AI Ethics and Governance in Practice: An Introduction

Conceitos Básicos

Facilitating responsible AI development through ethical considerations.

Resumo

Introduction to AI Ethics and Governance in Practice Programme.
Acknowledgements for workbook contributors.
Overview of the Workbook Series, its audience, and structure.
Detailed exploration of key concepts in AI and ML.
Explanation of technical components like data, models, and machine learning.
Breakdown of the AI/ML project lifecycle stages with a focus on sociotechnical aspects.

Estatísticas

This work was supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/W006022/1.
The workbook series covers how to implement key components of the PBG Framework.

Citações

"AI-supported healthcare has helped clinicians to spot early signs of illness and diagnose diseases." - Content Section 11
"By bringing together diverse groups from around the globe through real-time speech-to-speech translation, AI systems are enabling humans to successfully confront an ever-widening range of societal challenges." - Content Section 11

Principais Insights Extraídos De

AI Ethics and Governance in Practice

by David Leslie... às arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15403.pdf

Perguntas Mais Profundas

How can stakeholders ensure that ethical considerations are integrated into every stage of AI project delivery?

Stakeholders can ensure that ethical considerations are integrated into every stage of AI project delivery by establishing clear guidelines and frameworks for ethical AI development. This includes conducting thorough impact assessments to identify potential risks and biases, involving diverse perspectives in decision-making processes, and prioritizing transparency and accountability throughout the project lifecycle. Additionally, stakeholders should continuously evaluate the social implications of their AI systems, engage with relevant regulatory bodies and experts in ethics, and provide ongoing training on ethical principles for all team members involved in the project.

What potential biases or risks might arise from using unsupervised machine learning models in public sector applications?

Using unsupervised machine learning models in public sector applications may lead to several biases and risks. One common risk is the amplification of existing societal biases present in the data used for training these models. Without explicit labels or supervision, unsupervised ML algorithms may inadvertently reinforce discriminatory patterns or overlook marginalized groups within datasets. Additionally, there is a risk of generating inaccurate or unreliable insights due to the lack of oversight during model training. Unsupervised ML models may also struggle with interpretability issues, making it challenging to understand how decisions are being made without clear labels guiding the learning process.

How can a sociotechnical approach enhance the development and deployment of AI systems beyond technical considerations?

A sociotechnical approach enhances the development and deployment of AI systems by recognizing that technology does not exist in isolation but is deeply intertwined with social structures, human values, norms, and institutions. By incorporating sociotechnical perspectives into AI projects, stakeholders can better address complex ethical dilemmas, consider broader societal impacts beyond technical functionalities alone, foster interdisciplinary collaboration among diverse teams (including ethicists), promote responsible innovation practices aligned with community needs and values while ensuring fairness equity across different user groups.