TECHNOLOGY
Accenture examines “ what is fair ” to tackle AI bias There is still a lot to be done by people as well as machines . Identifying and prioritising which biases to address requires experts from a wide range of disciplines to develop and implement technical improvements , says Sue Tripathi , Managing Director , Global GTM , Data , AI at Accenture .
“ To complicate matters , making ‘ fair ’ decisions to minimise bias implies there is a uniform and standard way in defining ‘ fairness ’, which is not the case ,” explains Tripathi . “ How then do we define ‘ fairness ’ and also measure it ?”
Those involved in codifying definitions of fairness and attempting to provide ‘ fairness ’ metrics highlight the complexities involved . Various academic centres of AI , highlight cases where determining the “ fair ” percentage in their algorithms may not actually represent the real world in terms of pay or health equity practices , for example .
“ Should an organisation set different decision thresholds for different groups , based on race , gender , social , and economic factors ?” asks Tripathi . “ Is there a single universal definition of fairness or metrics that can be applied ?”
“DECREASING BIAS OVER SOME PROTECTED VARIABLE MAY INCREASE BIAS OVER ANOTHER . THIS IS WHY INCLUSIVENESS , TRANSPARENCY AND EXPLAINABILITY ARE FUNDAMENTAL ”
FRANCESCA ROSSI IBM FELLOW AND AI ETHICS GLOBAL LEADER
Democratise data to do away with AI bias , says Alteryx Upskilling in-house experts in data literacy and analytics is also a crucial preventative measure to avoid the pitfalls and ethical issues around deploying AI , explains Alteryx ’ s Jacobson .
“ Cloud-based and on-premise platforms with drag-and-drop – or no-code / low-code – and automated ML capabilities will shorten the learning curve for many users ,” he says . “ Additionally ,
80 October 2022