How is AI augmenting collective intelligence for the SDGs?

Curated on Posted on by Stefaan Verhulst

Article by UNDP: “Increasingly AI techniques like natural language processing, machine learning and predictive analytics are being used alongside the most common methods in collective intelligence, from citizen science and crowdsourcing to digital democracy platforms.

At its best, AI can be used to augment and scale the intelligence of groups. In this section we describe the potential offered by these new combinations of human and machine intelligence. First we look at the applications that are most common, where AI is being used to enhance efficiency and categorize unstructured data, before turning to the emerging role of AI – where it helps us to better understand complex systems.

These are the three main ways AI and collective intelligence are currently being used together for the SDGs:

1. Efficiency and scale of data processing

AI is being effectively incorporated into collective intelligence projects where timing is paramount and a key insight is buried deep within large volumes of unstructured data. This combination of AI and collective intelligence is most useful when decision makers require an early warning to help them manage risks and distribute public resources more effectively. For example, Dataminr’s First Alert system uses pre-trained machine learning models to sift through text and images scraped from the internet, as well as other data streams, such as audio broadcasts, to isolate early signals that anticipate emergency events…(More)”. (See also: Where and when AI and CI meet: exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern).

AI for collective intelligence

Curated on Posted on by Stefaan Verhulst

Introduction to special issue by Christoph Riedl and David De Cremer: “AI has emerged as a transformative force in society, reshaping economies, work, and everyday life. We argue that AI can not only improve short-term productivity but can also enhance a group’s collective intelligence. Specifically, AI can be employed to enhance three elements of collective intelligence: collective memory, collective attention, and collective reasoning. This editorial reviews key emerging work in the area to suggest ways in which AI can support the socio-cognitive architecture of collective intelligence. We will then briefly introduce the articles in the “AI for Collective Intelligence” special issue…(More)”.

Expanding the Horizons of Collective Artificial Intelligence (CAI): From Individual Nudges to Relational Cognition

Curated on Posted on by Stefaan Verhulst

Blog by Evelien Verschroeven: “As AI continues to evolve, it is essential to move beyond focusing solely on individual behavior changes. The individual input — whether through behavior, data, or critical content — remains important. New data and fresh perspectives are necessary for AI to continue learning, growing, and improving its relevance. However, as we head into what some are calling the golden years of AI, it’s critical to acknowledge a potential challenge: within five years, it is predicted that 50% of AI-generated content will be based on AI-created material, creating a risk of inbreeding where AI learns from itself, rather than from the diversity of human experience and knowledge.

Platforms like Google’s AI for Social Good and Unanimous AI’s Swarm play pivotal roles in breaking this cycle. By encouraging the aggregation of real-world data, they add new content that can influence and shape AI’s evolution. While they focus on individual data contributions, they also help keep AI systems grounded in real-world scenarios, ensuring that the content remains critical and diverse.

However, human oversight is key. AI systems, even with the best intentions, are still learning from patterns that humans provide. It’s essential that AI continues to receive diverse human input, so that its understanding remains grounded in real-world perspectives. AI should be continuously checked and guided by human creativity, critical thinking, and social contexts, to ensure that it doesn’t become isolated or too self-referential.

As we continue advancing AI, it is crucial to embrace relational cognition and collective intelligence. This approach will allow AI to address both individual and collective needs, enhancing not only personal development but also strengthening social bonds and fostering more resilient, adaptive communities…(More)”.

