1 00:00:00,000 --> 00:00:02,280 All right, you guys send us a ton of stuff on AI, 2 00:00:02,280 --> 00:00:05,560 and we are zeroing in on the AI RMF Playbook. 3 00:00:05,560 --> 00:00:06,880 It's all about risk management. 4 00:00:06,880 --> 00:00:08,960 So think of this like a deep dive. 5 00:00:08,960 --> 00:00:12,280 Getting you the info you absolutely need on this. 6 00:00:12,280 --> 00:00:15,520 It's pretty amazing how the Playbook stresses 7 00:00:15,520 --> 00:00:17,800 building trustworthy AI. 8 00:00:17,800 --> 00:00:19,560 And we're talking right for the beginning, 9 00:00:19,560 --> 00:00:21,480 even from the initial idea, 10 00:00:21,480 --> 00:00:26,400 not just avoiding harm, but reliable, safe, fair systems. 11 00:00:26,400 --> 00:00:28,520 And it goes way beyond just data breaches. 12 00:00:28,520 --> 00:00:31,200 Like they really get into the potential for harm 13 00:00:31,200 --> 00:00:33,240 to people, communities, even the planet. 14 00:00:33,240 --> 00:00:35,800 It's pretty wild to think how AI systems 15 00:00:35,800 --> 00:00:36,800 could impact everything. 16 00:00:36,800 --> 00:00:37,360 Exactly. 17 00:00:37,360 --> 00:00:39,520 AI isn't operating in isolation. 18 00:00:39,520 --> 00:00:41,360 It's part of our complex world. 19 00:00:41,360 --> 00:00:42,800 And the effects, the ripple effects, 20 00:00:42,800 --> 00:00:43,880 you might not see them coming. 21 00:00:43,880 --> 00:00:46,360 That's why they emphasize understanding context. 22 00:00:46,360 --> 00:00:47,720 Like what are the laws and regulations? 23 00:00:47,720 --> 00:00:49,360 Who's going to use it and what do they expect? 24 00:00:49,360 --> 00:00:52,360 OK, so we're laying the groundwork, risk tolerance context. 25 00:00:52,360 --> 00:00:53,720 But what I found super interesting 26 00:00:53,720 --> 00:00:55,360 was the focus on a diverse team. 27 00:00:55,360 --> 00:00:57,120 I mean, yeah, representation is important, 28 00:00:57,120 --> 00:00:59,400 but how does that actually help manage risk? 29 00:00:59,400 --> 00:01:01,680 It's not just about who's at the table, 30 00:01:01,680 --> 00:01:04,120 but the different viewpoints they bring. 31 00:01:04,120 --> 00:01:07,120 Imagine if you only had engineers designing an AI 32 00:01:07,120 --> 00:01:08,440 for health care. 33 00:01:08,440 --> 00:01:10,400 They might be amazing technically. 34 00:01:10,400 --> 00:01:11,960 But what about the ethical side? 35 00:01:11,960 --> 00:01:13,920 What about the patient experience? 36 00:01:13,920 --> 00:01:16,720 And even the biases they could bring their own biases in 37 00:01:16,720 --> 00:01:18,560 without even realizing it. 38 00:01:18,560 --> 00:01:20,920 A team with people from all different backgrounds 39 00:01:20,920 --> 00:01:22,840 can help avoid those blind spots. 40 00:01:22,840 --> 00:01:25,560 So it's like having different inspectors on a bridge project, 41 00:01:25,560 --> 00:01:27,440 each looking for different weaknesses. 42 00:01:27,440 --> 00:01:31,120 And the playbook even mentions interdisciplinary collaboration. 43 00:01:31,120 --> 00:01:32,840 That's when it gets really interesting. 44 00:01:32,840 --> 00:01:34,480 What if you have data scientists, 45 00:01:34,480 --> 00:01:37,240 ethicists, sociologists, maybe even artists, 46 00:01:37,240 --> 00:01:38,640 all working together? 47 00:01:38,640 --> 00:01:40,040 Bringing all those ideas together 48 00:01:40,040 --> 00:01:43,200 can create some really strong and responsible systems. 49 00:01:43,200 --> 00:01:45,320 OK, so diverse team, check. 50 00:01:45,320 --> 00:01:47,080 But then we have to actually figure out 51 00:01:47,080 --> 00:01:48,400 what the risks are, right? 52 00:01:48,400 --> 00:01:50,120 They call this mapping the risks. 53 00:01:50,120 --> 00:01:52,280 And it's not just technical glitches. 54 00:01:52,280 --> 00:01:53,360 You got it. 55 00:01:53,360 --> 00:01:54,440 You have to consider everything. 56 00:01:54,440 --> 00:01:55,600 What's the AI supposed to do? 57 00:01:55,600 --> 00:01:57,360 How could it be misused? 58 00:01:57,360 --> 00:02:00,640 And then there's this wild concept of emergent properties. 