Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential

Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective

“…We also need to consider the legal requirement for explicability of decision-making, and to prove that no customer has been unfairly discriminated through the use of technology ( Crosman, 2019 ). This is particularly – though not exclusively – likely to occur in cases where the AI system has autonomy to act, and when there are self-reinforcing feedback loops ( Canhoto & Clear, 2020 ), as well as when the algorithm is used for prediction rather than description ( Mittelstadt et al, 2016 ). Based on the description of AML monitoring at BANK, this means that supervised learning might be the least likely to breach these criteria, because it is used for description not prediction, and there are no feedback loops.…”

“…Artificial intelligence is an assemblage of technological components which collect, process and act on data in ways that simulate human intelligence ( Canhoto & Clear, 2020 ). AI can handle large volumes of data, including unstructured inputs such as images or speech, which makes it extremely relevant – or even essential – in the age of Big Data ( Kietzmann, Paschen, & Treen, 2018 ).…”

“…Once data have been processed through the machine learning algorithm, the system produces an output, which may vary in terms of type and autonomy from human intervention ( Canhoto & Clear, 2020 ). The examples of machine learning that tend to be featured in the media are those where the system has autonomy to act on the basis of the results of the computational process – such as steering a car without human intervention ( Goodall, 2016 ).…”

Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development

“…Human experts often provide these labels and thus provide the algorithm with the ground truth. To replicate human decisions or to make predictions, the algorithm learns patterns from the labeled data and develops rules, which can be applied for future instances for the same problem (Canhoto and Clear 2020). In contrast, in unsupervised ML, only input data are given, and the model learns patterns from the data without a priori labeling (Murphy 2012).…”

“…In contrast, in unsupervised ML, only input data are given, and the model learns patterns from the data without a priori labeling (Murphy 2012). Unsupervised ML algorithms capture the structural behaviors of variables in the input data for theme analysis or grouping data (Canhoto and Clear 2020). Finally, reinforcement learning, as a separate group of methods, is not based on fixed input/output data.…”