Prompt-based interfaces for Large Language Models (LLMs) have made
prototyping and building AI-powered applications easier than ever before.
However, identifying potential harms that may arise from AI applications
remains a challenge, particularly during prompt-based prototyping. To address
this, we present Farsight, a novel in situ interactive tool that helps people
identify potential harms from the AI applications they are prototyping. Based
on a user's prompt, Farsight highlights news articles about relevant AI
incidents and allows users to explore and edit LLM-generated use cases,
stakeholders, and harms. We report design insights from a co-design study with
10 AI prototypers and findings from a user study with 42 AI prototypers. After
using Farsight, AI prototypers in our user study are better able to
independently identify potential harms associated with a prompt and find our
tool more useful and usable than existing resources. Their qualitative feedback
also highlights that Farsight encourages them to focus on end-users and think
beyond immediate harms. We discuss these findings and reflect on their
implications for designing AI prototyping experiences that meaningfully engage
with AI harms. Farsight is publicly accessible at:
this https URL

利用 Farsight 工具，基于用户的提示信息，我们能更好地识别与此提示相关的潜在危害，同时发现该工具比现有资源更有用和易用，鼓励以终端用户为重心并超越直接伤害进行思考，这些发现对设计有意义的 AI 原型体验具有重要启示。

Farsight：AI 应用原型期间培养负责任的 AI 认知

Farsight: Fostering Responsible AI Awareness During AI Application  Prototyping

Whole-body biometric recognition is an important area of research due to its
vast applications in law enforcement, border security, and surveillance. This
paper presents the end-to-end design, development and evaluation of FarSight,
an innovative software system designed for whole-body (fusion of face, gait and
body shape) biometric recognition. FarSight accepts videos from elevated
platforms and drones as input and outputs a candidate list of identities from a
gallery. The system is designed to address several challenges, including (i)
low-quality imagery, (ii) large yaw and pitch angles, (iii) robust feature
extraction to accommodate large intra-person variabilities and large
inter-person similarities, and (iv) the large domain gap between training and
test sets. FarSight combines the physics of imaging and deep learning models to
enhance image restoration and biometric feature encoding. We test FarSight's
effectiveness using the newly acquired IARPA Biometric Recognition and
Identification at Altitude and Range (BRIAR) dataset. Notably, FarSight
demonstrated a substantial performance increase on the BRIAR dataset, with
gains of +11.82% Rank-20 identification and +11.3% TAR@1% FAR.

FarSight 是一款应用于视频监控的软件系统，可通过整合面部、步态和身体形态等信息，对全身生物识别进行端到端的设计、开发和评估，并使用深度学习模型和影像物理学相结合进行影像修复和生物特征编码，其测试结果较好。