AI-Powered World Health Chatbot Is Flubbing Some Answers

Curated on Posted on by Stefaan Verhulst

Article by Jessica Nix: “The World Health Organization is wading into the world of AI to provide basic health information through a human-like avatar. But while the bot responds sympathetically to users’ facial expressions, it doesn’t always know what it’s talking about.

SARAH, short for Smart AI Resource Assistant for Health, is a virtual health worker that’s available to talk 24/7 in eight different languages to explain topics like mental health, tobacco use and healthy eating. It’s part of the WHO’s campaign to find technology that can both educate people and fill staffing gaps with the world facing a health-care worker shortage.

WHO warns on its website that this early prototype, introduced on April 2, provides responses that “may not always be accurate.” Some of SARAH’s AI training is years behind the latest data. And the bot occasionally provides bizarre answers, known as hallucinations in AI models, that can spread misinformation about public health.The WHO’s artificial intelligence tool provides public health information via a lifelike avatar.Source: Bloomberg

SARAH doesn’t have a diagnostic feature like WebMD or Google. In fact, the bot is programmed to not talk about anything outside of the WHO’s purview, including questions on specific drugs. So SARAH often sends people to a WHO website or says that users should “consult with your health-care provider.”

“It lacks depth,” Ramin Javan, a radiologist and researcher at George Washington University, said. “But I think it’s because they just don’t want to overstep their boundaries and this is just the first step.”..(More)”

A Brief History of Automations That Were Actually People

Curated on Posted on by Stefaan Verhulst

Article by Brian Contreras: “If you’ve ever asked a chatbot a question and received nonsensical gibberish in reply, you already know that “artificial intelligence” isn’t always very intelligent.

And sometimes it isn’t all that artificial either. That’s one of the lessons from Amazon’s recent decision to dial back its much-ballyhooed “Just Walk Out” shopping technology, a seemingly science-fiction-esque software that actually functioned, in no small part, thanks to behind-the-scenes human labor.

This phenomenon is nicknamed “fauxtomation” because it “hides the human work and also falsely inflates the value of the ‘automated’ solution,” says Irina Raicu, director of the Internet Ethics program at Santa Clara University’s Markkula Center for Applied Ethics.

Take Just Walk Out: It promises a seamless retail experience in which customers at Amazon Fresh groceries or third-party stores can grab items from the shelf, get billed automatically and leave without ever needing to check out. But Amazon at one point had more than 1,000 workers in India who trained the Just Walk Out AI model—and manually reviewed some of its sales—according to an article published last year on the Information, a technology business website.

An anonymous source who’d worked on the Just Walk Out technology told the outlet that as many as 700 human reviews were needed for every 1,000 customer transactions. Amazon has disputed the Information’s characterization of its process. A company representative told Scientific American that while Amazon “can’t disclose numbers,” Just Walk Out has “far fewer” workers annotating shopping data than has been reported. In an April 17 blog post, Dilip Kumar, vice president of Amazon Web Services applications, wrote that “this is no different than any other AI system that places a high value on accuracy, where human reviewers are common.”…(More)”

The Ethics of Advanced AI Assistants

Curated on Posted on by Stefaan Verhulst

Paper by Iason Gabriel et al: “This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user – across one or more domains – in line with the user’s expectations. The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of applications. It then explores questions around AI value alignment, well-being, safety and malicious uses. Extending the circle of inquiry further, we next consider the relationship between advanced AI assistants and individual users in more detail, exploring topics such as manipulation and persuasion, anthropomorphism, appropriate relationships, trust and privacy. With this analysis in place, we consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants. Finally, we conclude by providing a range of recommendations for researchers, developers, policymakers and public stakeholders…(More)”.

