Named Entity Recognition (NER) task aims at automatically recognising and classifying named entities in a given natural language input. Majority of the studies related to Named Entity Recognition are focused on English language. Named Entity Recognition in Indian languages is challenging due to the complex grammar and morphology of Indian languages and the scarcity of good quality labelled data. The dissimilarity between the literary and spoken versions of these languages is also a big challenge regarding usability of NER models. Kannada is such an Indian language for which the task of NER is still an active area of research. Usage of Deep learning, especially Transfer learning using Transformer models has drastically improved performance of many NLP tasks at scale. However, Transfer learning still requires considerable amount of data for the required task. In case of low resource language like Kannada, very few labelled datasets are available publicly and creating one from scratch is expensive in terms of time and labor. Active learning (AL) aims to tackle the labelled data acquisition problem by having the learning model and an Oracle to cooperate. Active Learning iteratively builds an optimally labelled and sufficiently large dataset.  This study focuses on Named Entity Recognition in Kannada language. We explore the application of Active Learning technique to Named Entity Recognition problem in the low-resource language Kannada. Results show that AL can be used to boost a multilingual models performance in fine-tuning for NER. We also try to mitigate the gap between formal and colloquial dialects of Kannada in NER datasets.

Computational models for ethical autonomy, are crucial for building trustworthy autonomous systems. While different paradigms of ethical autonomy are pursued, comparing and contrasting these paradigms remains a challenge. In this work, we present SPECTRA (Strategic Protocol Evaluation and Configuration Testbed for Responsible Autonomy) a general purpose multi-agent, message passing framework on top of which, different models of computational ethics can be implemented. The paper also presents our implementation of four paradigms of ethics on this framework– deontology, utilitarianism, virtue ethics and a recently proposed paradigm called computational transcendence. We observe that although agents have the same goal, differences in their underlying paradigm of ethics has a significant impact on the outcomes for individual agents as well as on the system as a whole. We also simulate a mixed population of agents following different paradigms of ethics and study the properties of the emergent system.