Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda

He is currently a senior advisor to Acumen Public Affairs in Brussels, and an adjunct professor of strategic and political communications at American University in Washington. He is an advisor/member to Diplomats Without Borders, and is a mentor at the Techstars start-up accelerator. Within the public sector, conversational AI has the potential to augment and even fully automate aspects of citizen services by providing 24/7 support for everyday administrative tasks. More advanced copilots can also assist public employees in their day-to-day work, reducing process ambiguity and friction points. With the ability to analyze large volumes of data, AI can provide tailored services based on their individual needs and preferences.

Investments in AI have led to transformative advances now impacting our everyday lives, including mapping technologies, voice-assisted smart phones, handwriting recognition for mail delivery, financial trading, smart logistics, spam filtering, language translation, and more. AI advances are also providing great benefits to our social wellbeing in areas such as precision medicine, environmental sustainability, education, and public welfare. Use AI to identify patterns, impacts, and mitigation options for challenges related to public health and climate change. Of government organizations are still at the initial or developing digital maturity stages. However, as with any other project, AI adoption poses challenges that the public sector must overcome.

Advantages and Disadvantages of Artificial Intelligence – The Bottom Line

✅Collaborations between governments and AI industry players will foster innovation and accelerate the development of tailored solutions. Efficiency and cost reduction are critical goals in governance, and AI presents opportunities to streamline operations and automate administrative tasks. Two notable aspects within this scope are the use of chatbots and virtual assistants for citizen support, and the provision of personalized services and recommendations. AI has the potential to revolutionize service delivery in governance, fostering better citizen engagement. Artificial Intelligence (AI) has become a transformative force in various sectors, and its potential impact on government and governance is no exception.

• In today’s fast-paced world, government agencies are constantly challenged with making crucial decisions that have far-reaching impacts on their constituents.
• This is important as societal acceptance will take time and success stories of ethical AI need attention and celebration to win over the sceptical consumer.
• However, as we are progressing fast with a mechanistic, mathematical-proven, notion of fairness and de-biasing of data, one could prove that the AI system could discriminate less than humans.

We all know that Artificial Intelligence (AI) has arrived and has the potential to transform government IT. While many agencies are beginning to explore new AI capabilities, it’s always good to have a clear picture of the benefits and concerns of this new innovation frontier. (a)  There is established, within the Executive Office of the President, the White House Artificial Intelligence Council (White House AI Council). The function of the White House AI Council is to coordinate the activities of agencies across the Federal Government to ensure the effective formulation, development, communication, industry engagement related to, and timely implementation of AI-related policies, including policies set forth in this order. (iv)   take such steps as are necessary and appropriate, consistent with applicable law, to support and advance the near-term actions and long-term strategy identified through the RFI process, including issuing new or updated guidance or RFIs or consulting other agencies or the Federal Privacy Council. (v)    establish an office to coordinate development of AI and other critical and emerging technologies across Department of Energy programs and the 17 National Laboratories.

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I encourage my fellow local government officials to explore the potential of AI and to start thinking about how it can be used to improve the way we serve our citizens. The combination of e-governance and artificial intelligence can greatly improve the accessibility of government services. The use of AI technology allows governments to simplify procedures and offer citizens more efficient and convenient services.

(iii)  ensure that such efforts are guided by principles set out in the NIST AI Risk Management Framework and United States Government National Standards Strategy for Critical and Emerging Technology. (iii)  Within 180 days of the date of this order, the Director of the Office of Personnel Management (OPM), in coordination with the Director of OMB, shall develop guidance on the use of generative AI for work by the Federal workforce. (iii)  The Director of NSF shall use the results of the United States-United Kingdom PETs Prize Challenge to inform the approaches taken, and opportunities identified, for PETs research and adoption. (i)    Within 120 days of the date of this order, the Director of NSF, in collaboration with the Secretary of Energy, shall fund the creation of a Research Coordination Network (RCN) dedicated to advancing privacy research and, in particular, the development, deployment, and scaling of PETs. The RCN shall serve to enable privacy researchers to share information, coordinate and collaborate in research, and develop standards for the privacy-research community.

