AI data transparency: an exploration through the lens of AI incidents

• URL: http://arxiv.org/abs/2409.03307v1
• Date: Thu, 5 Sep 2024 07:23:30 GMT
• Title: AI data transparency: an exploration through the lens of AI incidents
• Authors: Sophia Worth, Ben Snaith, Arunav Das, Gefion Thuermer, Elena Simperl,
• Abstract summary: This research explores the status of public documentation about data practices within AI systems generating public concern. We highlight a need to develop systematic ways of monitoring AI data transparency that account for the diversity of AI system types.
• Score: 2.255682336735152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowing more about the data used to build AI systems is critical for allowing different stakeholders to play their part in ensuring responsible and appropriate deployment and use. Meanwhile, a 2023 report shows that data transparency lags significantly behind other areas of AI transparency in popular foundation models. In this research, we sought to build on these findings, exploring the status of public documentation about data practices within AI systems generating public concern. Our findings demonstrate that low data transparency persists across a wide range of systems, and further that issues of transparency and explainability at model- and system- level create barriers for investigating data transparency information to address public concerns about AI systems. We highlight a need to develop systematic ways of monitoring AI data transparency that account for the diversity of AI system types, and for such efforts to build on further understanding of the needs of those both supplying and using data transparency information.

Related papers

• Through the Looking-Glass: Transparency Implications and Challenges in Enterprise AI Knowledge Systems [3.7640559288894524]
  We present the looking-glass metaphor and use it to conceptualize AI knowledge systems as systems that reflect and distort. We identify three transparency dimensions necessary to realize the value of AI knowledge systems, namely system transparency, procedural transparency and transparency of outcomes.
  arXiv  Detail & Related papers  (2024-01-17T18:47:30Z)
• Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability [0.0]
  As AI systems become increasingly sophisticated, ensuring their transparency and explainability becomes crucial. We propose a design for user-centered compliant-by-design transparency in transparent systems. By providing a comprehensive understanding of the challenges associated with transparency in AI systems, we aim to facilitate the development of AI systems that are accountable, trustworthy, and aligned with societal values.
  arXiv  Detail & Related papers  (2023-10-13T04:25:30Z)
• Representation Engineering: A Top-Down Approach to AI Transparency [132.0398250233924]
  We identify and characterize the emerging area of representation engineering (RepE) RepE places population-level representations, rather than neurons or circuits, at the center of analysis. We showcase how these methods can provide traction on a wide range of safety-relevant problems.
  arXiv  Detail & Related papers  (2023-10-02T17:59:07Z)
• Towards Cross-Provider Analysis of Transparency Information for Data Protection [0.0]
  This paper presents a novel approach to enable large-scale transparency information analysis across service providers. We provide the general approach for advanced transparency information analysis, an open source architecture and implementation in the form of a queryable analysis platform. Future work can build upon our contributions to gain more insights into so-far hidden data-sharing practices.
  arXiv  Detail & Related papers  (2023-09-01T10:36:09Z)
• Users are the North Star for AI Transparency [111.5679109784322]
  Despite widespread calls for transparent artificial intelligence systems, the term is too overburdened with disparate meanings to express precise policy aims or to orient concrete lines of research. Part of why this happens is that a clear ideal of AI transparency goes unsaid in this body of work. We explicitly name such a north star -- transparency that is user-centered, user-appropriate, and honest.
  arXiv  Detail & Related papers  (2023-03-09T18:53:29Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance [14.043062659347427]
  Laws are being proposed and passed by governments around the world to regulate Artificial Intelligence (AI) systems implemented into the public and private sectors. Many of these regulations address the transparency of AI systems, and related citizen-aware issues like allowing individuals to have the right to an explanation about how an AI system makes a decision that impacts them. We propose a novel stakeholder-first approach that assists technologists in designing transparent, regulatory compliant systems.
  arXiv  Detail & Related papers  (2022-06-10T09:39:00Z)
• Trustworthy Transparency by Design [57.67333075002697]
  We propose a transparency framework for software design, incorporating research on user trust and experience. Our framework enables developing software that incorporates transparency in its design.
  arXiv  Detail & Related papers  (2021-03-19T12:34:01Z)
• A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions [0.6850683267295249]
  This paper presents a novel method for deriving a quantifiable metric capable of ranking the overall transparency of the process pipelines used to generate AI systems. The methodology for calculating the metric, and the type of criteria that could be used to make judgements on the visibility of contributions to systems are evaluated.
  arXiv  Detail & Related papers  (2021-03-05T11:28:50Z)
• Dimensions of Transparency in NLP Applications [64.16277166331298]
  Broader transparency in descriptions of and communication regarding AI systems is widely considered desirable. Previous work has suggested that a trade-off exists between greater system transparency and user confusion.
  arXiv  Detail & Related papers  (2021-01-02T11:46:17Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)

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.