sourcing and how digital transformation is taking hold in procurement today. On the other hand, there's strong data showing that AI is being rapidly adopted across the procurement industry," Edmund Zagorin, Founder and CEO of Bid Ops, said in an email to Spend Matters. "So it's confusing for a lot of procurement professionals -- is AI a meaningless buzzword or is it critical for businesses to'do more with less' and manage volatility and change?

Referring with my screen, I'm gonna I'm going to finish the presentation first so last night uh as a as a simply next step um oh, yeah. So I think this is good. This confirms what I have uh in my in my slide coming from our consultation. Christians came 30%, I would say this is very uh interesting promising uh and uh perhaps setting the stage for the discussion on how we should we should achieve this. Come out uh next year early next year with the revised plan and with uh a proposed regulatory framework uh that will apply to Europe uh but of course we're interested in uh discussing and exchanging ideas with other countries and in particular with UA and I look forward to this.