Ethical Statistical Practice and Ethical AI

• URL: http://arxiv.org/abs/2410.22475v1
• Date: Tue, 29 Oct 2024 19:09:34 GMT
• Title: Ethical Statistical Practice and Ethical AI
• Authors: Rochelle E. Tractenberg,
• Abstract summary: An increase in social, cultural, industrial, scientific, and governmental concerns about the ethical development and use of AI systems worldwide. The ASA has issued a statement on ethical statistical practice and AI (ASA, 2024) Here we discuss the support for ethical statistical practice and ethical AI that has been established in long-standing human rights law and ethical practice standards for computing and statistics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Artificial Intelligence (AI) is a field that utilizes computing and often, data and statistics, intensively together to solve problems or make predictions. AI has been evolving with literally unbelievable speed over the past few years, and this has led to an increase in social, cultural, industrial, scientific, and governmental concerns about the ethical development and use of AI systems worldwide. The ASA has issued a statement on ethical statistical practice and AI (ASA, 2024), which echoes similar statements from other groups. Here we discuss the support for ethical statistical practice and ethical AI that has been established in long-standing human rights law and ethical practice standards for computing and statistics. There are multiple sources of support for ethical statistical practice and ethical AI deriving from these source documents, which are critical for strengthening the operationalization of the "Statement on Ethical AI for Statistics Practitioners". These resources are explicated for interested readers to utilize to guide their development and use of AI in, and through, their statistical practice.

Related papers

• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• Ethics in AI through the Practitioner's View: A Grounded Theory Literature Review [12.941478155592502]
  In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. We conducted a grounded theory literature review (GTLR) of 38 primary empirical studies that included AI practitioners' views on ethics in AI. We present a taxonomy of ethics in AI from practitioners' viewpoints to assist AI practitioners in identifying and understanding the different aspects of AI ethics.
  arXiv  Detail & Related papers  (2022-06-20T00:28:51Z)
• AI Ethics Issues in Real World: Evidence from AI Incident Database [0.6091702876917279]
  We identify 13 application areas which often see unethical use of AI, with intelligent service robots, language/vision models and autonomous driving taking the lead. Ethical issues appear in 8 different forms, from inappropriate use and racial discrimination, to physical safety and unfair algorithm.
  arXiv  Detail & Related papers  (2022-06-15T16:25:57Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• Actionable Approaches to Promote Ethical AI in Libraries [7.1492901819376415]
  The widespread use of artificial intelligence (AI) in many domains has revealed numerous ethical issues. No practical guidance currently exists for libraries to plan for, evaluate, or audit the ethics of intended or deployed AI. We report on several promising approaches for promoting ethical AI that can be adapted from other contexts.
  arXiv  Detail & Related papers  (2021-09-20T16:38:49Z)
• Measuring Ethics in AI with AI: A Methodology and Dataset Construction [1.6861004263551447]
  We propose to use such newfound capabilities of AI technologies to augment our AI measuring capabilities. We do so by training a model to classify publications related to ethical issues and concerns. We highlight the implications of AI metrics, in particular their contribution towards developing trustful and fair AI-based tools and technologies.
  arXiv  Detail & Related papers  (2021-07-26T00:26:12Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• The State of AI Ethics Report (October 2020) [30.265104923077185]
  The State of AI Ethics captures the most relevant developments in the field of AI Ethics since July 2020. This report aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the ever-changing developments in the field. The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments.
  arXiv  Detail & Related papers  (2020-11-05T12:36:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.