New technologies have changed operational processes across industries, from supply chain and CRM applications to the rollout of simple Apps that expedite customer touchpoints with organizations. The financial industry is no exception. Technologies have expedited payment processes, trading and investment activities, commercial banking and lending operations, to name a few. Unfortunately, digitization can also introduce a dark side to business activities in the form of cybercrimes. A growing problem in fintech is money laundering. According to the US government, money laundering involves disguising financial assets so they can be used without detection of the illegal activity that produced them and transformed into funds with a legal source. The UN has estimated that up to 5% of global GDP is laundered annually. As a result, regulators have adopted a sound risk management approach, including policies and regulations to mitigate these activities. Difficulties arise, however, as financial organizations seek to maintain their operational efficiencies while adhering to these regulations.
Embodied AI changes the data problem in artificial intelligence. Text, images, and videos can be collected from the digital world at large scale, but robot interaction data must be produced through physical execution [1], [2]. A useful dataset may include camera streams, robot trajectories, force and tactile signals, teleoperation commands, task outcomes, and environmental context. These records are costly because they depend on hardware, operators, calibration, safety checks, and repeated trials. As a result, many embodied AI systems still rely on small demonstrations, isolated laboratory datasets, or task-specific data pipelines.
The democratic evolution of governments across Africa took prominence after a series of military dictatorships following the shift from colonial rule. This era of post-independence democratization efforts, often referred to as the "third wave of democratization" [25], began in the early 90s, with countries such as Benin, Guinea-Bissau, and Zambia transitioning to democratic rule. While organizations like the African Union, initially established as the Organization for African Unity in 1963, have been essential in promoting democratic principles, a growing shift towards authoritarian rule across the continent has resulted in democratic backsliding in countries such as Ethiopia, Côte D'Ivoire, Guinea, the Central African Republic, and Mali [2]. While the COVID-19 pandemic has accelerated this democratic backsliding [22], African countries face enduring issues such as corruption, economic instability, ethnic rivalry, religious conflict, and weak governance institutions that hinder democratic maturity [4, 7]. As political instability, conflict, and foreign intervention undermine the quality of governance, AI will introduce new complexities to these challenges. Issues with access to electrification, a lack of computing infrastructure, and a low concentration of skilled AI talent hinder AI development across the continent. Additionally, as African governments increasingly leverage AI in governance and democratic processes such as elections, AI tools could limit political awareness, intensify voter suppression, and spread disinformation.
Since inception, AI research has been inspired by the idea of building machines that think and behave like humans. Or put another way, machines that think and act rationally [14]. This paradigm has its historical foundations in the assumption that what makes humans different from other forms of life is our capacity for self-reflective, rational thought. This assumption has caused research into artificial intelligence and human intelligence more generally to undervalue the role of emotions in generalized problem-solving. The very definition of rationality has, for much of Western history, discounted emotions from its conception. Where emotions are investigated, they are often treated as irrational, immaterial, or illusory aspects of human experience. This has been an unfortunate mistake.
I first joined SIGCAS in 2020, in some ways, by accident. I was up for my ACM renewal, and some of the SIGs that I was a member of didn't seem to focus on the issues that I had started to become more interested in. The lab that I was leading at the time started to work on research that was focusing more on issues in communities where computing could provide solutions to some of these issues. As I scrolled through the list of available SIGs, I came across SIGCAS and read the description, and it seemed like a perfect fit: "The ACM Special Interest Group on Computers and Society brings together computer professionals, specialists in other fields, and the public at large to address concerns and raise awareness about the ethical and societal impact of computers." The work our lab was doing was focused on societal impact, and I was interested to join a community that had similar interests. This seemed like a perfect fit, so I joined.
AI psychosis was a measured risk of capitalist technology. The cases of AI psychosis continue to rise and increasingly appear in news feeds. In 2024, a Florida teen committed suicide after prolonged conversations with a chatbot [1]. In 2025, a similar incident involving a California teen was reported [2]. However, it is not just a problem among youth. A Toronto man was reportedly convinced by a chatbot that he had discovered a revolutionary mathematical framework with world-altering implications [3]. These are not isolated incidents; data suggests that roughly half a million users each week display signs of psychosis or mania related to AI chatbots [4]. Sure, at some level, this is a result of the modern-day mental health crisis. However, I also want to highlight that AI psychosis is a measured risk for technology companies, encouraged by the design of large language models and their marketing.
The promise of artificial intelligence as a developmental accelerator for Global Majority countries has captured considerable attention in policy circles, with proponents suggesting these technologies could help nations "fast-track" progress toward the Sustainable Development Goals. Yet this optimistic narrative obscures a more complex reality where AI's transformative potential intersects with significant risks already manifesting globally, with notable impacts in Global Majority countries. From exploitative labor practices in data annotation to algorithmic bias that deepens existing inequalities, communities worldwide are confronting the uneven distribution of AI's benefits and harms.
Over the past decade, the technology industry has promoted the integration of artificial intelligence (AI) across a wide range of economic sectors. From early efforts focused on machine learning-based applications in predictive governance and healthcare, we now witness the adoption of generative AI in media, education, business, the arts, and various other industries. As a result, the discourse around the social implications of this technology has evolved considerably, sparking critical debates among policymakers, technologists, scholars, grassroots organizers, artists, and other stakeholders about ethical and legal approaches to its advancement.
