Articles that discuss the use of LLMs in science.
| Title | Type of Resource | Description of Resource | Link to Resource | Open Science | Use of LLM | Research Discipline(s) |
|---|---|---|---|---|---|---|
| We Have No Satisfactory Social Epistemology of AI-Based Science | Discussion Article | In the social epistemology of scientific knowledge, it is largely accepted that relationships of trust, not just reliance, are necessary in contemporary collaborative science characterised by relationships of opaque epistemic dependence. Such relationships of trust are taken to be possible only between agents who can be held accountable for their actions. But today, knowledge production in many fields makes use of AI applications that are epistemically opaque in an essential manner. This creates a problem for the social epistemology of scientific knowledge, as scientists are now epistemically dependent on AI applications that are not agents, and therefore not appropriate candidates for trust. | No Satisfactory Social Epistemology | Open Source | Other | Philosophy |
| Science Based on Artificial Intelligence Need not Pose a Social Epistemological Problem | Discussion Article | It has been argued that our currently most satisfactory social epistemology of science can’t account for science that is based on artificial intelligence (AI) because this social epistemology requires trust between scientists that can take full responsibility for the research tools they use, and scientists can’t take full responsibility for the AI tools they use since these systems are epistemically opaque. I think this argument overlooks that much AI-based science can be done without opaque models, and that agents can take full responsibility for the systems they use even if these systems are opaque. Requiring that an agent fully understand how a system works is an untenably strong condition for that agent to take full responsibility for the system and risks absolving AI developers from responsibility for their products. AI-based science need not create trust-related social epistemological problems if we keep in mind that what makes both individual scientists and their use of AI systems trustworthy isn’t full transparency of the internal processing but their adherence to social and institutional norms that ensure that scientific claims can be trusted. | Need not Pose a Social Epistemological Problem | Preprint | Other | Philosophy |
| Living with Uncertainty: Full Transparency of AI isn’t Needed for Epistemic Trust in AI-based Science | Discussion Article | Can AI developers be held epistemically responsible for the processing of their AI systems when these systems are epistemically opaque? And can explainable AI (XAI) provide public justificatory reasons for opaque AI systems’ outputs? Koskinen (2024) gives negative answers to both questions. Here, I respond to her and argue for affirmative answers. More generally, I suggest that when considering people’s uncertainty about the factors causally determining an opaque AI’s output, it might be worth keeping in mind that a degree of uncertainty about conclusions is inevitable even in entirely human-based empirical science because in induction there’s always a risk of getting it wrong. Keeping this in mind may help appreciate that requiring full transparency from AI systems before epistemically trusting their outputs might be unusually (and potentially overly) demanding. | Living with Uncertainty | Preprint | Other | Philosophy |
| The illusion of artificial inclusion | Discussion Article | Human participants play a central role in the development of modern artificial intelligence (AI) technology, in psychological science, and in user research. Recent advances in generative AI have attracted growing interest to the possibility of replacing human participants in these domains with AI surrogates. We survey several such "substitution proposals" to better understand the arguments for and against substituting human participants with modern generative AI. Our scoping review indicates that the recent wave of these proposals is motivated by goals such as reducing the costs of research and development work and increasing the diversity of collected data. However, these proposals ignore and ultimately conflict with foundational values of work with human participants: representation, inclusion, and understanding. This paper critically examines the principles and goals underlying human participation to help chart out paths for future work that truly centers and empowers participants. | artificial inclusion | Preprint | Data Collection, Data Generation | Computer Science |
| Generative AI Can Supercharge Your Academic Research | Discussion Article, Use Case Example | Conducting relevant scholarly research can be a struggle. Educators must employ innovative research methods, carefully analyze complex data, and then master the art of writing clearly, all while keeping the interest of a broad audience in mind. Generative AI is revolutionizing this sometimes tedious aspect of academia by providing sophisticated tools to help educators navigate and elevate their research. But there are concerns, too. AI’s capabilities are rapidly expanding into areas that were once considered exclusive to humans, like creativity and ingenuity. This could lead to improved productivity, but it also raises questions about originality, data manipulation, and credibility in research. With a simple prompt, AI can easily generate falsified datasets, mimic others’ research, and avoid plagiarism detection. [4 how to tutorials follow] | Using LLM in Research Process | Open Source | Other | Other |
