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Full-Text Articles in Artificial Intelligence and Robotics
Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi Asyrofi, Zhou Yang, Imam Nur Bani Yusuf, Hong Jin Kang, Thung Ferdian, David Lo
Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi Asyrofi, Zhou Yang, Imam Nur Bani Yusuf, Hong Jin Kang, Thung Ferdian, David Lo
Research Collection School Of Computing and Information Systems
Artificial intelligence systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human bias. Consequently, such systems may exhibit unintended demographic bias against specific characteristics (e.g., gender, occupation, country-of-origin, etc.). Such bias manifests in an SA system when it predicts different sentiments for similar texts that differ only in the characteristic of individuals described. To automatically uncover bias in SA systems, this paper presents BiasFinder, an approach that can discover biased predictions in SA systems via metamorphic testing. A key feature of BiasFinder is the automatic curation of suitable templates from any given …
Exais: Executable Ai Semantics, Richard Schumi, Jun Sun
Exais: Executable Ai Semantics, Richard Schumi, Jun Sun
Research Collection School Of Computing and Information Systems
Neural networks can be regarded as a new programming paradigm, i.e., instead of building ever-more complex programs through (often informal) logical reasoning in the programmers' mind, complex 'AI' systems are built by optimising generic neural network models with big data. In this new paradigm, AI frameworks such as TensorFlow and PyTorch play a key role, which is as essential as the compiler for traditional programs. It is known that the lack of a proper semantics for programming languages (such as C), i.e., a correctness specification for compilers, has contributed to many problematic program behaviours and security issues. While it is …