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by Amit M. Joshi Published 8 February 2022 in Brain Circuits • 2 min read
In the ever-changing world of data analytics, AI and machine learning, the number of ethical questions is growing, including which data should be collected, how should it be handled, and how will it be disposed of? These issues may not be in the spotlight today, but you can be assured they will be at some point. When the next data usage scandal breaks, it will pay off to have the answers to the following questions.
Where are your red lines?
Be clear on the things your organization will never do. What are the firm boundaries you have set for use of your data?
Do your product managers know where your boundaries lie?
Ethics can just be decided on and rest at the top. Just as you should empower your people to make decisions at all levels, they need to know what their boundaries are – and this pertains to ethics as well.
Who guides your organization’s ethics?
Some organizations choose to appoint a Chief Ethics Officer. But regardless of whether you decide to give them a title, there should be someone in charge of managing ethical dilemmas within your organization. This person and their team should have the authority to make final decisions on ethical dilemmas and bear the final responsibility in the case of wrongdoing.
Just like having a crisis communications team in place, an ethics team will help you be prepared for when questions arise so that small issues don’t turn into giant challenges.
Further reading:Â
Profit or ethics? The false data dilemma by Amit Joshi
Professor of AI, Analytics and Marketing Strategy at IMD
Amit Joshi is Professor of AI, Analytics, and Marketing Strategy at IMD and Program Director of the Digital Strategy, Analytics & AI program, Generative AI for Business Sprint, and the Business Analytics for Leaders course. Â He specializes in helping organizations use artificial intelligence and develop their big data, analytics, and AI capabilities. An award-winning professor and researcher, he has extensive experience of AI and analytics-driven transformations in industries such as banking, fintech, retail, automotive, telecoms, and pharma.
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