Because Improving the Lives of Children is Complicated

For empirical researchers in the social and behavioral sciences who focus on children, adolescents, and young adults, high-quality survey data are an essential ingredient for studying important scientific and policy research questions. Such data are a public good and foundational infrastructure for the social and behavioral sciences. They are the equivalent of the Hubble Telescope for researchers across all career stages—but especially for new and early-stage investigators. Survey data are typically offered to the research community as a free and shared resource that can answer an untold number of questions. Recent budget cutbacks, however, threaten the future of these essential data.

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Because It Can Contribute to AI that Benefits Society

Artificial Intelligence – “AI” – continues to be the subject of hot debate around the world as governments seek ways to regulate it to protect the public, and developers continue to push towards AI with more human-like capabilities. What’s at stake depends on who you listen to: some extoll the benefits of AI to “transform” the way we live and work, downplaying the potential for negative impacts on society, while others warn of an existential threat to humanity.  Most perspectives land somewhere in between. We see AI, like other technological advances before it, as an exciting tool with tremendous potential. As such, it is not inherently helpful or harmful: its impacts depend on how it is used. Now is the perfect time for the thoughtful and extensive integration of social science evidence and expertise into AI development, deployment, implementation, and use so that AI can be optimally, positively effective while minimizing risks of harm to society.

AI has existed in various forms for decades and until recently, was developed under tightly constrained parameters to do specific tasks. However, the 2022 launch of easy-to-access Large Language Model (LLM) tools such as ChatGPT, which input massive amounts of data and generate responses to questions in conversational language, had leaders across many sectors – from education to business – scrambling to set guidelines and parameters for AI’s use in their domains. Indeed, AI and other technologies are not typically implemented in isolation but in systems. Social science approaches can help us understand and address these technologies’ reach, implications, and impact within these “AI systems.”

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Because It Can Explain How the Next Technological Revolution Impacts Our Lives and the Communities We Care About

For those reading nearly any media outlet during 2023, it is hard to miss the fact that Artificial Intelligence (AI)–in all its varied manifestations–is a regularized component of public discussion and debate. The celebrated and feared emergence of ChatGPT in late 2022, the Writers Guild of America strike and the concerns that writing would cease to be a human endeavor, and the recent firing and subsequent rehiring of OpenAI CEO Sam Altman clearly illustrate that AI is having a moment. So much so that prediction and forecasting site Metaculus is tracking to see if Time's person of the year for 2023 will be AI. Honestly, it is hard to argue against this idea. But before we get too far down a path of contending and arguing that an AI revolution will fundamentally transform the world in which we live, perhaps we should consider what social science can tell us and has told us about the longitudinal impacts of technological change on society. The interdisciplinary world of Science & Technology Studies can provide a window into how new and emerging technologies impact how we live on the planet.

Science & Technology Studies as a field of inquiry that emerged in the middle of the twentieth century, bringing together those interested and invested in better understanding the ways science and technology impacted society. Situated within broader discussions about science "and" society, and science "for" society, early work–exemplified by Thomas Kuhn's The Structure of Scientific Revolutions–aimed to reveal the deep social roots of scientific discovery and technological innovation.

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Because Engineering Is Intended to Benefit Society

Why introduce the social sciences into engineering education and practice? The intent of engineers is to “do good” – to improve the quality and security of life. Unfortunately, it doesn’t always work out this way, often because of a lack of appreciation by engineers of how technology affects and is used by people. For example, a new technology might promise and deliver great benefit to portions of society but harm certain groups. We call such results “unintended consequences.”

Examples of unintended consequences abound. Social media connect people and cultures, but they are also weaponized to marginalize individuals based on race, culture, and personal attributes and to spread false information. Remote learning and teleworking, essential in the age of Covid-19, have widened the gulf between the haves and have-nots. The potential benefits of artificial intelligence (AI) to society will be blunted if human biases find their way into coding. In designing urban infrastructure, economic and technical criteria should be balanced against social impacts, such as the isolation of underprivileged neighborhoods after the construction of a new highway or transit system. Good engineering cannot be separated from social awareness and deliberate consideration.

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Because It Makes Computing Work for People

Two years ago, the leadership of the House Science, Space and Technology Committee looked to our organization, the Computing Research Association, to endorse an approach to reauthorize funding at a number of key Federal science agencies. The proposed legislation would provide increases for computing research funding at the National Science Foundation while keeping the overall agency budget essentially flat by bolstering computing — along with mathematics, physics, biology, and engineering — at the expense of the social, behavioral, and economic sciences (and the geosciences). The committee Chair hoped that CRA, which represents nearly 200 academic computing departments and industrial research labs — including computing research labs at IBM, Google, Facebook, and Microsoft — would support the approach, given the direct and indirect benefits increased investment in computing research at NSF would have to our member institutions.

The science advocacy community in Washington, DC, is comprised of many organizations like CRA, each representing some typically discipline-specific slice of the academic and research community, but bound by the shared goal of ensuring that policymakers understand the importance of the Federal investment in research and the value of peer and merit review in setting priorities. As such, we are typically averse to efforts that pit the disciplines against each other, like the one proposed. But that wasn’t the only important reason for us to oppose the proposal. What primarily motivated our opposition was our strong belief that cutting social, behavioral, and economic science investments would also do great damage to computing research.

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