 Live from the MIT campus in Cambridge, Massachusetts, it's theCUBE, covering the 12th annual MIT Chief Data Officer and Information Quality Symposium, brought to you by SiliconANGLE Media. Welcome back to theCUBE's coverage of MIT CDOIQ here in Cambridge, Massachusetts. I'm your host, Rebecca Knight, along with my co-host, Peter Burris. We're joined by Ilana Goldman. She is the Manager of Artificial Intelligence Accelerator at PWC, based in Los Angeles. Thanks so much for coming on the show. Thank you for having me. So I know you were on the main stage giving a presentation, really talking about fears, unfounded or not, about how artificial intelligence will change the way companies do business. Lay out the problem for us to tell our viewers a little bit about how you see the landscape right now. Yeah, so I think, and we've really all experienced this that we're generating more data than we ever have in the past. So there's all this data coming in. A few years ago, that was the hot topic, big data, that big data's coming in. How are we going to harness big data? And big data coupled with this increase in computing power has really enabled us to build stronger models that can provide more predictive power for a variety of use cases. So this is a good thing. The problem is that we're seeing these really cool models come out that are black box. They're very difficult to understand how they're making decisions, and that's not just for us as end users, but also developers. We don't really know 100% why some models are making the decisions that they are. And that can be a problem for auditing. It can be a problem for regulation if that comes into play as end users for us to trust the model. Comes down to the use case, so why we're building these models. But ultimately, we want to ensure that we're building models responsibly so that the models are in line with our mission as businesses and they also don't do any unintended harm. And so because of that, we need some additional layers to protect ourselves. We need to build explainability into models. So you said two really interesting things. Let's take one and then the other. We need to better understand how we build models and we need to do a better job of articulating what those models are. Let's start with the building of models. What does it mean to do a better job of building models? Where are we in the adoption of better? So I think right now we're at the point where we just have a lot of data and we're very excited about it and we want to throw it into whatever models we can and see what we can get that has the best performance. But we need to take a step back and look at the data that we're using. Is the data biased? Does the data match what we see in the real world? Do we have a variety of opinions in both the data collection process and also the model design process? Diversity is not just important for opinions in a room but it's also important for models. So we need to take a step back and make sure that we have that covered. Once we're sure that we have data that's sufficient for our use case and the bias isn't there or the bias is there to the extent that we want it to be, then we can go forward and build these better models. So I think we're at the point where we're really excited and we're seeing what we can do but businesses are starting to take a step back and see how they can do that better. No, the one B, that is, and the tooling. Where is the tooling? The tooling, if you follow any of the literature, you see new publications come out sometimes every minute of the different applications for these really advanced models. Some of the hottest models on the market today are deep learning models and reinforcement learning models. They may not have an application for some businesses yet but they definitely are building those types of applications. So the techniques themselves are continuing to advance and I expect them to continue to do so, mostly because the data is there and the processing power is there and there's so much investment coming in from various government institutions and governments in these types of models. And the way typically these things work is the techniques and the knowledge of the techniques advance and then we turn them into tools. So the tools are lagging a little bit still behind the techniques but it's catching up. Would you agree? I would agree with that. Just because commercial tools can't keep up with the pace of the academic environment, we wouldn't really expect them to but once you've invested in a tool you wanna try and improve that tool rather than reformat that tool with the best technique that came out yesterday. So there is some kind of iteration that will continue to happen to make sure that our commercially available tools match what we see in the academic space. So the second question is, now we've got the model, how do we declare the model? What is the state of the art in articulating metadata? What the model does? What its issues are? How are we doing a better job and what can we do better to characterize these models so they can be more applicable while at the same time maintaining the fidelity that was originally intended and embedded? The first step is identifying your use case. To the extent to which we want to explain the model really is dependent on this use case. For instance, if you have a model that is going to be navigating a self-driving car you probably wanna have a lot more rigor around how that model is developed than with a model that targets mailers. There's a lot of middle ground there and most of the business applications fall into that middle ground but there's still business risks that need to be considered. So to the extent to which we can clearly articulate and define the use case for an AI application that will help inform what level of explainability or interpretability we need. So are you thinking in terms of what it means? How do we successfully define use cases? Do you have templates that you're using that PWC or other approaches to ensure that you get the rigor in the definition or the characterization of the model that then can be applied both to a lesser, who are you mailing versus a life and death situation like is the car behaving the way it's expected to? And yet the mailing, we have the example, the very famous target example that outed a young teenage girl who was pregnant before. So these can have real life implications. And they