AI Sustainability in Practice: Foundations for Sustainable AI Projects

Khái niệm cốt lõi

Foundations for Sustainable AI Projects and the importance of Stakeholder Engagement.

Tóm tắt

The content delves into the foundations of sustainable AI projects, focusing on the SUM Values and Stakeholder Engagement. It covers key concepts such as bioethics, human rights, and real-world hazards posed by AI/ML technologies. The Stakeholder Engagement Process is detailed, emphasizing the need for ongoing reflection and involvement of stakeholders throughout the project lifecycle.

• Acknowledgements highlight contributors to workbook creation.
• Workbook series origins trace back to UK Government guidance on AI ethics.
• The curriculum includes eight workbooks covering various aspects of responsible AI innovation.
• Intended audience primarily civil servants involved in AI Ethics and Governance Programme.
• Introduction to SUM Values and their application in assessing societal impacts of AI projects.
• Detailed breakdown of Key Concepts including sustainability principles, stakeholder analysis, and engagement objectives.
• Positionality Reflection emphasizes understanding personal biases and perspectives.
• Stakeholder Engagement Process outlined with steps for scoping, analysis, reflection, and methods establishment.
• Importance of ongoing stakeholder involvement throughout project lifecycle.

Thống kê

This workbook was supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/W006022/1.

Trích dẫn

"We believe that governments can reap the benefits of these technologies only if they make considerations of ethics and safety a first priority." - Workbook Introduction
"Stakeholders are individuals or groups that have interests or rights affected by organizational decisions." - Stakeholder Analysis

Thông tin chi tiết chính được chắt lọc từ

AI Sustainability in Practice Part One

by David Leslie... lúc arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14635.pdf

Yêu cầu sâu hơn

How can stakeholders be effectively engaged throughout all stages of an AI project?

Stakeholders can be effectively engaged in AI projects by following a structured approach that involves them from the initial planning phases to the implementation and evaluation stages. Here are some key strategies for effective stakeholder engagement:

Identifying Stakeholders: Begin by identifying all potential stakeholders who may be impacted by or have an impact on the AI project.
Understanding Stakeholder Needs: Conduct thorough analyses to understand the needs, concerns, and expectations of different stakeholder groups.
Clear Communication: Maintain transparent and open communication channels with stakeholders to keep them informed about project progress, decisions, and outcomes.
Inclusive Decision-Making: Involve stakeholders in decision-making processes to ensure their perspectives are considered and incorporated into project plans.
Feedback Mechanisms: Establish feedback mechanisms where stakeholders can provide input, suggestions, and concerns throughout the project lifecycle.
Regular Updates: Provide regular updates on project developments to keep stakeholders engaged and informed about any changes or challenges.
Tailored Engagement Approaches: Customize engagement approaches based on stakeholder preferences, such as workshops, surveys, focus groups, or one-on-one meetings.

What potential risks arise from inadequate stakeholder involvement in sustainable AI projects?

Inadequate stakeholder involvement in sustainable AI projects can lead to several risks that may impact the success and ethical integrity of the initiatives:

Lack of Diverse Perspectives: Without diverse stakeholder inputs, there is a risk of overlooking important considerations related to ethics, social impacts, biases, or unintended consequences.
Limited Acceptance & Adoption: Inadequate involvement may result in low acceptance or resistance from affected communities or end-users due to a lack of understanding or alignment with their needs.
Ethical Blind Spots & Biases: Insufficient engagement increases the likelihood of ethical blind spots within AI systems leading to biased outcomes that harm certain groups unfairly.
Reputational Damage & Trust Issues: Failure to engage stakeholders adequately can damage trust between organizations implementing AI solutions and those impacted by them resulting in reputational harm.

How can positionality reflections enhance ethical decision-making in AI innovation?

Positionality reflections play a crucial role in enhancing ethical decision-making within AI innovation by promoting self-awareness among team members regarding their own biases,
privileges,
and perspectives
that influence their judgments
and actions
in developing
AI technologies
By acknowledging these factors,
individuals involved
in
AI projects become more conscious
of how their backgrounds shape their viewpoints
and interactions with others,
especially those from diverse backgrounds Positionality reflections encourage individuals
to critically assess how power dynamics,
social identities,
and personal experiences affect their approach towards designing,
implementing,and evaluating AIsystems This introspective process helps team members identify potential sources of bias,misinterpretation,and discrimination withintheir work,promoting fairness,equity,and inclusivityinthe developmentofAIinnovations