Skip to content

AI Ethics in Industry: A Research Framework

Oct 2019 · Conference on Technology Ethics · Vol abs/1910.12695 · 27 citations · ⚡ 3 influential · 31 references
Sociology Computer Science

TL;DR

This paper discusses a research framework for implementing AI ethics in industrial settings and presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.

Abstract

Artificial Intelligence (AI) systems exert a growing influence on our society. As they become more ubiquitous, their potential negative impacts also become evident through various real-world incidents. Following such early incidents, academic and public discussion on AI ethics has highlighted the need for implementing ethics in AI system development. However, little currently exists in the way of frameworks for understanding the practical implementation of AI ethics. In this paper, we discuss a research framework for implementing AI ethics in industrial settings. The framework presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.

View source

Similar papers

Aug 2026

Sustainable AI: A Necessity for Industry [In My View]

Sustainable artificial intelligence (AI) has gained increasing prominence in academic, policy, and industry discussions, yet the concept remains underspecified and frequently deployed in ways that obscure the environmental and social harms associated with contemporary AI systems. This article argues that, for industry,...

Aimee van Wynsberghe, Chelsea Haramia · 0 citations
Open access Jul 2026

Is Ethical Artificial Intelligence Possible?

The assessments indicate that the European Union's desire to compete with the United States and China in the global artificial intelligence race creates a context that may make prioritizing ethical principles difficult.

Tolgay Ercan · 0 citations
Aug 2026

Trustworthy AI

This book offers a clear, concise introduction to trustworthy AI, treating AI not just as a technical artifact but as a socio-technical system embedded in human contexts, designed for teaching and learning in computer science, data science, law, policy, business, and related fields.

Andrea Aler Tubella, Virginia Dignum, Marçal Mora-Cantallops et al. · 0 citations
Open access Jul 2026

Artificial Intelligence and Human Values: An Interdisciplinary Exploration

The rapid and profound transformation of society brought about by Artificial Intelligence (AI) raises critical and complex questions concerning its alignment with core human values and ethical principles. This article thoroughly examines the intricate relationship between the development of AI technologies and the ethi...

Malay Kumar Das · 0 citations
Open access Aug 2026

Governing Artificial Intelligence in Scientific Research: The ARCHON Framework — Ethical Requirements Derived from Technical Architecture

This paper proposes the ARCHON framework, a set of ten requirements derived from the technical architecture of AI systems, to facilitate ethical compliance of scientific research for experts and practicioners engaging in roles such as proposal evaluators, project officers, ethics board members, or research administrato...

Jesus Manuel Benitez Baleato · 0 citations
Open access Jul 2026

The ethics of AI systems that emulate identifiable individuals: challenges for value aligned development

This work builds on and extends the Beneficent Intelligence (BI) framework from [23] to clarify the mediating role that such AI systems play, how this impacts alignment with the goals of a range of parties, and identify conditions necessary for such systems to confer meaningful benefit.

Ida Mattsson, Mai-Lee Chang, Niloofar Nikookar et al. · 0 citations

Related blog posts

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

Microsoft Research Blog Jul 30, 2026

Echoverse: Deep, evolving environments for computer-use agents

Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve. The post Echoverse: Deep, evolving environments for computer-use agents appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.