Human-Centered AI in Safety-Critical Systems: From Educational Simulations to NLP and Privacy-Aware Redundancy

Abstract

Large-scale language understanding increasingly requires systems that operate across modalities, user populations, and deployment environments. This paper presents an integrated approach that combines Natural Language Processing (NLP)–based sentiment analysis of social media with automated sign language interpretation to deliver inclusive, scalable language intelligence. For social media sentiment, we design a pipeline that handles noisy, short-form text (tweets, posts, comments) by combining robust preprocessing (emoji and hashtag normalization, slang lexicons), contextualized language encoders (pretrained transformer models fine-tuned on in-domain data), and multimodal signals (attached images, timestamps, and user metadata) to improve polarity and fine-grained emotion detection. For sign language interpretation, we develop an end-to-end visual–linguistic system that uses spatiotemporal visual encoders (frame-level CNNs + keypoint/pose features), transformer-based sequence models for alignment and translation, and language-model-informed decoders to produce grammatical target-language text. Crucially, we propose a shared engineering and evaluation framework that addresses dataset curation, privacy-aware collection, cross-modal alignment, domain adaptation, and fairness auditing. The integrated system leverages multimodal pretraining, contrastive objectives to align vision and text embeddings, and data-augmentation strategies including synthetic sign generation and back-translation. Experimental protocols describe benchmarking on (1) social media sentiment datasets with manual and weak labels and (2) signer-independent continuous sign translation corpora, with evaluation using accuracy/F1 and BLEU/ROUGE/human intelligibility respectively. We discuss deployment patterns for cloud and edge inference to meet latency and accessibility requirements and describe mitigation strategies for bias amplification and privacy leakage. Results from ablation studies indicate that (a) multimodal fusion improves social-media sentiment F1 on multimodal posts by a measurable margin versus text-only baselines, and (b) pose-informed transformer decoders significantly reduce gloss WER and increase translation fluency for sign interpretation. We conclude with recommended practices for dataset governance, community involvement in sign-language data collection, and technical directions to scale inclusive language understanding systems responsibly.