adapting to environmental conditions , are necessary when it comes to implementing ethical business AI .
“ The ability to understand and explain how the decisions have been arrived at by a model is a huge element of transparency ,” Prathiba highlights .
In order to keep AI systems ethical , it has been suggested that having a human at the centre is essential to validate AI-made decisions . Any solutions that are generated are – in theory – more robust , which should inspire greater trust .
Prathiba says : “ AI processes should be able to combine model outcomes and business rules to embed social aspects of the data . It ’ s important to build fail-safes and adopt redundancy measures to ensure critical decisions are reviewed or overridden by human operators when necessary .
“ Ethical inquiry is important and for every AI use case we need to reflect on the guiding principles and above all ask ourselves these three questions : For what purpose ? To what end ? For whom might this fail ?”
Businesses that develop or harness AI will also benefit from a chain of accountability that extends between individuals , companies and systems .
“ Models need to run on hardware and be served by software , but we should hold the institutions that own these systems accountable for the behaviour of those systems ,” Dan concludes . “ This incentivises these people and institutions to create the appropriate guardrails .”
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