Goto

Collaborating Authors

procurement


Guidelines for AI procurement in government

#artificialintelligence

Artificial intelligence holds great potential for public-sector institutions around the world to improve government operations as well as service to citizens. But governments don't necessarily have experience in acquiring modern AI solutions and can tend to be cautious about harnessing new technology. By helping to guide the process of procuring AI, we aim to address major AI adoption pain points early in the process and make it easier for governments to implement this advanced technology. Overall, the guidelines aim to assist all parties involved in the procurement life cycle – policy officials, procurement officials and government commercial teams, data practitioners, and AI-solutions providers – in safeguarding public benefit and well-being.


Unlocking Public Sector Artificial Intelligence

#artificialintelligence

The challenge Artificial intelligence (AI) holds the potential to vastly improve government operations and help meet the needs of citizens in new ways, ranging from traffic management to healthcare delivery to processing tax forms. But most public institutions have not yet adopted this powerful technology. While public sector officials are increasingly aware of the transformational impact of data and AI-powered solutions, the data needed for AI solutions to be developed and deployed is often neither accessible nor discoverable. Public sector officials may also lack the appropriate knowledge and expertise to make strategic buying decisions for AI-powered tools. Uncertainty about ethical considerations adds further layers of complexity. As a result, officials tend to delay buying decisions, or reduce perceived risk by concentrating their purchasing on a few known suppliers. The opportunity The World Economic Forum’s Centre for the Fourth Industrial Revolution has brought together a multistakeholder community to co-design the AI Procurement in a Box toolkit guide for governments to rethink their public procurement processes:  IntroductionGuidelines for AI procurement, presenting the general considerations to be taken when government is procuring AI-powered solutionsWorkbook for policy and procurement officials guiding them through the guidelines ChallengesPilot case studiesThis guidance aims to empower government officials to more confidently make responsible AI purchasing decisions. The tools also improve the experience for AI solutions providers by supporting the creation of transparent and innovative public procurement processes that meet their needs. Impact By co-designing these guidelines with governments, small and large businesses, civil society and academia, the intended impact is the responsible deployment of AI solutions for the public benefit of constituents. Leveraging the significant purchasing power of government in the market, the private-sector adoption of the guidelines can permeate the industry beyond the adoption by public sector organizations. Embedding the principles advocated for in the guidelines into administrative processes will also expand opportunities for new entrants and create a more competitive environment for the ethical development of AI. Further, as industry debates its own standards on these technologies, the government’s influence can help set a baseline for the harmonization of standards-setting. Project accomplishments  March–September 2019: Policy development – the World Economic Forum worked with fellows from the public and private sectors, and a multistakeholder group that also included academia and civil society organizations, to create action-orientated guidelines for government procurement of AI. October–March 2020: Pilot and Iteration – the project team validated guidelines through feedback sessions and a pilot project with the United Kingdom government, the Dubai Electricity and Water Authority and the Government of Bahrain. June 2020: Publication of the AI Procurement in a Box guide that will allow governments to effectively learn and adopt the best practices developed. Contact information For more information, contact Kay Firth-Butterfield, Head of AI and Machine Learning, World Economic Forum, at Kay.Firth-Butterfield@weforum.org.


Guidelines for AI procurement

#artificialintelligence

Artificial Intelligence is a technology that has the potential to greatly improve our public services by reducing costs, enhancing quality, and freeing up valuable time of frontline staff. Recognising this, the UK Government published the Data Ethics Framework and A Guide to using AI in the Public Sector to enable public bodies to adopt AI systems in a way that works for everyone in society. These new procurement guidelines will help inform and empower buyers in the public sector, helping them to evaluate suppliers, then confidently and responsibly procure AI technologies for the benefit of citizens.


Artificial Intelligence and the Integrated Review: The Need for Strategic Prioritisation

#artificialintelligence

The government's Integrated Review comes at a time of considerable technological change. The UK has entered a'Fourth Industrial Revolution' (4IR), which promises to'fundamentally alter the way we live, work, and relate to one another'. This new era will be characterised by scientific breakthroughs in fields such as the Internet of Things, Blockchain, quantum computing, fifth-generation wireless technologies (5G), robotics, and artificial intelligence (AI), which together are expected to deliver transformational changes across almost every sector of the economy. Of particular note are recent developments in AI, specifically advances in the sub-field of machine learning. Progress over the last decade has been driven by an exponential growth in computing power, coupled with increased availability of vast datasets with which to train machine learning algorithms.


