Six Guidelines for Trustworthy, Ethical and Responsible Automation Design
- URL: http://arxiv.org/abs/2508.02371v1
- Date: Mon, 04 Aug 2025 13:01:09 GMT
- Title: Six Guidelines for Trustworthy, Ethical and Responsible Automation Design
- Authors: Matouš Jelínek, Nadine Schlicker, Ewart de Visser,
- Abstract summary: Calibrated trust in automated systems is critical for their safe and seamless integration into society.<n>We propose six design guidelines to help designers optimize for accurate trustworthiness assessments.
- Score: 0.6144680854063939
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Calibrated trust in automated systems (Lee and See 2004) is critical for their safe and seamless integration into society. Users should only rely on a system recommendation when it is actually correct and reject it when it is factually wrong. One requirement to achieve this goal is an accurate trustworthiness assessment, ensuring that the user's perception of the system's trustworthiness aligns with its actual trustworthiness, allowing users to make informed decisions about the extent to which they can rely on the system (Schlicker et al. 2022). We propose six design guidelines to help designers optimize for accurate trustworthiness assessments, thus fostering ethical and responsible human-automation interactions. The proposed guidelines are derived from existing literature in various fields, such as human-computer interaction, cognitive psychology, automation research, user-experience design, and ethics. We are incorporating key principles from the field of pragmatics, specifically the cultivation of common ground (H. H. Clark 1996) and Gricean communication maxims (Grice 1975). These principles are essential for the design of automated systems because the user's perception of the system's trustworthiness is shaped by both environmental contexts, such as organizational culture or societal norms, and by situational context, including the specific circumstances or scenarios in which the interaction occurs (Hoff and Bashir 2015). Our proposed guidelines provide actionable insights for designers to create automated systems that make relevant trustworthiness cues available. This would ideally foster calibrated trust and more satisfactory, productive, and safe interactions between humans and automated systems. Furthermore, the proposed heuristics might work as a tool for evaluating to what extent existing systems enable users to accurately assess a system's trustworthiness.
Related papers
- On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective [334.48358909967845]
Generative Foundation Models (GenFMs) have emerged as transformative tools.<n>Their widespread adoption raises critical concerns regarding trustworthiness across dimensions.<n>This paper presents a comprehensive framework to address these challenges through three key contributions.
arXiv Detail & Related papers (2025-02-20T06:20:36Z) - AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.<n>The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
arXiv Detail & Related papers (2025-02-19T05:58:52Z) - Position: We Need An Adaptive Interpretation of Helpful, Honest, and Harmless Principles [24.448749292993234]
The Helpful, Honest, and Harmless (HHH) principle is a framework for aligning AI systems with human values.<n>We argue for an adaptive interpretation of the HHH principle and propose a reference framework for its adaptation to diverse scenarios.<n>This work offers practical insights for improving AI alignment, ensuring that HHH principles remain both grounded and operationally effective in real-world AI deployment.
arXiv Detail & Related papers (2025-02-09T22:41:24Z) - Trustworthiness in Stochastic Systems: Towards Opening the Black Box [1.7355698649527407]
behavior by an AI system threatens to undermine alignment and potential trust.<n>We take a philosophical perspective to the tension and potential conflict between foundationality and trustworthiness.<n>We propose latent value modeling for both AI systems and users to better assess alignment.
arXiv Detail & Related papers (2025-01-27T19:43:09Z) - Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization.
We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
arXiv Detail & Related papers (2024-10-25T07:53:32Z) - Trust-Oriented Adaptive Guardrails for Large Language Models [9.719986610417441]
Guardrails are designed to ensure that large language models (LLMs) align with human values by moderating harmful or toxic responses.<n>This paper addresses a critical issue: existing guardrails lack a well-founded methodology to accommodate the diverse needs of different user groups.<n>We introduce an adaptive guardrail mechanism, to dynamically moderate access to sensitive content based on user trust metrics.
arXiv Detail & Related papers (2024-08-16T18:07:48Z) - Measuring Value Alignment [12.696227679697493]
This paper introduces a novel formalism to quantify the alignment between AI systems and human values.
By utilizing this formalism, AI developers and ethicists can better design and evaluate AI systems to ensure they operate in harmony with human values.
arXiv Detail & Related papers (2023-12-23T12:30:06Z) - A Survey on Fairness-aware Recommender Systems [59.23208133653637]
We present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems.
Next, we delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications.
arXiv Detail & Related papers (2023-06-01T07:08:22Z) - A Systematic Literature Review of User Trust in AI-Enabled Systems: An
HCI Perspective [0.0]
User trust in Artificial Intelligence (AI) enabled systems has been increasingly recognized and proven as a key element to fostering adoption.
This review aims to provide an overview of the user trust definitions, influencing factors, and measurement methods from 23 empirical studies.
arXiv Detail & Related papers (2023-04-18T07:58:09Z) - Designing for Responsible Trust in AI Systems: A Communication
Perspective [56.80107647520364]
We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH.
We highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users.
We propose a checklist of requirements to help technology creators identify appropriate cues to use.
arXiv Detail & Related papers (2022-04-29T00:14:33Z) - Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and
Goals of Human Trust in AI [55.4046755826066]
We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i.e., trust between people)
We incorporate a formalization of 'contractual trust', such that trust between a user and an AI is trust that some implicit or explicit contract will hold.
We discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted.
arXiv Detail & Related papers (2020-10-15T03:07:23Z)
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.