| Website | http://jennwv.com |
The FATE group at Microsoft Research NYC is now accepting applications for 2025 interns. 🥳🎉
For full consideration, apply by December 18 and include a cover letter.
Interested in AI evaluation? Apply for the STAC internship too!
The FATE group at Microsoft Research NYC is looking for interns and postdocs!
Relevant research themes include:
- Computational, statistical, & sociotechnical approaches to fairness assessment
- Human-centered AI transparency
- Institutional, organizational, & economic challenges of AI development, deployment, and use
- AI law and policy
- Responsible AI in practice
Interns (apply soon, we're reviewing applications now!): https://jobs.careers.microsoft.com/global/en/job/1661365/Research-Intern---FATE%2C-NYC-(Fairness%2C-Accountability%2C-Transparency%2C-and-Ethics-in-AI)
The FATE group at Microsoft Research NYC is looking for interns and postdocs!
Relevant research themes include:
- Computational, statistical, & sociotechnical approaches to fairness assessment
- Human-centered AI transparency
- Institutional, organizational, & economic challenges of AI development, deployment, and use
- AI law and policy
- Responsible AI in practice
Interns (apply soon, we're reviewing applications now!): https://jobs.careers.microsoft.com/global/en/job/1661365/Research-Intern---FATE%2C-NYC-(Fairness%2C-Accountability%2C-Transparency%2C-and-Ethics-in-AI)
It's that time of year again! The FATE group at Microsoft Research NYC is accepting applications for 2024 interns. 🥳🎉
All internships will be in person again. We'll begin reviewing applications in early December. Cover letter required; see the posting for more details.
Has anyone successfully run experiments on MTurk in the past 1 or 2 years? I find it impossible to collect usable data even with qualifications, captchas, extensive attention/location checks, etc.
What works these days???
(Would switch to Prolific if I could, but it's not an option.)
Longer write-up on the #NeurIPS2021 consistency experiment now up on arxiv: https://arxiv.org/abs/2306.03262
Some takeaways:
- There was noise in 2014; there's noise now. Conference growth hasn't changed this much.
- Spotlights/orals are especially noisy. Being more selective would make decisions more arbitrary.
- The amount of noise in which papers get flagged for ethics review is also striking.
We present the NeurIPS 2021 consistency experiment, a larger-scale variant of the 2014 NeurIPS experiment in which 10% of conference submissions were reviewed by two independent committees to quantify the randomness in the review process. We observe that the two committees disagree on their accept/reject recommendations for 23% of the papers and that, consistent with the results from 2014, approximately half of the list of accepted papers would change if the review process were randomly rerun. Our analysis suggests that making the conference more selective would increase the arbitrariness of the process. Taken together with previous research, our results highlight the inherent difficulty of objectively measuring the quality of research, and suggest that authors should not be excessively discouraged by rejected work.
We synthesize lessons from HCI and RAI/FATE research—specifically, around taking a goal-oriented perspective, supporting appropriate levels of trust, the importance of mental models, the importance of how we communicate information, and the need to support control.
Finally, we lay out 4 common approaches to transparency—model reporting, publishing evaluation results, providing explanations, and communicating uncertainty—and open questions around how these approaches might be applied to LLMs.
We argue for developing and designing approaches to transparency by considering stakeholder needs, novel types of LLM-infused applications, and new usage patterns around LLMs—all while building on lessons learned from human-centered research.
We reflect on challenges that arise providing transparency for LLMs: complex model capabilities, massive opaque architectures, proprietary tech, complex applications, diverse stakeholders, rapidly evolving public perception, and pressure to move fast.
New paper to share! 📣 @qveraliao and I lay out our vision of a human-centered research roadmap for “AI Transparency in the Age of LLMs.”
https://arxiv.org/abs/2306.01941
There's lots of talk about the responsible development and deployment of LLMs, but transparency (including model reporting, explanations, uncertainty communication, and more) is often missing from this discourse.
We hope this framing will spark more discussion and research.
Attempting my first mastodon thread below... 🧵
The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. However, a central pillar of responsible AI -- transparency -- is largely missing from the current discourse around LLMs. It is paramount to pursue new approaches to provide transparency for LLMs, and years of research at the intersection of AI and human-computer interaction (HCI) highlight that we must do so with a human-centered perspective: Transparency is fundamentally about supporting appropriate human understanding, and this understanding is sought by different stakeholders with different goals in different contexts. In this new era of LLMs, we must develop and design approaches to transparency by considering the needs of stakeholders in the emerging LLM ecosystem, the novel types of LLM-infused applications being built, and the new usage patterns and challenges around LLMs, all while building on lessons learned about how people process, interact with, and make use of information. We reflect on the unique challenges that arise in providing transparency for LLMs, along with lessons learned from HCI and responsible AI research that has taken a human-centered perspective on AI transparency. We then lay out four common approaches that the community has taken to achieve transparency -- model reporting, publishing evaluation results, providing explanations, and communicating uncertainty -- and call out open questions around how these approaches may or may not be applied to LLMs. We hope this provides a starting point for discussion and a useful roadmap for future research.