Leveraging Crowd Intelligence to Enhance Fairness and Accuracy in AI-powered Recruitment Decisions

Curated on Posted on by Stefaan Verhulst

Paper by Zhen-Song Chen and Zheng Ma: “Ensuring fair and accurate hiring outcomes is critical for both job seekers’ economic opportunities and organizational development. This study addresses the challenge of mitigating biases in AI-powered resume screening systems by leveraging crowd intelligence, thereby enhancing problem-solving efficiency and decision-making quality. We propose a novel counterfactual resume-annotation method based on a causal model to capture and correct biases from human resource (HR) representatives, providing robust ground truth data for supervised machine learning. The proposed model integrates multiple language embedding models and diverse HR-labeled data to train a cohort of resume-screening agents. By training 60 such agents with different models and data, we harness their crowd intelligence to optimize for three objectives: accuracy, fairness, and a balance of both. Furthermore, we develop a binary bias-detection model to visualize and analyze gender bias in both human and machine outputs. The results suggest that harnessing crowd intelligence using both accuracy and fairness objectives helps AI systems robustly output accurate and fair results. By contrast, a sole focus on accuracy may lead to severe fairness degradation, while, conversely, a sole focus on fairness leads to a relatively minor loss of accuracy. Our findings underscore the importance of balancing accuracy and fairness in AI-powered resume-screening systems to ensure equitable hiring outcomes and foster inclusive organizational development…(More)”

Collective Intelligence: The Rise of Swarm Systems and their Impact on Society

Curated on Posted on by Stefaan Verhulst

Book edited by Uwe Seebacher and Christoph Legat: “Unlock the future of technology with this captivating exploration of swarm intelligence. Dive into the future of autonomous systems, enhanced by cutting-edge multi-agent systems and predictive research. Real-world examples illustrate how these algorithms drive intelligent, coordinated behavior in industries like manufacturing and energy. Discover the innovative Industrial-Disruption-Index (IDI), pioneered by Uwe Seebacher, which predicts industry disruptions using swarm intelligence. Case studies from media to digital imaging offer invaluable insights into the future of industrial life cycles.

Ideal for AI enthusiasts and professionals, this book provides inspiring, actionable insights for the future. It redefines artificial intelligence, showcasing how predictive intelligence can revolutionize group coordination for more efficient and sustainable systems. A crucial chapter highlights the shift from the Green Deal to the Emerald Deal, showing how swarm intelligence addresses societal challenges…(More)”.

Generative Agent Simulations of 1,000 People

Curated on Posted on by Stefaan Verhulst

Paper by Joon Sung Park: “The promise of human behavioral simulation–general-purpose computational agents that replicate human behavior across domains–could enable broad applications in policymaking and social science. We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals–applying large language models to qualitative interviews about their lives, then measuring how well these agents replicate the attitudes and behaviors of the individuals that they represent. The generative agents replicate participants’ responses on the General Social Survey 85% as accurately as participants replicate their own answers two weeks later, and perform comparably in predicting personality traits and outcomes in experimental replications. Our architecture reduces accuracy biases across racial and ideological groups compared to agents given demographic descriptions. This work provides a foundation for new tools that can help investigate individual and collective behavior…(More)”.

Conversational Swarms of Humans and AI Agents enable Hybrid Collaborative Decision-making

Curated on Posted on by Stefaan Verhulst

Paper by Louis Rosenberg et al: “Conversational Swarm Intelligence (CSI) is an AI-powered communication and collaboration technology that allows large, networked groups (of potentially unlimited size) to hold thoughtful conversational deliberations in real-time. Inspired by the efficient decision-making dynamics of fish schools, CSI divides a human population into a set of small subgroups connected by AI agents. This enables the full group to hold a unified conversation. In this study, groups of 25 participants were tasked with selecting a roster of players in a real Fantasy Baseball contest. A total of 10 trials were run using CSI. In half the trials, each subgroup was augmented with a fact-providing AI agent referred to herein as an Infobot. The Infobot was loaded with a wide range of MLB statistics. The human participants could query the Infobot the same way they would query other persons in their subgroup. Results show that when using CSI, the 25-person groups outperformed 72% of individually surveyed participants and showed significant intelligence amplification versus the mean score (p=0.016). The CSI-enabled groups also significantly outperformed the most popular picks across the collected surveys for each daily contest (p<0.001). The CSI sessions that used Infobots scored slightly higher than those that did not, but it was not statistically significant in this study. That said, 85% of participants agreed with the statement ‘Our decisions were stronger because of information provided by the Infobot’ and only 4% disagreed. In addition, deliberations that used Infobots showed significantly less variance (p=0.039) in conversational content across members. This suggests that Infobots promoted more balanced discussions in which fewer members dominated the dialog. This may be because the infobot enabled participants to confidently express opinions with the support of factual data…(More)”.