In the government and public services (GPS) industry, AI adoption and maturity levels tend to vary depending on the government agencies, their existing infrastructure’s reliance on legacy systems, and workforce fluency. Building one, through a government-directed model development and deployment program, would require a lot of effort—and the greatest challenges in developing public A.I. Following the guidance of “Deep Throat” of Watergate fame, in this new series of articles, we follow the federal trail of money to understand the federal market for AI work, the hardware, software, and services being purchased. We also track the key players who allocate the money (legislators), spend the money (program managers), and receive the money (vendors). Taken together, this series provides a comprehensive look at federal IT spending, its direction, and its key players. AI’s capacity to take in and process massive amounts of data in real time means organizations can implement near-instantaneous monitoring capabilities to alert them to issues, recommend actions and, in some cases, initiate a response, according to experts.

A nonprofit board controls OpenAI, an unusual governance structure that made it possible to push out the CEO. This pioneering set of intergovernmental standards on AI stresses the importance of ensuring that AI systems embody human-centred values, such as fairness, transparency, explainability and accountability, among others. The OECD.AI Policy Observatory serves as the orgaisational lead in advancing OECD’s AI-related efforts, including the adoption of the OECD AI Principles.

We are more than capable of harnessing AI for justice, security, and opportunity for all. To learn how AI can improve and automate processes at your government agency/org, simply book a free demo. To harness the benefits of AI in government, investing in skills and workforce development is vital. Governments must equip their employees with the necessary expertise to leverage AI technologies effectively and responsibly.

They think much faster than humans and perform multiple tasks at a time with accurate results. They can even handle tedious repetitive jobs easily with the help of AI algorithms. H2O.ai has grown alongside financial services firms, who are not only our biggest customers but also our biggest investors, and it is evident in the platform today.

HEALTHCARE

One application of artificial intelligence is a robot, which is displacing occupations and increasing unemployment (in a few cases). Therefore, some claim that there is always a chance of unemployment as a result of chatbots and robots replacing humans. Some of the most technologically advanced companies engage with users using digital assistants, which eliminates the need for human personnel. Some chatbots are built in a way that makes it difficult to tell whether we are conversing with a human or a chatbot.

It isn’t clear whether this is an unavoidable flaw of how they work, or whether it can be corrected for. Democracy could be undermined if citizens trust technologies that just make stuff up at random, and the companies trying to sell these technologies can’t be trusted to admit their flaws. AI’s monitoring capabilities can be effective in other areas, such as in enterprise cybersecurity operations where large amounts of data need to be analyzed and understood.

In addition, knowledge-sharing plays a crucial role in ensuring that the benefits of AI are maximized across the government sector. Several state governments have begun adopting AI/ML capabilities to improve and support the services they provide to their citizens, including areas such as public transit, modernizing DMV capabilities, and helping make intelligence decisions for enrollment analytics. However, there is also a darker future that could potentially result if this approach is taken. As discussed previously, the U.S. is significantly behind in AI development and, in particular, trails China by a substantial margin.

For example, public sector agencies can make better use of data by migrating certain technology systems to the cloud and infuse it with AI. AI-powered tools hold the potential to help with pattern detection in large stores of data, and also be able to write computer programs. This could benefit cost optimization and also strengthen cybersecurity, as it can help detect threats quickly. This way, instead of seeking hard-to-find skills, agencies can reduce their skills gap and tap into evolving talent. On top of that, Finland has a uniquely well functioning data governance infrastructure, exemplified by MyData11

– a world class model for personal data management that offers individuals access to and control over the data collected about them. Canada’s ambition to shape global AI policy is realized also through international initiatives.

Easily fit and solve forecasting problems with unique feature engineering and autoML capabilities specifically designed to handle time series data. See how predictions are generated, and easily build forecasts across many categories such as individual skus, product hierarchy and more. The United Nations has urged governments around the world to learn lessons on how to respond to disasters as it warned action to better protect people from major catastrophes had stalled. Benefit fraud at the hands of criminal gangs cost the Department of Work and Pensions nearly £2.1 billion in 2016, a rise of £200 million in just one year. The losses to fraud equate to the entire budget of the Foreign Office, which is £1.1 billion annually.

