ABSTRACT
Artificial intelligence (AI) researchers call it “the uncanny valley”: numerous studies indicate that people fear and distrust machines that seem nearly human, but not quite. There are many theories about this, but nothing conclusive. In information systems conflicts, the difficulty is even greater. Douglas W. Hubbard, a well-known expert on measurement and risk assessment, declares, “The biggest risk in cybersecurity is not measuring cybersecurity risk correctly”. In big data analytics, much efficiency in input processing can be obtained by “sharding” the data, breaking the data into more manageable smaller chunks. In some cases, this might consist of something sensible, like separating the data set into geographic regions, in which there is little interaction among data elements in different regions. Data analytics also has much to contribute to AI/ML on the input side. AI/ML systems, whether used for data inference, control, or decision-making, are voracious consumers of data. Indeed, they cannot function without data.
