As much as AI has the ability to change the world in very positive ways, it also can be incredibly destructive. Sean McGregor knows this well, as he is currently developing the Partnership on AI’s AI Incident Database, a searchable collection of news articles that covers questionable use, failures, and other incidents that affect people when AI solutions are poorly designed.  

On this episode of Experiencing Data, Sean takes us through his notable work around using machine learning in the domain of fire suppression, and how human-centered design is critical to ensuring these decision support solutions are actually used and trusted by the users. We also covered the social implications of new decision-making tools leveraging AI, and:

• Sean's focus on ensuring his models and interfaces were interpretable by users when designing his fire-suppression system and why this was important. (0:51)

“As soon as you enter into the decision-making space, you’re really tearing at the social fabric in a way that hasn’t been done before. And that’s where analytics and the systems we’re talking about right now are really critical because that is the middle point that we have to meet in and to find those points of compromise.” - Sean (12:28)

“I think that a lot of times, unfortunately, the assumption [in data science is], ‘Well if you don’t understand it, that’s not my problem. That’s your problem, and you need to learn it.’ But my feeling is, ‘Well, do you want your work to matter or not? Because if no one’s using it, then it effectively doesn’t exist.’” - Brian (17:41)

“[The AI Incident Database is] a collection of largely news articles [about] bad things that have happened from AI [so we can] try and prevent history from repeating itself, and [understand] more of [the] unintended and bad consequences from AI....” - Sean (19:44)

“Human-centered design will prevent a great many of the incidents [of AI deployment harm] that have and are being ingested in the database. It’s not a hundred percent thing. Even in human-centered design, there’s going to be an absence of imagination, or at least an inadequacy of imagination for how these things go wrong because intelligent systems — as they are currently constituted — are just tremendously bad at the open-world, open-set problem.” - Sean (22:21)

“It’s worth the time and effort to work with the people that are going to be the proponents of the system in the organization — the ones that assure adoption — to kind of move them through the wireframes and examples and things that at the end of the engineering effort you believe are going to be possible. … Sometimes you have to know the nature of the data and what inferences can be delivered on the basis of it, but really not jumping into the principal engineering effort until you adopt and agree to what the target is. [This] is incredibly important and very often overlooked.” - Sean (31:36)

• seanbmcgregor.com: https://seanbmcgregor.com

Twitter: https://twitter.com/seanmcgregor