Another option is to create a regulatory body specifically for AI which would be responsible for licensing and monitoring AI technologies. This could have a range of powers, such as the ability to licence AI technologies, to ensure that AI is being used safely and responsibly, to investigate complaints, and to impose sanctions on those who break the rules. A third option is to develop a code of conduct for businesses using AI; this could cover areas such as data handling, privacy, and accountability.

But the whys, and hows of BigTech regulation are different from those of AI regulation. Regulation of BigTech is a detailed topic on its own and let’s limit ourselves to AI regulation here. What is clear is that governments need to think very carefully about how they should react to the AI revolution. They need to develop a framework for regulating AI, which will protect society while not holding back its development. We could encounter issues with the brittleness of AI systems, dependencies on data, and configurations that constantly change in order to keep improving AI’s performance (a key advantage of Machine Learning is that it constantly learns and improves its current state).

Read more about Benefits Of AI For Government here.

AI-Powered Omnichannel Personalization Platform

AI technology is the earliest to identify atypical data movements in the system, and no human administrator can do it faster. For this reason, lots of retailers use AI powers to provide solid data security. This measure is crucial as companies store a lot of information about their customers and buying trends. What retailers (including your business) do need now is deep insights, allowing them to compete and grow their loyal audience.

The company has received very positive feedback on the metrics it has already collected. EBay expects improvements in metrics such as acceptance rates, editing rates, and conversion rates to assess the effectiveness of the AI-generated descriptions. Generative AI is continuously evolving, leading to the exploration and implementation of new and exciting use cases. Note that the projects mentioned here have all been very recently released. Generative AI in retail still holds immense potential and offers a world of exciting opportunities to be discovered. Let our AI and ML top guns draft a roadmap to AI-driven sales and lay out a plan for step-by-step implementation of intelligent assistants that will take your optimization efforts to a whole new level.

Datadog President Amit Agarwal on Trends in…

According to the most recent research, 35% of companies globally use AI in their operations. More than 50% plan to implement artificial intelligence for business by the end of 2023. Let’s look at the benefits of AI in business, driving the adoption of this technology in the corporate world. Otter.ai can transcribe audio files so you can focus on the conversation at hand, whether it’s a team Zoom meeting or an in-person interview.

• One prime example of this change is how natural language processing enhances shopping experiences.
• AI-powered technologies like facial recognition and computer vision can be used to identify customers when they enter the store, allowing businesses to tailor their offerings accordingly.
• Burberry and Tommy Hilfiger have already launched AI-driven bots on Facebook Messenger that guide customers through their latest collections and answer their inquiries.
• The solution provides highly accurate inventory calculations based on performance analysis and demand throughout the product’s sales history.
• Paid accounts with more conversational AI features and other benefits begin at $14.99 per month.

The market is changing rapidly, responding to global trends and emerging customer needs. The numbers look impressive, but how about details on the real business benefits of artificial intelligence in retail? We’re not going to make you believe in miracles – only in powerful AI algorithms.

How to leverage AI for your Shopify store

With AI, you can expedite processes, get value from company data, and provide clients with a better experience. You can use analysis systems to make data-backed decisions when it comes to your sales and marketing initiatives, like paid advertising or search engine optimization (SEO). This data-driven approach can help your company improve advertising and marketing spend, as well as increase its returns.

On top of that, automation improves accuracy while mitigating costly errors. This is especially helpful if you don’t consider yourself a writer or marketer. You can prompt the AI writing tool to write social media captions, headlines, email subject lines, product descriptions, and more. Some editing is almost always required, but it’s a great way to get started with a blank page.

Plan and Analyze User Journeys

The company created a tech incubator that looks exactly like a usual store but with a huge twist –it’s stuffed with all kinds of innovative technologies. This is a customer assistant that “speaks” several languages and knows the product location better than anyone else. It can provide maps and directions to visitors, while employees provide customers with more in-depth knowledge of the product. Cameras placed all over your store may benefit you by another important feature – mood tracking. Store workers can’t keep an eye on every visitor, but AI algorithms can.

AI-powered technology scrutinizes an image and analyzes colors, shapes, and patterns to identify an item. Chatbots are one more popular application of AI in the retail industry. Chatbots help retailers to provide great customer service, help customers find items on the site, notify them about new collections, and offer them apparel similar to things they’ve already chosen. For example, if a customer has already added black jeans to the cart, a chatbot can offer them new silver Converse shoes to finish the look. Morrisons has partnered with BlueYonder, a leading AI solutions provider for retailers, to optimize stock forecasting and replenishment across its 491 stores. Use of artificial intelligence has helped the company to reduce shelf gaps in-store by up to 30 percent.

