In order to train, test, and evaluate nudity detection models, machine learning researchers typically rely on nude images scraped from the Internet. Our research finds that this content is collected and, in some cases, subsequently distributed by researchers without consent, leading to potential misuse and exacerbating harm against the subjects depicted. This position paper argues that the distribution of nonconsensually collected nude images by researchers perpetuates imagebased sexual abuse and that the machine learning community should stop the nonconsensual use of nude images in research. To characterize the scope and nature of this problem, we conducted a systematic review of papers published in computing venues that collect and use nude images. Our results paint a grim reality: norms around the usage of nude images are sparse, leading to a litany of problematic practices like distributing and publishing nude images with uncensored faces, and intentionally collecting and sharing abusive content. We conclude with a call-to-action for publishing venues and a vision for research in nudity detection that balances user agency with concrete research objectives.

Moral judgment is integral to large language models' (LLMs) social reasoning. As multi-agent systems gain prominence, it becomes crucial to understand how LLMs function when collaborating compared to operating as individual agents. In human moral judgment, group deliberation leads to a Utilitarian Boost: a tendency to endorse norm violations that inflict harm but maximize benefits for the greatest number of people. We study whether a similar dynamic emerges in multi-agent LLM systems. We test six models on well-established sets of moral dilemmas across two conditions: (1) Solo, where models reason independently, and (2) Group, where they engage in multi-turn discussions in pairs or triads.

In AI research and practice, rigor remains largely understood in terms of methodological rigor--such as whether mathematical, statistical, or computational methods are correctly applied. We argue that this narrow conception of rigor has contributed to the concerns raised by the responsible AI community, including overblown claims about the capabilities of AI systems. Our position is that a broader conception of what rigorous AI research and practice should entail is needed. We believe such a conception--in addition to a more expansive understanding of (1) methodological rigor--should include aspects related to (2) what background knowledge informs what to work on (epistemic rigor); (3) how disciplinary, community, or personal norms, standards, or beliefs influence the work (normative rigor); (4) how clearly articulated the theoretical constructs under use are (conceptual rigor); (5) what is reported and how (reporting rigor); and (6) how well-supported the inferences from existing evidence are (interpretative rigor). In doing so, we also provide useful language and a framework for much-needed dialogue about the AI community's work by researchers, policymakers, journalists, and other stakeholders.

Plug-in panels are getting popular--how do we make sure they're safe? Dozens of US states are considering legislation to allow people to install plug-in solar systems, often called balcony solar. These small arrays require little to no setup and could help cut emissions and power bills. Balcony solar is already popular in Europe, and proponents say that the systems could make solar power more accessible for more people in the US, including renters. As popularity rises, though, some experts caution that there are safety concerns with how balcony solar would work with existing electrical equipment in homes. Let's talk about what balcony solar is, why it's unique, and how new testing requirements could affect our progress toward deploying the technology in the US.

We report the details of hyperparameters in our experiments.

Neural Information Processing Systems http://nips.cc/

Stimulus-drivenbrain-computer interfaces (BCIs), such astheP300 speller,rely onusing asequence ofsensory stimuli toelicit specific neural responses ascontrol signals, while a user attends to relevant target stimuli that occur within the sequence. In current BCIs, the stimulus presentation schedule is typically generated in a pseudo-random fashion. Given the non-stationarity of brain electrical signals, a better strategy could be to adapt the stimulus presentation schedule in real-time by selecting the optimal stimuli that will maximize the signal-to-noise ratios of the elicited neural responses and provide the most information about the user's intent based on the uncertainties of the data being measured. However, the high-dimensional stimulus space limits the development of algorithms with tractable solutions for optimized stimulus selection to allow for real-time decision-making within the stringent time requirements of BCI processing.

Is there anything afuture user could do to mitigate theseundesirableharms? Although ConfLab's long-term vision is towards developing technology to assist individuals in navigating social interactions, the data could also affect a community in unintended ways: for instance, cause worsened social satisfaction, alackofagency,stereotype newcomers andveterans, or benefit only those members of the community who make use of resulting applications at the expense of the rest. More nefarious uses involve exploiting the data for developing methods that harmfully surveilorprofile people.

Ourbenchmarks showcase some of the open research tasks related to in-the-wild privacy-preserving social data analysis: keypoints detection from overhead camera views, skeleton-based no-audiospeakerdetection,andF-formationdetection.