When we first joined AWS AI/ML as Amazon Scholars over three years ago, we had already been doing scientific research in the area now known as responsible AI for a while. We had authored a number of papers proposing mathematical definitions of fairness and machine learning (ML) training algorithms enforcing them, as well as methods for ensuring strong notions of privacy in trained models. We were well versed in adjacent subjects like explainability and robustness and were generally denizens of the emerging responsible-AI research community. We even wrote a general-audience book on these topics to try to explain their importance to a broader audience.
So we were excited to come to AWS in 2020 to apply our expertise and methodologies to the ongoing responsible-AI efforts here — or at least, that was our mindset on arrival. But our journey has taken us somewhere quite different, somewhere more consequential and interesting than we expected. It’s not that the definitions and algorithms we knew from the research world aren’t relevant — they are — but rather that they are only one component of a complex AI workstream comprising data, models, services, enterprise customers, and end-users. It’s also a workstream in which AWS is uniquely situated due to its pioneering role in cloud computing generally and cloud AI services specifically.
Our time here has revealed to us some practical challenges of which we were previously unaware. These include diverse data modalities, “last mile” effects with customers and end-users, and the recent emergence of AI activism. Like many good interactions between industry and academia, what we’ve learned at AWS has altered our research agenda in healthy ways. In case it’s useful to anyone else trying to parse the burgeoning responsible-AI landscape (especially in the generative-AI era), we thought we’d detail some of our experiences here.
This article was written by Aaron Roth and Michael Kearns. To read the full story, please visit Amazon Science.