Data and Ethics: At the Beginning of the Conversation
Questions are being raised about the ethical treatment of the data that powers the advances in AI. In fact, data ethics was the focus of Collibra’s NYC Data Citizens Meetup held in early November.
More than 50 industry leaders came together to explore the new area of data ethics. Organizations are recognizing that the poor handling of data, deliberate or not, could lead to negative outcomes for both the organizations and individuals to whom the data refers.
Propelling these concerns is the rise of AI because it promises to deliver considerable economic gains–IDC estimates that $52 billion will be spent on it annually by 2021. Companies around the globe are exploring ways in which they can transform their data through AI solutions to reduce costs, meet regulatory demands, deliver an enhanced customer experience, and innovate.
Getting it (the capturing, processing, managing and storing of data) right is not as straightforward. The Cambridge Analytica scandal may have 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. 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 value overnight.
Five key steps that organizations should consider taking today to promote a more ethical culture around their data resources include:
- Integrating data ethics into your culture and code of conduct
- Setting up an ethics review board to oversee AI and training data being consumed
- Establishing an ethics learning program focusing on data professionals
- Publishing a public statement on intentions of AI and data usage
- Updating whistleblower policies to include data misconduct
In summary, data ethics is a relatively new field that is gaining more attention with the development of AI. Organizations, particularly boards and senior management, should consider next steps in creating a data ethics approach over the coming year as a priority.