With artificial intelligence (AI) and machine learning (ML) driving business innovation and operational efficiencies, understanding data ethics is paramount to leveraging AI and ML for successful results.
The promise of AI & ML
AI promises to deliver considerable business benefit – IDC estimates that $52 billion will be spent on it annually by 2021. Companies around the globe are exploring ways in which they can use the right data to feed into their AI solutions to reduce costs, meet regulatory demands, deliver an enhanced customer experience, and innovate. Getting it right, the capturing, processing, managing and storing of data, is not as straightforward, however.
The Cambridge Analytica scandal brought the issue of data ethics to the headlines, particularly in the context of social media platforms, but other concerns are being raised within the technology industry that are more subtle. For example, can an AI machine learn morality and just which set of morals should it learn? Morals differ dramatically from culture to culture, as a recent Massachusetts Institute of Technology (MIT) experiment showed. Others are asking if the conscious and unconscious biases of those who assemble an AI solution will be then found baked into that solution.
Think tanks are focusing on defining what the ethical treatment of data should look like. The Information Accountability Foundation recently published a paper that asks probing questions about risks and benefits around data ethics within organizations. The Center for Information Policy Leadership has published a report that also examines data ethics issues.
Google recently issued its own AI principles and we should expect other technology companies to follow suit with their own data ethics policies over the next year or two. Investors may not be asking for data ethics policies today, but they will be soon. The reputational risk from a data ethics failure can destroy considerable shareholder

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