11 The IERP® Monthly Newsletter September - November 2021
Besides these, human biases may be inadvertently programmed into ML systems. A few instances have already appeared, much to the embarrassment of the companies involved. Some of the incidents have even led to reputational damage, with customers alleging racial discrimination, gender insensitivity or occupational biases. This indicates an obvious ethical risk with ML, but the core of the problem here is due to human involvement. As it turns out, humans are the biggest AI risk to manage. Misuse and abuse of AI systems are rising, as evidenced by many realistic but fake audio and video recordings – “deepfakes’ – making the rounds.
Being a neutral tool, the application of ML depends on the intentions of the user. A major challenge therefore is to ensure that machines are used ethically. This can be done by following standards and guidelines issued by the authorities or standards bodies. Guidelines and governance are likely to increase as the technological environment evolves, and with the increasing uptake of AI and ML across industries. Risk professionals will have to keep up. “Risk professionals must become comfortable with the technology,” advised Ramesh. “Don’t be scared, or you will not be able to understand it.” He added that it was important to learn how to assess and manage it.
Reading the proper journals, asking the right questions and doing due diligence will go a long way towards helping risk managers understand the risks of AI and ML. It is essential that risk professionals undertake this, as properly-selected tools, knowledge and technology can help organisations create their own ML models. With the rapid advancement of ML, risk professionals needed to advance in parallel, to keep biases, the lack of skill and unethical behaviour from negatively affecting projects. They will need to keep themselves constantly updated, in order to give proper guidance on the assessment and management of new technologies.