The End of the Policy Analyst? Testing the Capability of Artificial Intelligence to Generate Plausible, Persuasive, and Useful Policy Analysis

Curated on Posted on by Stefaan Verhulst

Article by Mehrdad Safaei and Justin Longo: “Policy advising in government centers on the analysis of public problems and the developing of recommendations for dealing with them. In carrying out this work, policy analysts consult a variety of sources and work to synthesize that body of evidence into useful decision support documents commonly called briefing notes. Advances in natural language processing (NLP) have led to the continuing development of tools that can undertake a similar task. Given a brief prompt, a large language model (LLM) can synthesize information in content databases. This article documents the findings from an experiment that tested whether contemporary NLP technology is capable of producing public policy relevant briefing notes that expert evaluators judge to be useful. The research involved two stages. First, briefing notes were created using three models: NLP generated; human generated; and NLP generated/human edited. Next, two panels of retired senior public servants (with only one panel informed of the use of NLP in the experiment) were asked to judge the briefing notes using a heuristic evaluation rubric. The findings indicate that contemporary NLP tools were not able to, on their own, generate useful policy briefings. However, the feedback from the expert evaluators indicates that automatically generated briefing notes might serve as a useful supplement to the work of human policy analysts. And the speed with which the capabilities of NLP tools are developing, supplemented with access to a larger corpus of previously prepared policy briefings and other policy-relevant material, suggests that the quality of automatically generated briefings may improve significantly in the coming years. The article concludes with reflections on what such improvements might mean for the future practice of policy analysis…(More)”.

The AI That Could Heal a Divided Internet

Curated on Posted on by Stefaan Verhulst

Article by Billy Perrigo: “In the 1990s and early 2000s, technologists made the world a grand promise: new communications technologies would strengthen democracy, undermine authoritarianism, and lead to a new era of human flourishing. But today, few people would agree that the internet has lived up to that lofty goal. 

Today, on social media platforms, content tends to be ranked by how much engagement it receives. Over the last two decades politics, the media, and culture have all been reshaped to meet a single, overriding incentive: posts that provoke an emotional response often rise to the top.

Efforts to improve the health of online spaces have long focused on content moderation, the practice of detecting and removing bad content. Tech companies hired workers and built AI to identify hate speech, incitement to violence, and harassment. That worked imperfectly, but it stopped the worst toxicity from flooding our feeds. 

There was one problem: while these AIs helped remove the bad, they didn’t elevate the good. “Do you see an internet that is working, where we are having conversations that are healthy or productive?” asks Yasmin Green, the CEO of Google’s Jigsaw unit, which was founded in 2010 with a remit to address threats to open societies. “No. You see an internet that is driving us further and further apart.”

What if there were another way? 

Jigsaw believes it has found one. On Monday, the Google subsidiary revealed a new set of AI tools, or classifiers, that can score posts based on the likelihood that they contain good content: Is a post nuanced? Does it contain evidence-based reasoning? Does it share a personal story, or foster human compassion? By returning a numerical score (from 0 to 1) representing the likelihood of a post containing each of those virtues and others, these new AI tools could allow the designers of online spaces to rank posts in a new way. Instead of posts that receive the most likes or comments rising to the top, platforms could—in an effort to foster a better community—choose to put the most nuanced comments, or the most compassionate ones, first…(More)”.

United against algorithms: a primer on disability-led struggles against algorithmic injustice

Curated on Posted on by Stefaan Verhulst

Report by Georgia van Toorn: “Algorithmic decision-making (ADM) poses urgent concerns regarding the rights and entitlements of people with disability from all walks of life. As ADM systems become increasingly embedded in government decision-making processes, there is a heightened risk of harm, such as unjust denial of benefits or inadequate support, accentuated by the expanding reach of state surveillance.

ADM systems have far reaching impacts on disabled lives and life chances. Despite this, they are often designed without the input of people with lived experience of disability, for purposes that do not align with the goals of full rights, participation, and justice for disabled people.

This primer explores how people with disability are collectively responding to the threats posed by algorithmic, data-driven systems – specifically their public sector applications. It provides an introductory overview of the topic, exploring the approaches, obstacles, and actions taken by people with disability in their ‘algoactivist’ struggles…(More)”.

The impact of generative artificial intelligence on socioeconomic inequalities and
policy making

Curated on Posted on by Stefaan Verhulst

Paper by Valerio Capraro et al: “Generative artificial intelligence, including chatbots like ChatGPT, has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the probable impacts of generative AI on four critical domains: work, education, health, and information. Our goal is to warn about how generative AI could worsen existing inequalities while illuminating directions for using AI to resolve pervasive social problems. Generative AI in the workplace can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning but may widen the digital divide. In healthcare, it improves diagnostics and accessibility but could deepen pre-existing inequalities. For information, it democratizes content creation and access but also dramatically expands the production and proliferation of misinformation. Each section covers a specific topic, evaluates existing research, identifies critical gaps, and recommends research directions. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while
mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We contend that these policies should promote shared prosperity through the advancement of generative AI. We suggest several concrete policies to encourage further research and debate. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI…(More)”.