| Automated Social Science: A Structural Causal Model-Based Approach | Discussion Article | We present an approach for automatically generating and testing, in silico, social scientific hypotheses. This automation is made possible by recent advances in large language models (LLM), but the key feature of the approach is the use of structural causal models. Structural causal models provide a language to state hypotheses, a blueprint for constructing LLM-based agents, an experimental design, and a plan for data analysis. The fitted structural causal model becomes an object available for prediction or the planning of follow-on experiments. We demonstrate the approach with several scenarios: a negotiation, a bail hearing, a job interview, and an auction. In each case, causal relationships are proposed and tested, finding evidence for some and not others. In the auction experiment, we show that the in silico simulation results closely match the predictions of auction theory, but elicited predictions of the clearing prices from an LLM are inaccurate. However, the LLM’s predictions are dramatically improved if the model can condition on the fitted structural causal model. When given a proposed structural causal model for one of the scenarios, the LLM is good at predicting the signs of estimated effects, but it cannot reliably predict the magnitudes of those effects. This suggests that social simulations give the model insight not available purely through direct elicitation. In short, the LLM knows more than it can (immediately) tell. | Automated Social Science | Open Source | Other | Other |
| ChatGPT Undermines Human Reflexivity, Scientific Responsibility and Responsible Management Research | Discussion Article | We herewith certify that this essay represents original and independent scholarship. That is, generative AI was not used in the idea-generating phase of this essay, nor was it used to assist the writing or editing of this essay (with the exception of serving the purpose of a ‘bad’ example). We observe with great concern that many journal publishers – unlike Science – become complicit in undermining the meaning of the term ‘original scholarship’ by allowing the use of generative AI in the research process, while actual enforceability of relevant policies is low. Eventually, we need to be mindful of the deskilling of the (academic) mental sphere, while corporate influences on what constitutes knowledge is set to grow. | Underminds | Open Source | Other | Business |
| The future of research in an artificial intelligence-driven world. [multiple essays] | Discussion Article | Current and future developments in artificial intelligence (AI) systems have the capacity to revolutionize the research process for better or worse. On the one hand, AI systems can serve as collaborators as they help streamline and conduct our research. On the other hand, such systems can also become our adversaries when they impoverish our ability to learn as theorists, or when they lead us astray through inaccurate, biased, or fake information. No matter which angle is considered, and whether we like it or not, AI systems are here to stay. In this curated discussion, we raise questions about human centrality and agency in the research process, and about the multiple philosophical and practical challenges we are facing now and ones we will face in the future. [multiple essays] | Future of Research | Preprint | Other | Business, Other |
| Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other large language models in scholarly peer review | Discussion Article | The emergence of systems based on large language models (LLMs) such as OpenAI’s ChatGPT has created a range of discussions in scholarly circles. Since LLMs generate grammatically correct and mostly relevant (yet sometimes outright wrong, irrelevant or biased) outputs in response to provided prompts, using them in various writing tasks including writing peer review reports could result in improved productivity. Given the significance of peer reviews in the existing scholarly publication landscape, exploring challenges and opportunities of using LLMs in peer review seems urgent. After the generation of the first scholarly outputs with LLMs, we anticipate that peer review reports too would be generated with the help of these systems. However, there are currently no guidelines on how these systems should be used in review tasks. | LLMs in Peer Review | Open Source | Science Communication, Other | Other |
| ChatGPT in Thematic Analysis: Can AI become a research assistant in qualitative research? | Discussion Article | The release of ChatGPT in November 2022 heralded a new era in various professional fields, yet its application in qualitative data analysis (QDA) remains underdeveloped. This article presents an experiment involving applying ChatGPT (Model GPT-4) to thematic analysis. By employing an adapted version of King et al.’s (2018) Template Analysis framework, this article aims to assess how ChatGPT can help with QDA in a full analytical process of a sample dataset provided by Lumivero. My experiment includes applying ChatGPT in four stages: data familiarization; preliminary coding and initial template formation; clustering and template modification and finalization; and theme development. Findings reveal GPT-4’s capacity in efficiency and speed in grasping the data and generating codes, subcodes, clusters, and themes, alongside its learning and adapting capabilities. However, the current version of the model has limitations in terms of effectively handling detailed analysis of large databases and producing consistent results, as well as the need to move across workspaces and the lack of relevant training data for QDA purposes. | ChatGPT in Thematic Analysis | Preprint | Data Analysis | Sociology |