Human-Centric NLP or AI-Centric Illusion?: A Critical Investigation
- URL: http://arxiv.org/abs/2412.10939v1
- Date: Sat, 14 Dec 2024 19:16:53 GMT
- Title: Human-Centric NLP or AI-Centric Illusion?: A Critical Investigation
- Abstract summary: This study identifies a significant gap between the ideas of human-centricity and actual practices.<n>The paper advocates for a redefinition of Human-Centric NLP, urging a broader focus on real-world utility and societal implications.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Human-Centric NLP often claims to prioritise human needs and values, yet many implementations reveal an underlying AI-centric focus. Through an analysis of case studies in language modelling, behavioural testing, and multi-modal alignment, this study identifies a significant gap between the ideas of human-centricity and actual practices. Key issues include misalignment with human-centred design principles, the reduction of human factors to mere benchmarks, and insufficient consideration of real-world impacts. The discussion explores whether Human-Centric NLP embodies true human-centred design, emphasising the need for interdisciplinary collaboration and ethical considerations. The paper advocates for a redefinition of Human-Centric NLP, urging a broader focus on real-world utility and societal implications to ensure that language technologies genuinely serve and empower users.
Related papers
- Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics [25.781418598536906]
Safety risks of AI are becoming increasingly evident in human interactions with AI technologies.<n>To examine this apparent gap in the acceptance of human research, we conduct an expert survey (n=93) and expert interviews (n=17) with AI Safety & Ethics (AISE) researchers.
arXiv Detail & Related papers (2026-08-06T06:52:52Z) - Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Making [0.0]
We introduce a human-AI collaborative decision-making framework designed to augment human capabilities and align AI agents with human preferences and expectations.<n>The Human-Centric Reflective Architecture (HCRA) integrates human-calibrated models with reinforcement learning agents that leverage linguistic feedback in an iterative, reflective process.
arXiv Detail & Related papers (2026-07-03T07:12:44Z) - Large Language Models as a Semantic Interface and Ethical Mediator in Neuro-Digital Ecosystems: Conceptual Foundations and a Regulatory Imperative [83.99688944263843]
This article introduces and substantiates the concept of Neuro-Linguistic Integration (NLI)<n>It is a novel paradigm for human-technology interaction where Large Language Models act as a key semantic interface between raw neural data and their social application.<n>We argue for the development of a second-order neuroethics,' focused not merely on neural data protection but on the ethics of AI-mediated semantic interpretation itself.
arXiv Detail & Related papers (2026-03-18T07:34:17Z) - HumanPCR: Probing MLLM Capabilities in Diverse Human-Centric Scenes [72.26829188852139]
HumanPCR is an evaluation suite for probing MLLMs' capacity about human-related visual contexts.<n>Human-P, HumanThought-C, and Human-R feature over 6,000 human-verified multiple choice questions.<n>Human-R offers a challenging manually curated video reasoning test.
arXiv Detail & Related papers (2025-08-19T09:52:04Z) - Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missing [66.04926909181653]
We argue for anthropomimetic uncertainty, meaning that intuitive and trustworthy uncertainty communication requires a degree of linguistic authenticity and personalization to the user.<n>We conclude by pointing out unique factors in human-machine communication of uncertainty and deconstruct the data biases that influence machine uncertainty communication.
arXiv Detail & Related papers (2025-07-11T14:07:22Z) - Exploring Societal Concerns and Perceptions of AI: A Thematic Analysis through the Lens of Problem-Seeking [0.0]
This study introduces a novel conceptual framework distinguishing problem-seeking from problem-solving to clarify the unique features of human intelligence in contrast to AI.<n>The framework emphasizes that while AI excels at efficiency and optimization, it lacks the orientation derived from grounding and the embodiment flexibility intrinsic to human cognition.
arXiv Detail & Related papers (2025-05-29T18:24:34Z) - Human-Centered Human-AI Collaboration (HCHAC) [9.14056952246194]
Human-AI Collaboration (HAC) represents a novel type of human-machine relationship facilitated by AI technologies.<n>Human-centered AI (HCAI) emphasizes that humans play critical leadership roles in the collaboration.<n>This chapter delves into the essence of HAC from the human-centered perspective, outlining its core concepts and distinguishing features.
arXiv Detail & Related papers (2025-05-28T15:27:52Z) - On Benchmarking Human-Like Intelligence in Machines [77.55118048492021]
We argue that current AI evaluation paradigms are insufficient for assessing human-like cognitive capabilities.
