Meeting Ethical and Economic Challenges: Building Trust and Learning About Food Demand
Year 2 Research Project

Meeting Ethical and Economic Challenges: Building Trust and Learning About Food Demand

Description

SIGNIFICANCE

We propose these actions to support AIFS in achieving its mission by establishing a meaningful and viable ethical foundation that will support our researchers in carrying out their work, and support trust among those who will ultimately use AIFS technologies.

GOALS

1. Collaborative Culture-Building

2. Understanding Stakeholder Perspectives

3. Continuous Learning, Knowledge Sharing, and Identity Development

4. Negotiation Support

5. Planning for Development of Trust-Building Tools

Team

Portrait of Aaron Smith

Aaron Smith

Principal Investigator

Portrait of Mark Yarborough

Mark Yarborough

Co Principal Investigator

Publications

Journal Article · Precision Agriculture · 2023

Who Is Responsible for ‘Responsible AI’?: Navigating Challenges to Build Trust in AI Agriculture and Food System Technology

Alexander, Carrie S, Mark Yarborough, and Aaron Smith

Paper page for Who is responsible for ‘Responsible AI’?: Navigating challenges to build trust in AI agriculture and food system technologyProject for Who is responsible for ‘Responsible AI’?: Navigating challenges to build trust in AI agriculture and food system technologyDOI 10.1007/s11119-023-10063-3

Journal Article · Frontiers in Artificial Intelligence · 2023

Safer Not to Know? Shaping Liability Law and Policy to Incentivize Adoption of Predictive AI Technologies in the Food System

Alexander, Carrie S, Aaron Smith, and Renata Ivanek

Paper page for Safer not to know? Shaping liability law and policy to incentivize adoption of predictive AI technologies in the food systemPDF of Safer not to know? Shaping liability law and policy to incentivize adoption of predictive AI technologies in the food systemProject for Safer not to know? Shaping liability law and policy to incentivize adoption of predictive AI technologies in the food systemDOI 10.3389/frai.2023.1298604