In recent years, researchers in human-centered computing and adjacent fields have increasingly found themselves encouraged to highlight "real-world impacts" in their work; by funders, by their departments, and by publication venues. But given low incentives to do so in practice, and especially compared to publication and other academic-focused requirements, most academics tend to spend time and effort within their own scholarly ecosystems, publishing at key venues and presenting to familiar faces. On the one hand, this produces deep, rigorous scholarship and a sense of professional community. On the other hand, the research itself also stays within the boundaries of the immediate academic community.
Large language models (LLMs) are becoming embedded in systems that shape daily life—from content moderation and search results to educational tools and customer service chatbots. Yet as these technologies cross geographical and cultural boundaries, they carry with them a troubling pattern of systematic underrepresentation and erasure of marginalized communities.
The use and advancement of generative AI has exploded in the past few years. This has resulted in large tech companies seeking alternative approaches we would not have previously considered to supply the power-hungry processes that require training these advanced models. These approaches include not only placing data centers strategically near power plants, but also creating their own nuclear reactors. Quantifying the power consumption of these services is difficult, and skeptics of the value of generative AI could argue that quantification is intentionally difficult. One analysis compared the generation of an image to powering a smartphone [3]. Another analysis compared generating 100 words consumes up to 3 bottles of water [6]. There have already been warnings that energy consumption from data centers, AI, and cryptocurrency could double in just four years [1]. While this new demand is affecting some of big tech's climate goals, what is clear is that they are also looking for a competitive edge in obtaining power at a lower cost and are willing to take approaches we would not have previously considered.
Quantum Computing (QC) represents a class of technologies that harness fundamental principles of quantum mechanics, particularly superposition and entanglement, to process, transmit, and secure information in ways unattainable by classical systems [1]. Positioned at the core of Quantum Information and Communication Technologies (QICT) [2], QC is central to the ongoing Second Quantum Revolution, driving innovation infrastructures and redefining capabilities in computation, networking, and secure information exchange at fundamental levels [3]. Unlike the first quantum revolution, which focused on physics and laboratory experiments, today's Quantum technology (QT) applications are inherently digital, dependent on and deeply integrated within the digital stack, relying on cloud platforms, high-performance computing, advanced telecommunications, and potentially converging with other emerging digital technologies (EDT) such as artificial intelligence (AI), blockchain, big data, the Internet of Things (IoT) [4], [5], offering new paradigms for the digital economy and society [6]. While there remains considerable uncertainty regarding their practical, scalable applications, the disruptive potential of QC is increasingly recognized [7] with profound ethical, legal, social, and policy implications [8] and a need for practical approaches to their governance [9]. QC is not a standalone technology, but operates as a complex innovation ecosystem where research, policy, markets, and ethics co-evolve [10].
The SIGCAS Works In Progress program provides an opportunity for researchers to present their work and engage in discussions with the SIGCAS community. From my experience as both a presenter, attendee, and now host, I am not only amazed by the critical work that the community is engaged in, but have also received valuable feedback in my own research as well as learned of new research areas.
Generative AI is reshaping social media by automating content creation, personalization, and distribution. While it enhances accessibility and engagement, it also raises concerns around misinformation, bias, and reduced human oversight. A case study using a Twilio-powered WhatsApp bot for NASA's Astronomy Picture of the Day highlights both the potential and the limitations of AI-generated captions. Balancing automation with human judgment is essential to ensure ethical, inclusive, and trustworthy digital ecosystems.
Recently, the rise of ChatGPT has attracted enormous attentions around the world. This breakthrough AI capability, represented by ChatGPT, signifies that AI may have reached a tipping point for large-scale commercialization, and has opened the doors of many monetization opportunities. For instance, a recent report indicates the potential of ChatGPT revolutionizing the search engine business [1]. In addition, Microsoft already plans to integrate ChatGPT technologies into its search engine Bing as well as Office 365 products.
Machine learning (ML) models are increasingly being used to aid decision-making in high-risk applications. However, these models can perpetuate biases present in their training data or the systems in which they are integrated. When unaddressed, these biases can lead to harmful outcomes, such as misdiagnoses in healthcare [11], wrongful denials of loan applications [9], and over-policing of minority communities [2, 4]. Consequently, the fair ML community is dedicated to developing algorithms that minimize the influence of data and model bias.
In this series we take a significant contribution to the visual representation of sustainability, and probe it for insights for computing. In a post-structuralist approach, we present this as a dialogue.
Quantum computing has tremendous potential to change the world by solving many previously unsolvable problems. However, with this tremendous computational power comes threats to our existing technologies safeguarding the world's communication channels and data storage. Specifically, it threatens our standardized and widely deployed cryptographic systems that are in use today. These existing cryptosystems are based on mathematical techniques that are difficult (essentially infeasible) for a classical computer to solve. Quantum computing presents a threat since many previously infeasible problems are likely to become feasible or even easy to solve by a quantum computer. This paper briefly reviews quantum computing and its properties before studying related work into postquantum cryptography. The eventuality of quantum computing is discussed based on readily available research and public information, in addition to expert opinion, which then provides insight into the eventuality of postquantum cryptography and the validity of action, or inaction, around this research. Further topics focus on the standardization of postquantum cryptosystems, and future research trends and opportunities in the field of postquantum cryptography.
The position postulated in this Parting Opinion is quite simple: the answer to the question posed in the title is a resounding NO for many people; no matter which of the categories you're in. Hopefully, you're curious to know why I believe this. If you think I'm crazy or that it simply doesn't apply to you, please read on and submit your counter argument to the next issue of Computers and Society. My opinion derives tangentially from my recent work on the CS2023 Steering Committee and pondering, what's next? , with respect to computers and society.