We identify a set of key shortcomings: a lack of human-validated labels, inadequate representation of human response variability and uncertainty, and reliance on simplified and ecologically-invalid tasks.
arXiv Detail & Related papers (2025-02-27T20:21:36Z) - Aligning Generalisation Between Humans and Machines [74.120848518198]
Recent advances in AI have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals.
The responsible use of AI increasingly shows the need for human-AI teaming.
A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
arXiv Detail & Related papers (2024-11-23T18:36:07Z) - AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitations [0.2106667480549292]
We show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness.
We highlight tensions and contradictions inherent in the goals of RLxF.
We conclude by urging researchers and practitioners alike to critically assess the sociotechnical ramifications of RLxF.
arXiv Detail & Related papers (2024-06-26T13:42:13Z) - Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions [101.67121669727354]
Recent advancements in AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment.
The lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment.
We introduce a systematic review of over 400 papers published between 2019 and January 2024, spanning multiple domains such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), Machine Learning (ML)
arXiv Detail & Related papers (2024-06-13T16:03:25Z) - ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models [53.00812898384698]
We argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking.
We highlight how cognitive biases can conflate fluent information and truthfulness, and how cognitive uncertainty affects the reliability of rating scores such as Likert.
We propose the ConSiDERS-The-Human evaluation framework consisting of 6 pillars -- Consistency, Scoring Criteria, Differentiating, User Experience, Responsible, and Scalability.
arXiv Detail & Related papers (2024-05-28T22:45:28Z) - Collaborative Evaluation: Exploring the Synergy of Large Language Models
and Humans for Open-ended Generation Evaluation [71.76872586182981]
Large language models (LLMs) have emerged as a scalable and cost-effective alternative to human evaluations.
We propose a Collaborative Evaluation pipeline CoEval, involving the design of a checklist of task-specific criteria and the detailed evaluation of texts.
arXiv Detail & Related papers (2023-10-30T17:04:35Z) - Unmasking Nationality Bias: A Study of Human Perception of Nationalities
in AI-Generated Articles [10.8637226966191]
We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods.
Our study employs a two-step mixed-methods approach to identify and understand the impact of nationality bias in a text generation model.
Our findings reveal that biased NLP models tend to replicate and amplify existing societal biases, which can translate to harm if used in a sociotechnical setting.
arXiv Detail & Related papers (2023-08-08T15:46:27Z) - Learning and Evaluating Human Preferences for Conversational Head
Generation [101.89332968344102]
We propose a novel learning-based evaluation metric named Preference Score (PS) for fitting human preference according to the quantitative evaluations across different dimensions.
PS can serve as a quantitative evaluation without the need for human annotation.
arXiv Detail & Related papers (2023-07-20T07:04:16Z) - UniHCP: A Unified Model for Human-Centric Perceptions [75.38263862084641]
We propose a Unified Model for Human-Centric Perceptions (UniHCP)
UniHCP unifies a wide range of human-centric tasks in a simplified end-to-end manner with the plain vision transformer architecture.
With large-scale joint training on 33 human-centric datasets, UniHCP can outperform strong baselines by direct evaluation.
arXiv Detail & Related papers (2023-03-06T07:10:07Z) - User Centered Design (VI): Human Factors Approaches for Intelligent
Human-Computer Interaction [9.7988110067549]
This paper analyzes the human factors characteristics of intelligent human-computer interaction (iHCI)
It proposes a new human factors framework for iHCI based on the theories of joint cognitive systems, situation awareness, and intelligent agents.
arXiv Detail & Related papers (2021-11-08T23:48:40Z) - An Objective Laboratory Protocol for Evaluating Cognition of Non-Human
Systems Against Human Cognition [0.0]
The existence of a non-human system with cognitive capabilities comparable to those of humans might make once-philosophical questions of safety and ethics urgent.
This is important because the existence of a non-human system with cognitive capabilities comparable to those of humans might make once-philosophical questions of safety and ethics immediate and urgent.
arXiv Detail & Related papers (2021-02-17T18:40:49Z) - You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation.
Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
arXiv Detail & Related papers (2020-04-11T12:51:07Z)
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.