Towards Transparency by Design for Artificial Intelligence

By Heike Felzmann, Eduard Fosch-Villaronga, C. Lutz, Aurelia Tamó-Larrieux. Science and Engineering Ethics, 2020

449 citations recorded by the source corpus.

In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making (ADM) environments. With the rise of artificial intelligence (AI) and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds different promises that struggle to be realized in concrete applications. Indeed, the complexity of transparency for ADM shows tension between transparency as a normative ideal and its translation to practical application. To address this tension, we first conduct a review of transparency, analyzing its challenges and limitations concerning automated decision-making practices. We then look at the lessons learned from the development of Privacy by Design, as a basis for developing the Transparency by Design principles. Finally, we propose a set of nine principles to cover relevant contextual, technical, informational, and stakeholder-sensitive considerations. Transparency by Design is a model that helps organizations design transparent AI systems, by integrating these principles in a step-by-step manner and as an ex-ante value, not as an afterthought.

Subfields: Artificial Intelligence, Information Systems, Safety Research

View Paper: https://www.semanticscholar.org/paper/03d7cd2475ba417fbc0d5f6b7255f8aa14eb227a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Q. Liao
2. Jennifer Wortman Vaughan
3. Andrew Zaldivar
4. Oddur Kjartansson
5. Mahima Pushkarna
6. Ming Yin

In Information Systems

1. Q. Liao
2. Oddur Kjartansson
3. Jennifer Wortman Vaughan

In Safety Research

1. Q. Liao
2. Jennifer Wortman Vaughan
3. Will Sutherland
4. Andrew Zaldivar
5. Mohammad Hossein Jarrahi
6. Oddur Kjartansson
7. Mahima Pushkarna
8. Louisa H.Y. Tsi

Citations in the corpus

• Algorithmic management in a work context (2021, methodology): “The transparency by design framework (Felzmann et al., 2020) or a participatory AI design framework (Lee et al., 2019) can provide procedures and tools that enable such stake-holder participation.”
• AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap (2023, methodology): “In this paper, we adopt a focus on informational transparency— essentially, what information about a model (or system building on that model) should be disclosed to enable appropriate understanding—which has been emphasized within the machine learning (ML) research community and in industry practice”
• Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI (2022, background): “This reflects real world constraints of the diversity of goals, workflows, and backgrounds of individual stakeholders participating in the life cycles of datasets and artificial intelligence (AI) systems [11, 13, 14].”