can. They can. But that's a very rare instance, right? And you could also argue that that's not the same as missing a stop sign and potentially injuring someone in a car. So there are always going to be extremes but usually when we think about use cases we think about criticality which is the extent to which someone could be harmed and vulnerability which is the willingness for a person and user to accept a model and the decision that it makes. A high vulnerability use case could be like there, a few years ago I was, or a year ago I was talking to a professor at UCSD University of California, San Diego and he was talking to a medical devices company that manufactures devices for monitoring your blood sugar levels. So this could be a high vulnerability case if you have an incorrect reading. Someone's life could be in danger. This medical device was intended to read the blood sugar levels by non-invasive means just by scanning your skin. But the metric that was used to calculate this blood sugar was correct. It just wasn't the same that an end user was expecting. Because that didn't match, these end users did not accept this device even though it did operate very well. They abandoned it. They abandoned it. It didn't sell. What this comes down to is this is a high vulnerability case. People want to make sure that their lives, the lives of their kids, whoever's using this device is in good hands and if they feel like they can't trust it they're not going to use it. So the use case I do believe is very important and when we think about use cases we think of them on those two metrics, vulnerability and criticality. Vulnerability and criticality. And we're always evolving our thinking on this but this is our current thinking. Where are we in terms of the way in which, from your perspective, and the way in which corporations are viewing this? Are they, do you believe that they have the right amount of trepidation? Are they too trepidatious when it comes to this? What is the mindset? Speaking in general terms. I think everybody's still trying to figure it out. What I've been seeing personally is businesses taking a step back and saying, we've been building all these proof of concepts or deploying these pilots but we haven't done anything enterprise wide yet. Generally speaking. So what we're seeing are businesses coming back and saying before we go any further we need a comprehensive AI strategy. We need something central within our organization that tells us that defines how we're going to move forward and build these future tools so that we're not then moving backwards and making sure everything aligns. So I think this is really the stage that businesses are in and once they have a central AI strategy I think it becomes much easier to evaluate regulatory risks or anything like that just because it all reports back to a central entity. But I want to build on that notion because generally we agree. I want to build on that notion though. We are doing a good job in the technology we're going to talk about how we're distributing processing power. We're doing a good job of describing how we're distributing data and we're even doing a good job of describing how we're distributing known process. We're not doing a really good job of what we call systems of agency how we're distributing agency in other words the degree to which a model is made responsible for acting on behalf of the brand. Now in some domains medical devices there is a very clear relationship between what the device says it's going to do and who ultimately decided to be who's culpable. But in the software world we use copyright law. And copyright law is a speech act. How do we ensure that this notion of agency we're distributing agency appropriately so that when something is being done on behalf of the brand that there is a lineage of culpability a lineage of obligations associated with that. Where are we. I think we're right now we're still and I can't speak for most organizations just my personal experience. I think that most of that the companies or the instances I've seen we're still really early on in that because AI is different from traditional software but it still needs to be audited. So we're at the stage where we're taking a step back and we're saying we know we need a mechanism to monitor and audit our AI. We need controls around this. We need to accurately provide auditing and assurance around our AI applications but we recognize it's different from traditional software for a variety of reasons. AI is adaptive it's not static it's not like traditional software. It's probable as to not categorical. Exactly. So there are a lot of other externalities that need to be considered and so this is something that a lot of businesses are thinking about. One of the reasons why having a central AI strategy is really important is that you can also define a central controls framework. Some type of centralized assurance and auditing process that's mandated from a high level of the organization that everybody will follow and that's really the best way to get AI widely adopted because otherwise I think we'll be seeing a lot of challenges. So I got one more question and one question I have is if you look out in the next three years as someone who is working with customers working with academics trying to map, trying to match the need to the expertise what is the next conversation that's going to pop to the top of the stack in this world and say within the next two years? What will we be talking about next year or in five years from now too at the next CDO IQ? I think this topic of explainability will persist because I don't think we will necessarily take all the boxes in the next year. I think we'll uncover new challenges and we'll have to think about new ways to explain how models are operating. Other than that I think customers will want to see more transparency in the process itself. So not just the model and how it's making its decisions but what data is feeding into that. How are you using my data to impact how a model is making decisions on my behalf? What is feeding into my credit score and what can I do to improve it? Those are the types of conversations I think we'll be having in the next two years for sure. Great, well Alana thanks so much for coming on theCUBE it was great having you. Thank you for having me. I'm Rebecca Knight for Peter Burris we will have more from MIT Chief Data Officer Symposium 2018 just after this.