State of Ethical AI Frameworks

State of Ethical AI Frameworks

Document ID: CGR-ETH21 | Last Updated: Apr. 28, 2021

The topic of ethics and responsibility comes up frequently in the context of Artificial Intelligence, and for good reason. The concept of intelligent machines is scary to many people. Others are just as concerned about the increasing power that humans have over data and the potential for malicious use of powerful technology.

To address these concerns, organizations are producing frameworks to help classify the various ethics-related concerns for AI and ways in which people can maintain control over the use of potent AI technology. However, the area of ethics and technology is not one of clear definitions, concise terminology with well-defined boundaries, and areas of well-developed concepts. In this report, Cognilytica explores the range of ethical frameworks across the concepts of societal ethics, responsible use of AI, AI systemic transparency, governance of AI systems, and algorithmic interpretability and explainability. This report provides a basis for comparison across ethical concepts for the identified frameworks and identifies the strengths and potential weaknesses of different proposed ethical frameworks. Cognilytica also proposes a potential “normative” ethical framework covering all the concepts identified in this document, with concise boundaries between ethical concepts.

$995.00

The topic of ethics and responsibility comes up frequently in the context of Artificial Intelligence, and for good reason. The concept of intelligent machines is scary to many people. Others are just as concerned about the increasing power that humans have over data and the potential for malicious use of powerful technology. To address these concerns, organizations are producing frameworks to help classify the various ethics-related concerns for AI and ways in which people can maintain control over the use of potent AI technology. However, the area of ethics and technology is not one of clear definitions, concise terminology with well-defined boundaries, and areas of well-developed concepts. In this report, Cognilytica explores the range of ethical frameworks across the concepts of societal ethics, responsible use of AI, AI systemic transparency, governance of AI systems, and algorithmic interpretability and explainability. This report provides a basis for comparison across ethical concepts for the identified frameworks and identifies the strengths and potential weaknesses of different proposed ethical frameworks. Cognilytica also proposes a potential “normative” ethical framework covering all the concepts identified in this document, with concise boundaries between ethical concepts.
  • Cognilytica analyzed over 60 ethical frameworks developed by government bodies, multinational orgs, corporations, non-profit groups, technology consortia, standards organizations and other groups.
  • No ethical framework analyzed is complete across all the various areas of ethical concerns for AI.
  • Government frameworks are most lacking aspects of responsible AI use, corporate frameworks are missing most elements of AI system transparency, and multinational organizations seem most concerned with regulation, certification, and third-party oversight.
  • Canada’s various ethical AI frameworks are most complete whereas the ethical frameworks developed by the United States across various groups are lacking in many critical areas.
  • Aspects of human benefit and human accountability are the most common ethical principles observed within ethical AI frameworks while measurement of AI bias, organizational training, and lethal autonomous weapon use is least common.
Premium PDF. Source: Copyright © Cognilytica LLC
  • 155 Pages
  • 1 Figure, 1 Table
  • Comprehensive comparison spreadsheet of ethical AI frameworks included
Table of Contents
  • Executive Summary
  • Key Findings
  • Ethics Concepts and Definitions
  • Categorization of Ethical AI Principles
  • The Spectrum of Ethical AI Concepts
    • Societal Ethics AI Principles
  • Ethical AI vs. Responsible AI
    • Responsible AI Principles
  • AI Systemic Transparency vs. Algorithmic Interpretability
    • Systemic AI Transparency Principles
  • AI Governance
    • AI Governance Principles
  • Algorithmic Interpretability & Explainability
    • AI Algorithmic Explainability & Interpretability Principles
  • Governmental Ethical AI Frameworks
    • Australia
    • Canada
    • China
    • Colombia
    • Denmark
    • European Union
    • France
    • Germany
    • India
    • Japan
    • Malta
    • New Zealand
    • Singapore
    • United Arab Emirates (UAE) [Dubai]
    • United Kingdom
    • United States
    • Vatican City
  • Organizational Ethical AI Frameworks
  • Corporate Ethical AI Frameworks
  • Additional Resources
  • Analysis and Insights
    • Overall Framework Analysis
    • Comparing Government, Corporate, and Multinational Ethical AI frameworks
  • Related Research
  • About Cognilytica
Although Cognilytica believes that the results, conclusions, and analysis produced in support of this report are well informed, comprehensive, and reasonable, Cognilytica cannot guarantee future results, accuracy of market predictions, or applicability of conclusions to report purchaser or reader’s business. Moreover, Cognilytica does not assume responsibility for the accuracy and completeness of such statements. The information derived in this report are statements of opinion only, and Cognilytica shall not be held liable in any manner for any conclusions or actions taken pursuant to this report. The information contained herein has been obtained from sources believed to be reliable. Cognilytica shall have no liability for errors, omissions, or inadequacies in the information contained herein or for interpretations thereof. Report purchaser and/or reader assumes sole responsibility for the selection of these materials to achieve its intended results. The opinions expressed herein are subject to change without notice. Cognilytica does not make open its research methods, underlying data, sources, or means and methods of analysis for inquiry, evaluation, or examination.