The Electronic Frontier Foundation’s Jamie Williams and Lena Gunn have drawn up an annotated five-point list of questions to ask yourself before using a machine-learning algorithm to make predictions and guide outcomes.
The list draws heavily on two essential recent books on the subject: Cathy O'Neil’s Weapons of Math Destruction and Virginia Eubanks’s Automating Inequality, both of which are essential reads.
The list’s five questions are:
1. Will this algorithm influence—or serve as the basis of—decisions with the potential to negatively impact people’s lives?
2. Can the available data actually lead to a good outcome?
3. Is the algorithm fair?
4. How will the results (really) be used by humans?
5. Will people affected by these decisions have any influence over the system?
