97 Things About Ethics Everyone in Data Science Should Know

Chapter 76. How to Innovate Responsibly

Carole Piovesan

Partner and Cofounder, INQ Data Law

The phrase “responsible innovation” used to be oxymoronic. Rewind 15 years or so and you would have been mocked for using those two words together. The accepted ethos of innovation in the early 2000s was to “move fast and break things.” This ethos prioritized experimentation and exploration over caution and diligence. Boundless curiosity was embraced, fueled by an investment frenzy to fund the next big idea.

Fast-forward to the 2010s, however, and the consequences of that boundless curiosity could no longer be ignored. As we now collect all aspects of human behavior and analyze that data using sophisticated, predictive technologies such as AI, the fundamental implications of innovation are being scrutinized and tested.

Concerns over “responsible” innovation have invited a robust and sincere debate about technology in society (not new, mind you, but reinvigorated). Everything from the could, should, and would to the how, why, who, and then what is being asked about big data and AI. The very real social, political, economic, human rights, and legal implications of AI are legitimately being questioned, and demands for guardrails to protect society from unintended harms are proliferating.

But listen closely to those debates. Policymakers, civil society leaders, academics, entrepreneurs, ethicists, ...