I think the responses to this can be broken down into a 2x2 matrix: level of concern vs. understanding of technology.
1) Don't understand ML; not concerned - "I have nothing to hide."
2) Don't understand ML; concerned - "I bought this device and now people are spying on me!"
3) Understand ML; not concerned - "Of course, Google needs to label its training data."
4) Understand ML; concerned - "How can we train models/collect data in an ethical way?"
To me, category 3 is the most dangerous. Tech workers have a responsibility not just understand the technologies that they work with, but also educate themselves on the societal implications of those technologies. And as others have pointed out, this extends beyond home speakers to any voice-enabled device in general.
In conversations about this with engineers the response I've gotten is essentially: "Just trust that we [Google/Amazon/etc.] handle the data correctly." This is worrying.
1) Don't understand ML; not concerned - "I have nothing to hide."
2) Don't understand ML; concerned - "I bought this device and now people are spying on me!"
3) Understand ML; not concerned - "Of course, Google needs to label its training data."
4) Understand ML; concerned - "How can we train models/collect data in an ethical way?"
To me, category 3 is the most dangerous. Tech workers have a responsibility not just understand the technologies that they work with, but also educate themselves on the societal implications of those technologies. And as others have pointed out, this extends beyond home speakers to any voice-enabled device in general.
In conversations about this with engineers the response I've gotten is essentially: "Just trust that we [Google/Amazon/etc.] handle the data correctly." This is worrying.