Why SMEs should embrace machine learning

#artificialintelligence

RECENT technological advancements have placed artificial intelligence (AI) along with its subfield of machine learning (ML), at the forefront of transforming small and medium-sized enterprises (SMEs) digitally. Some SMEs are starting to tap into ML to shape their business processes and decision-making with the ultimate aim of raising profitability through revenue improvement, cost reduction and new sources of value creation. ML is seen as a continuation of the concepts around predictive analytics. However, a key difference in ML is that it uses mathematical algorithms to train computers in the processing and analysing of large amounts of data, allowing them to produce rules, identify patterns and generate classification predictions. It is important to note that computers automatically learn without human intervention or being explicitly programmed.


Adoption of AI and Blockchain at HHS: Interview with Jose Arrieta, US Department of Health & Human Services (HHS)

#artificialintelligence

Many governments worldwide are looking at using Artificial Intelligence (AI) and other cognitive technologies as part of making their operations more efficient, better serving their citizens, and increasing the range of ways they can meet their missions. It's no surprise then that the US Government and forward thinking leadership is making investments into AI technologies. Additionally, some agencies such as the US Department of Health & Human Services (HHS) are also seeing how other emerging technologies such as blockchain can help. Jose Arrieta, the CIO at the US Department of Health & Human Services (HHS), interviewed on a recent AI Today podcast episode while he was the associate Deputy Assistant Secretary for Acquisition at HHS is one such leader who sees how AI and blockchain can have a big impact at the agency. He became a program manager to build IT systems to use machine learning to analyze biographical information about people.


Public Sector Innovation Conference: Chair's Blog

#artificialintelligence

Like'digital transformation', innovation is an over-used and under-examined term. This applies within business generally, but more especially within the public sector, where there are limits to the amount of disruption and risk that it is considered acceptable to carry within the public domain. Further, a range of questions arises when government'innovates'. These include building the culture and incentives for innovation; understanding what innovation in the digital era is actually about (clue: it's not simply about having a new idea); handling the public-private sector relationship differently; scaling innovations; and handling the politics that inevitably surround changes of almost any kind to public services. The opportunity to chair the second Public Sector Innovation Conference on 25 February was a great opportunity to reflect on these, and many of the other tensions and opportunities that surround ongoing modernisation of public services, and benefit from a really high-quality speaker lineup.


6 European cities seek carbon emission-cut with AI

#artificialintelligence

Six European cities – Helsinki, Amsterdam, Copenhagen, Paris, Stavanger, and Tallinn – join forces in a new project named AI4Cities. The project challenges enterprises, researchers and others to develop solutions utilising artificial intelligence (AI) to generate cuts to carbon dioxide emissions, said the City of Helsinki in a press release. Helsinki emphasises utilisation of data and AI in its digitalisation programme to achieve the city's climate goals. The participating cities' respective programmes to cut carbon dioxide emissions emphasise emissions from transport and housing. Consequently, the AI4Cities Project focuses on emissions generated from transport and traffic as well as the energy consumption by buildings.


How generative design could reshape the future of product development

#artificialintelligence

Most product-development tasks are complex optimization problems. Design teams approach them iteratively, refining an initial best guess through rounds of engineering analysis, interpretation, and refinement. But each such iteration takes time and money, and teams may achieve only a handful of iterations within the development timeline. Because teams rarely have the opportunity to explore alternative solutions that depart significantly from their base-case assumptions, too often the final design is suboptimal. Today's technology offers an alternative.


2020 AI Trends: Here's Why People Actually Want More Machines In The Workplace

#artificialintelligence

Artificial intelligence (AI) and machine learning will be everywhere, and we will learn to love them for all the right reasons. Let's begin with the big picture. Gartner analysts said that AI -- with a particular emphasis on machine learning -- will eventually infiltrate just about every existing technology. IDC predicted companies will invest over $265 billion worldwide in new intelligence technologies by 2023. Slightly further out, IDC researchers predicted that AI will be inescapable by 2025.