Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach
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Social transparency makes transparent the social contexts around the usage of an AI system to help people better understand the AI and how to utilize it.
“Social transparency makes transparent the social contexts around the usage of an AI system to help people better understand the AI and how to utilize it.”
From Investigating Explainability of Generative AI for Code through Scenario-based Design (2022) · cites this paper as Ehsan and Riedl [21]
Section 3.2 (p. 11) proposes embedding social transparency to facilitate user actions and describes social signals that reveal a team’s perspective on an AI recommendation. This supports the claim about making social context visible to help people decide how to act, but the paper does not establish that social transparency helps users better understand the AI itself. (LLM, conf=0.82)
Experimental — accuracy not yet measuredXAI for autonomous systems offers rationales and accountability on intervention choices in crucial traffic situations.
“Based in the existing research summarized in Table 2, it can be concluded that XAI for autonomous systems is a catalogue of AI-driven methodologies that (1) guarantee a plausible degree of safety for a vehicle's major decisions, (2) offer rationales and accountability on the intervention choices in crucial traffic situations, and (3) follow all traffic regulations set forth by the law enforcement”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [103]
The Introduction (p. 1) defines XAI as providing human-understandable justifications, and the case study (pp. 3, 7) generates rationales for an AI agent avoiding traffic in the game Frogger. The paper mentions self-driving cars as an example of differing explanation needs (Introduction, p. 2), but does not establish that XAI provides accountability for autonomous vehicles’ intervention choices in crucial real-world traffic situations. (LLM, conf=0.86)
Experimental — accuracy not yet measuredXAI for autonomous systems follows all traffic regulations set forth by law enforcement agencies.
“Based in the existing research summarized in Table 2, it can be concluded that XAI for autonomous systems is a catalogue of AI-driven methodologies that (1) guarantee a plausible degree of safety for a vehicle's major decisions, (2) offer rationales and accountability on the intervention choices in crucial traffic situations, and (3) follow all traffic regulations set forth by the law enforcement”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [103]
The Introduction (pp. 1–2) defines XAI and mentions self-driving cars only as an example of differing explanation needs. The case study (p. 3) concerns an agent playing Frogger. A search for traffic regulations, law enforcement, and autonomous-vehicle compliance found no passage saying that XAI systems follow traffic laws. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe system can grasp operating conditions based on comprehensive data provided by 3-dimensional recognition occurrences.
“The system can grasp the operating conditions based on the comprehensive data provided by 3-dimensional recognition occurrences [102].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [102]
Section 2.2 (p. 7) describes a rationale generator that can use all information on a screen, not a system using three-dimensional recognition to grasp operating conditions. Searches for three-dimensional recognition and operating conditions found no passage addressing that mechanism. (LLM, conf=0.76)
Experimental — accuracy not yet measuredXAI for autonomous systems guarantees a plausible degree of safety for a vehicle's major decisions.
“Based in the existing research summarized in Table 2, it can be concluded that XAI for autonomous systems is a catalogue of AI-driven methodologies that (1) guarantee a plausible degree of safety for a vehicle's major decisions, (2) offer rationales and accountability on the intervention choices in crucial traffic situations, and (3) follow all traffic regulations set forth by the law enforcement”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [103]
The Introduction (p. 1) defines XAI in terms of human-understandable justifications, not vehicle safety. The case study concerns a Frogger-playing agent (p. 3), and the discussion of self-driving cars concerns different users’ explanation needs (Introduction, p. 2). Searches for autonomous-vehicle safety and traffic regulations found no passage saying XAI guarantees the safety of a vehicle’s decisions. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Ehsan, U., & Riedl, M. O.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Ehsan, Upol
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 449-466
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3004940685
- Cited by
- 274
- References
- 42
- Citations by year
- 2026: 61 · 2025: 56 · 2024: 63 · 2023: 50 · 2022: 29 · 2021: 12 · 2020: 3
- FWCI
- 8.80
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- explainable AI, post-hoc explanation, socio-technical systems, human factors, explanation systems, critical technical practice, value-sensitive design, participatory design, human-computer interaction, interpersonal dynamics
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