Possible ways forward towards the implementation of governance on AI are finally examined. The following applications of AI-driven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. The implication is that ML code scripts are rarely scrutinised interpretability is usually sacrificed in favour of usability and effectiveness. ML approaches-one of the typologies of algorithms underpinning artificial intelligence-are typically developed as black boxes. Our proposed TAQS model surpassed the performance of the state-of-the-art BiLSTM with SkipGram by a gain of 43.19% in accuracy.ĭecision-making on numerous aspects of our daily lives is being outsourced to machine-learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. The results show that the HT-BERT-BiLSTM with the features of Layer 12 reaches an accuracy of 94.45%, where the fine-tuning of AraBERTv2 and AraBERTv0.2 achieve 93.10% and 93.90% accuracy, respectively, for the Tawasul dataset. Second, we fine-tuned two versions of bidirectional encoder representations from transformers for Arabic language (AraBERT). The hybrid transfer representation combines two transfer learning techniques. Specifically, we propose two architectures: the BERT contextual representation with BiLSTM (BERT-BiLSTM) and the hybrid transfer BERT contextual representation with BiLSTM (HT-BERT-BiLSTM). Second, we curated and used an Arabic customer service question-similarity dataset with a 44,404 entries of question–answer pairs, called “Tawasul.” For TAQS, first, we use transfer learning to extract the contextualized bidirectional encoder representations from transformers (BERT) embedding with bidirectional long short-term memory (BiLSTM) in two different ways. First, we propose the Tawasul Arabic question similarity (TAQS) system with four Arabic semantic question similarity models using deep learning techniques. There are two main contributions of this research. Machine learning performance surpasses that of humans in some areas, such as natural language processing and text analysis, especially with large amounts of data. Machine question answering has appeared as an important emerging field for progress in natural language processing techniques. With the rapid increase of Arabic content on the web comes an increased need for short and accurate answers to queries.
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