How does a CRM system work in Retail?

This not only reduces the flexibility of the solution, but harms its reliability, as well as trust between the client and the supplier. For instance, PayPal leverages AI to analyze transaction patterns, identify fraudulent activities, and protect customers from potential security breaches. It also helps healthcare organizations provide the best assistance to every patient in the form of Virtual Nursing assistants. Thus, taking care of everything – from notifying about the medicine intake timings to sharing real-time health data with the corresponding doctors. AI is transforming healthcare in different ways – with customer experience at the top. Involving the community is critical to our mission of building safe AGI that benefits humanity.

EPAM Startups & SMBs is ready to assist any company embarking on this new phase of digital transformation in retail. We’ll share our multi-year experience in building the most sophisticated commercial solutions based on bleeding-edge technologies from leading providers of cloud-based AI. The short answer is everything.You can build a wide range of retail software custom-built on iOS, Android or PWAs. Or you can opt for one of our pre-packaged designs made for retail businesses that let you start selling online in just 2 weeks.You think it.

The robots can work alongside humans without disrupting operations, ideally yielding higher productivity and lower labor costs. Home improvement retail chain Lowes uses Fellow robots (“LoweBots”) in some locations to help monitor inventory in real-time. Technology like chatbots — the non-human customer service beings trained to engage in human-like exchanges online — are just the start of AI in retail. Thus, it might be better to think strategically and adopt it beforehand. Until some point in your company’s history you save money by not adopting CRM software.

The AI algorithms behind the sales promotion management solution are designed to provide the retailer with the necessary inventory before and during the promotion and to end it without overstocks or write-offs. The solution provides highly accurate inventory calculations based on performance analysis and demand throughout the product’s sales history. When launching promotions for new SKUs, the promotions management software will use the sales figures of analogous or similar products to generate the calculations. This way, each promotion is not based on a hypothesis, but on predictable and manageable results. Kroger, a major US grocery retailer, employs AI to optimize store shelves and pricing.

The GPT Store is rolling out later this month

Read more about Custom-Built AI for Your Retail Business here.

What Is Natural Language Understanding NLU?

For example, in medicine, machines can infer a diagnosis based on previous diagnoses using IF-THEN deduction rules. Using complex algorithms that rely on linguistic rules and AI machine training, Google Translate, Microsoft Translator, and Facebook Translation have become leaders in the field of “generic” language translation. Both NLP and NLU aim to make sense of unstructured data, but there is a difference between the two.

The first successful attempt came out in 1966 in the form of the famous ELIZA program which was capable of carrying on a limited form of conversation with a user. In this context, another term which is often used as a synonym is Natural Language Understanding (NLU).

Machine Learning and Deep Learning

For instance, a simple chatbot can be developed using NLP without the need for NLU. However, for a more intelligent and contextually-aware assistant capable of sophisticated, natural-sounding conversations, natural language understanding becomes essential. It enables the assistant to grasp the intent behind each user utterance, ensuring proper understanding and appropriate responses. Natural language processing primarily focuses on syntax, which deals with the structure and organization of language. NLP techniques such as tokenization, stemming, and parsing are employed to break down sentences into their constituent parts, like words and phrases. This process enables the extraction of valuable information from the text and allows for a more in-depth analysis of linguistic patterns.

The importance of NLU data with respect to NLU has been widely recognized in recent times. The significance of NLU data with respect to NLU is that it will help the user to gain a better understanding of the user’s intent behind the interaction with the bot. Once an intent has been determined, the next step is identifying the sentences’ entities. For example, if someone says, “I went to school today,” then the entity would likely be “school” since it’s the only thing that could have gone anywhere. The syntactic analysis involves the process of identifying the grammatical structure of a sentence. NLG, on the other hand, is above NLU, which can offer more fluidic, engaging, and exciting responses to users as a normal human would give.

What are the future possibilities for NLU and NLP?