The tech industry can’t agree on what open-source AI means. That’s a problem.

Curated on Posted on by Stefaan Verhulst

Article by Edd Gent: “Suddenly, “open source” is the latest buzzword in AI circles. Meta has pledged to create open-source artificial general intelligence. And Elon Musk is suing OpenAI over its lack of open-source AI models.

Meanwhile, a growing number of tech leaders and companies are setting themselves up as open-source champions. 

But there’s a fundamental problem—no one can agree on what “open-source AI” means. 

On the face of it, open-source AI promises a future where anyone can take part in the technology’s development. That could accelerate innovation, boost transparency, and give users greater control over systems that could soon reshape many aspects of our lives. But what even is it? What makes an AI model open source, and what disqualifies it?

The answers could have significant ramifications for the future of the technology. Until the tech industry has settled on a definition, powerful companies can easily bend the concept to suit their own needs, and it could become a tool to entrench the dominance of today’s leading players.

Entering this fray is the Open Source Initiative (OSI), the self-appointed arbiters of what it means to be open source. Founded in 1998, the nonprofit is the custodian of the Open Source Definition, a widely accepted set of rules that determine whether a piece of software can be considered open source. 

Now, the organization has assembled a 70-strong group of researchers, lawyers, policymakers, activists, and representatives from big tech companies like Meta, Google, and Amazon to come up with a working definition of open-source AI…(More)”.

New Jersey is turning to AI to improve the job search process

Curated on Posted on by Stefaan Verhulst

Article by Beth Simone Noveck: “Americans are experiencing some conflicting feelings about AI.

While people are flocking to new roles like prompt engineer and AI ethicist, the technology is also predicted to put many jobs at risk, including computer programmers, data scientists, graphic designers, writers, lawyers.

Little wonder, then, that a national survey by the Heldrich Center for Workforce Development found an overwhelming majority of Americans (66%) believe that they “will need more technological skills to achieve their career goals.” One thing is certain: Workers will need to train for change. And in a world of misinformation-filled social media platforms, it is increasingly important for trusted public institutions to provide reliable, data-driven resources.

In New Jersey, we’ve tried doing just that by collaborating with workers, including many with disabilities, to design technology that will support better decision-making around training and career change. Investing in similar public AI-powered tools could help support better consumer choice across various domains. When a public entity designs, controls and implements AI, there is a far greater likelihood that this powerful technology will be used for good.

In New Jersey, the public can find reliable, independent, unbiased information about training and upskilling on the state’s new MyCareer website, which uses AI to make personalized recommendations about your career prospects, and the training you will need to be ready for a high-growth, in-demand job…(More)”.

Global AI governance: barriers and pathways forward 

Curated on Posted on by Stefaan Verhulst

Paper by Huw Roberts, Emmie Hine, Mariarosaria Taddeo, Luciano Floridi: “This policy paper is a response to the growing calls for ambitious new international institutions for AI. It maps the geopolitical and institutional barriers to stronger global AI governance and considers potential pathways forward in light of these constraints. We argue that a promising foundation of international regimes focused on AI governance is emerging, but the centrality of AI to interstate competition, dysfunctional international institutions and disagreement over policy priorities problematizes substantive cooperation. We propose strengthening the existing weak ‘regime complex’ of international institutions as the most desirable and realistic path forward for global AI governance. Strengthening coordination between, and the capacities of, existing institutions supports mutually reinforcing policy change, which, if enacted properly, can lead to catalytic change across the various policy areas where AI has an impact. It also facilitates the flexible governance needed for rapidly evolving technologies.

To make this argument, we outline key global AI governance processes in the next section. In the third section, we analyse how first- and second-order cooperation problems in international relations apply to AI. In the fourth section we assess potential routes for advancing global AI governance, and we conclude by providing recommendations on how to strengthen the weak AI regime complex…(More)”.