While machine learning systems and algorithms are theoretically more objective than humans, this does not necessarily result in different or fairer results. Image: Dreamstime/TNS While machine learning systems and algorithms are theoretically more objective than humans, this does not necessarily result in different or fairer results. Image: Dreamstime/TNS
While machine learning systems and algorithms are theoretically more objective than humans, this does not necessarily result in different or fairer results. Image: Dreamstime/TNS
Mohan Jayaraman
Opinion

Opinion

Mohan Jayaraman

Machine learning systems, fair or biased, reflect our moral standards

  • While machine learning systems and algorithms are theoretically more objective than humans, this does not necessarily result in different or fairer results
  • There needs to be a concerted effort by regulators, governments and practitioners to ensure the technology stems inequities rather than perpetuates them

While machine learning systems and algorithms are theoretically more objective than humans, this does not necessarily result in different or fairer results. Image: Dreamstime/TNS While machine learning systems and algorithms are theoretically more objective than humans, this does not necessarily result in different or fairer results. Image: Dreamstime/TNS
While machine learning systems and algorithms are theoretically more objective than humans, this does not necessarily result in different or fairer results. Image: Dreamstime/TNS
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Mohan Jayaraman

Mohan Jayaraman

Mohan Jayaraman is regional managing director of decision analytics & business information, at Experian Asia Pacific. With over 20 years of experience in banking, Mohan is in charge of driving greater collaboration across Experian’s product, consultant and analytics businesses across the region. Mohan has also been a part of the Experian CSR team, focused on directing the innovation capabilities within the organisation to solve social issues related to financial inclusion and consumer education.