By analyzing customer inquiries and detecting patterns, NLU-powered systems can suggest relevant solutions and offer personalized recommendations, making the customer feel heard and valued. Hence the breadth and depth of “understanding” aimed at by a system determine both the complexity of the system (and the implied challenges) and the types of applications it can deal with. The “breadth” of a system is measured by the sizes of its vocabulary and grammar. The “depth” is measured by the degree to which its understanding approximates that of a fluent native speaker.

In such cases, salespeople in the physical stores used to solve our problem and recommended us a suitable product. In the age of conversational commerce, such a task is done by sales chatbots that understand user intent and help customers to discover a suitable product for them via natural language (see Figure 6). Thus, it helps businesses to understand customer needs and offer them personalized products. Data pre-processing aims to divide the natural language content into smaller, simpler sections. ML algorithms can then examine these to discover relationships, connections, and context between these smaller sections.

Natural Language Generation(NLG) is a sub-component of Natural language processing that helps in generating the output in a natural language based on the input provided by the user. This component responds to the user in the same language in which the input was provided say the user asks something in English then the system will return the output in English. Sentiment analysis and intent identification are not necessary to improve user experience if people tend to use more conventional sentences or expose a structure, such as multiple choice questions.

• It allows computers to “learn” from large data sets and improve their performance over time.
• Numeric entities would be divided into number-based categories, such as quantities, dates, times, percentages and currencies.
• However, navigating the complexities of natural language processing and natural language understanding can be a challenging task.
• Natural Language Processing aims to comprehend the user’s command and generate a suitable response against it.

Natural language processing is a field of computer science that works with human languages. It aims to make machines capable of understanding human speech and writing and performing tasks like translation, summarization, etc. NLP has applications in many fields, including information retrieval, machine translation, chatbots, and voice recognition.

Natural Language Understanding (NLU) has become an essential part of many industries, including customer service, healthcare, finance, and retail. NLU technology enables computers and other devices to understand and interpret human language by analyzing and processing the words and syntax used in communication. This has opened up countless possibilities and applications for NLU, ranging from chatbots to virtual assistants, and even automated customer service. In this article, we will explore the various applications and use cases of NLU technology and how it is transforming the way we communicate with machines. This involves breaking down sentences, identifying grammatical structures, recognizing entities and relationships, and extracting meaningful information from text or speech data.

• Systems can improve user experience and communication by using NLP’s language generation.
• NLU is the component that allows the contextual assistant to understand the intent of each utterance by a user.
• It classifies the user’s intention, whether it is a request for information, a command, a question, or an expression of sentiment.
• For example, a sentence may have the same words but mean something entirely different depending on the context in which it is used.

It’s aim is to make computers interpret natural human language in order to understand it and take appropriate actions based on what they have learned about it. This also includes turning the  unstructured data – the plain language query –  into structured data that can be used to query the data set. It uses neural networks and advanced algorithms to learn from large amounts of data, allowing systems to comprehend and interpret language more often involves incorporating external knowledge sources, such as ontologies, knowledge graphs, or commonsense databases, to enhance understanding. The technology also utilizes semantic role labeling (SRL) to identify the roles and relationships of words or phrases in a sentence with respect to a specific predicate. By understanding human language, NLU enables machines to provide personalized and context-aware responses in chatbots and virtual assistants.

Infuse your data for AI

At its core, NLP is about teaching computers to understand and process human language. This can involve everything from simple tasks like identifying parts of speech in a sentence to more complex tasks like sentiment analysis and machine translation. NLP is a field of computer science and artificial intelligence (AI) that focuses on the interaction between computers and humans using natural language. NLP is used to process and analyze large amounts of natural language data, such as text and speech, and extract meaning from it.

It involves understanding the intent behind a user’s input, whether it be a query or a request. NLU-powered chatbots and virtual assistants can accurately recognize user intent and respond accordingly, providing a more seamless customer experience. NLP stands for Natural Language Processing and it is a branch of AI that uses computers to process and analyze large volumes of natural language data. Given the complexity and variation present in natural language, NLP is often split into smaller, frequently-used processes. Common tasks in NLP include part-of-speech tagging, speech recognition, and word embeddings. Together, this help AI converge to the end goal of developing an accurate understanding of natural language structure.

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