When combinations of humans and AI are useful: A systematic review and meta-analysis

Curated on Posted on by Stefaan Verhulst

Paper by Michelle Vaccaro, Abdullah Almaatouq & Thomas Malone: “Inspired by the increasing use of artificial intelligence (AI) to augment humans, researchers have studied human–AI systems involving different tasks, systems and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here we addressed this question by conducting a preregistered systematic review and meta-analysis of 106 experimental studies reporting 370 effect sizes. We searched an interdisciplinary set of databases (the Association for Computing Machinery Digital Library, the Web of Science and the Association for Information Systems eLibrary) for studies published between 1 January 2020 and 30 June 2023. Each study was required to include an original human-participants experiment that evaluated the performance of humans alone, AI alone and human–AI combinations. First, we found that, on average, human–AI combinations performed significantly worse than the best of humans or AI alone (Hedges’ g = −0.23; 95% confidence interval, −0.39 to −0.07). Second, we found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content. Finally, when humans outperformed AI alone, we found performance gains in the combination, but when AI outperformed humans alone, we found losses. Limitations of the evidence assessed here include possible publication bias and variations in the study designs analysed. Overall, these findings highlight the heterogeneity of the effects of human–AI collaboration and point to promising avenues for improving human–AI systems…(More)”.

Orphan Articles: The Dark Matter of Wikipedia

Curated on Posted on by Stefaan Verhulst

Paper by Akhil Arora, Robert West, Martin Gerlach: “With 60M articles in more than 300 language versions, Wikipedia is the largest platform for open and freely accessible knowledge. While the available content has been growing continuously at a rate of around 200K new articles each month, very little attention has been paid to the accessibility of the content. One crucial aspect of accessibility is the integration of hyperlinks into the network so the articles are visible to readers navigating Wikipedia. In order to understand this phenomenon, we conduct the first systematic study of orphan articles, which are articles without any incoming links from other Wikipedia articles, across 319 different language versions of Wikipedia. We find that a surprisingly large extent of content, roughly 15\% (8.8M) of all articles, is de facto invisible to readers navigating Wikipedia, and thus, rightfully term orphan articles as the dark matter of Wikipedia. We also provide causal evidence through a quasi-experiment that adding new incoming links to orphans (de-orphanization) leads to a statistically significant increase of their visibility in terms of the number of pageviews. We further highlight the challenges faced by editors for de-orphanizing articles, demonstrate the need to support them in addressing this issue, and provide potential solutions for developing automated tools based on cross-lingual approaches. Overall, our work not only unravels a key limitation in the link structure of Wikipedia and quantitatively assesses its impact, but also provides a new perspective on the challenges of maintenance associated with content creation at scale in Wikipedia…(More)”.

AI-enhanced collective intelligence

Curated on Posted on by Stefaan Verhulst

Paper by Hao Cui and Taha Yasseri: “Current societal challenges exceed the capacity of humans operating either alone or collectively. As AI evolves, its role within human collectives will vary from an assistive tool to a participatory member. Humans and AI possess complementary capabilities that, together, can surpass the collective intelligence of either humans or AI in isolation. However, the interactions in humanAI systems are inherently complex, involving intricate processes and interdependencies. This review incorporates perspectives from complex network science to conceptualize a multilayer representation of human-AI collective intelligence, comprising cognition, physical, and information layers. Within this multilayer network, humans and AI agents exhibit varying characteristics; humans differ in diversity from surface-level to deep-level attributes, while AI agents range in degrees of functionality and anthropomorphism. We explore how agents’ diversity and interactions influence the system’s collective intelligence and analyze real-world instances of AI-enhanced collective intelligence. We conclude by considering potential challenges and future developments in this field….(More)” See also: Where and When AI and CI Meet: Exploring the Intersection of Artificial and Collective Intelligence