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Farsight: Fostering Responsible AI Awareness During AI Application Prototyping (2402.15350v2)

Published 23 Feb 2024 in cs.HC, cs.AI, cs.CY, and cs.LG

Abstract: Prompt-based interfaces for 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: https://PAIR-code.github.io/farsight.

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Farsight: Enhancing Responsible AI Practice in LLM Application Prototyping

Introduction to Farsight

Recent advancements in AI have put powerful tools like LLMs into the hands of a broader spectrum of developers, including those without specialized AI training. However, this democratization comes with the increased responsibility of identifying and mitigating potential harms that these technologies could cause. This paper introduces Farsight, an innovative in situ interactive tool designed to help AI prototypers envision potential harms associated with their LLM-powered applications during the early stages of prototyping.

Key Features of Farsight

Farsight incorporates several novel aspects that distinguish it from existing resources for responsible AI:

  • In Situ Integration: Farsight is designed to be a seamless part of AI prototyping environments, such as Google AI Studio and Jupyter Notebook. This design choice ensures that considerations of potential harms are naturally incorporated into the workflow of AI application development.
  • Interactive Harm Envisioning: Utilizing embedding similarities, Farsight dynamically suggests news articles about relevant AI incidents, thus providing contextually relevant examples of potential harms. It further leverages the capabilities of LLMs to generate and allow users to interactively explore potential use cases, stakeholders, and harms.
  • User Engagement and Agency: By enabling users to edit and augment LLM-generated content, Farsight ensures that AI prototypers engage critically with the tool, encouraging a deeper consideration of potential harms beyond the immediate functionalities of their application.
  • Open-source Implementation: To facilitate widespread adoption and future enhancements, Farsight is made available as an open-source tool. Its implementation as a collection of web components ensures compatibility with current web-based AI development environments.

Evaluation and Insights

The user paper involving 42 AI prototypers demonstrated Farsight's effectiveness in fostering responsible AI awareness. Key findings include:

  • Increased Harm Awareness: Participants who used Farsight were significantly better at identifying potential harms independently after the intervention, indicating that Farsight effectively enhances users' harm envisioning capabilities.
  • Shift in Focus: Qualitative feedback suggests that Farsight shifts users' attention from the AI model to its end-users, promoting a broader consideration of indirect stakeholders and cascading harms.
  • High Usability and Engagement: Farsight was rated favorably in terms of usability and usefulness compared to other resources. Participants found it to be a useful addition to their prototyping workflows, appreciating its seamless integration and the agency it provides.

Future Directions

While Farsight marks a significant step toward integrating responsible AI practices into the AI development lifecycle, it also opens avenues for further research. Future work could explore methods for incorporating actionable harm mitigation strategies within Farsight, addressing the current limitation of focusing primarily on harm identification. Such enhancements could offer AI prototypers not only the means to envision potential harms but also the tools to counteract them effectively from the early stages of AI application development.

Farsight's introduction into the AI prototyping process represents a substantive contribution toward fostering a culture of responsibility among AI developers. As AI technologies continue to evolve, tools like Farsight will be crucial in ensuring that these advancements proceed with due regard for their societal impact.

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Authors (5)
  1. Zijie J. Wang (39 papers)
  2. Chinmay Kulkarni (15 papers)
  3. Lauren Wilcox (10 papers)
  4. Michael Terry (25 papers)
  5. Michael Madaio (15 papers)
Citations (19)
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GitHub

  1. GitHub - PAIR-code/farsight: In situ interactive widgets for responsible AI 🌱 (25 stars)