59 00:02:00,640 --> 00:02:02,960 It's like, even if you design it perfectly, 60 00:02:02,960 --> 00:02:05,520 it can still develop capabilities you never expected. 61 00:02:05,520 --> 00:02:08,040 Wait, so even at the best intentions in all the testing, 62 00:02:08,040 --> 00:02:09,880 an AI could like, go row. 63 00:02:09,880 --> 00:02:10,880 That's kind of scary. 64 00:02:10,880 --> 00:02:13,240 It shows why you can't just set it up and forget about it. 65 00:02:13,240 --> 00:02:15,800 You need to be constantly vigilant. 66 00:02:15,800 --> 00:02:17,080 And that leads us to the next step. 67 00:02:17,080 --> 00:02:18,040 Tons of testing. 68 00:02:18,040 --> 00:02:19,640 They call it TEVV testing. 69 00:02:19,640 --> 00:02:22,160 Evaluation, validation, and verification. 70 00:02:22,160 --> 00:02:24,040 Making sure it does what it's supposed to. 71 00:02:24,040 --> 00:02:25,520 And dealing with those risks. 72 00:02:25,520 --> 00:02:27,200 And I'm guessing bias is a big thing 73 00:02:27,200 --> 00:02:28,760 they're looking for in these tests. 74 00:02:28,760 --> 00:02:30,440 The playbook mentions a few kinds. 75 00:02:30,440 --> 00:02:33,560 Systemic, computational, human cognitive. 76 00:02:33,560 --> 00:02:35,800 Each one needs a different approach. 77 00:02:35,800 --> 00:02:38,800 Systemic bias, it's like inequalities from society 78 00:02:38,800 --> 00:02:39,760 creeping in. 79 00:02:39,760 --> 00:02:42,640 Computational bias comes from the algorithms or the data 80 00:02:42,640 --> 00:02:43,760 itself. 81 00:02:43,760 --> 00:02:46,640 And then human cognitive, that's our own assumptions 82 00:02:46,640 --> 00:02:47,920 messing things up. 83 00:02:47,920 --> 00:02:50,280 Even with a perfectly unbiased AI, 84 00:02:50,280 --> 00:02:52,240 we can introduce bias in how we use it. 85 00:02:52,240 --> 00:02:53,200 It's really tricky. 86 00:02:53,200 --> 00:02:54,760 So building it right isn't enough. 87 00:02:54,760 --> 00:02:56,360 You have to use it right too. 88 00:02:56,360 --> 00:02:59,320 And then the playbook says monitoring has to be ongoing. 89 00:02:59,320 --> 00:02:59,920 Right. 90 00:02:59,920 --> 00:03:02,480 And AI's performance can change over time. 91 00:03:02,480 --> 00:03:06,320 Imagine a self-driving car that learned in sunny California. 92 00:03:06,320 --> 00:03:08,840 Then you put it in a snowy New England winter. 93 00:03:08,840 --> 00:03:10,520 It might not be able to handle it. 94 00:03:10,520 --> 00:03:13,640 That data it was trained on, it doesn't match the real world 95 00:03:13,640 --> 00:03:14,480 anymore. 96 00:03:14,480 --> 00:03:15,600 That can cause problems. 97 00:03:15,600 --> 00:03:16,800 That's a good analogy. 98 00:03:16,800 --> 00:03:20,720 So diverse team, map the risks, constant testing 99 00:03:20,720 --> 00:03:22,000 and monitoring. 100 00:03:22,000 --> 00:03:24,120 What if a risk actually shows up? 101 00:03:24,120 --> 00:03:26,520 If that's where managing and mitigating come in, 102 00:03:26,520 --> 00:03:28,320 you need a game plan. 103 00:03:28,320 --> 00:03:30,560 The playbook talks about a different option. 104 00:03:30,560 --> 00:03:33,480 Mitigate the risk, transfer it, avoid it, even accept it 105 00:03:33,480 --> 00:03:34,320 in some cases. 106 00:03:34,320 --> 00:03:36,520 So it's not always about getting rid of risk completely. 107 00:03:36,520 --> 00:03:39,320 It's about making choices based on what your organization can 108 00:03:39,320 --> 00:03:39,920 handle. 109 00:03:39,920 --> 00:03:40,640 Exactly. 110 00:03:40,640 --> 00:03:42,360 It's like with a bridge, you wouldn't 111 00:03:42,360 --> 00:03:44,160 design it to withstand a hurricane if you're 112 00:03:44,160 --> 00:03:45,400 building it in the desert. 113 00:03:45,400 --> 00:03:46,520 Makes sense. 114 00:03:46,520 --> 00:03:48,320 But here's something that surprised me. 115 00:03:48,320 --> 00:03:50,440 Even switching off an AI system, they say, 116 00:03:50,440 --> 00:03:52,080 that needs to be done carefully. 117 00:03:52,080 --> 00:03:54,640 Think about demolishing a building. 118 00:03:54,640 --> 00:03:56,400 You wouldn't just start swinging a wrecking ball 119 00:03:56,400 --> 00:03:58,480 without thinking about what's around it, right? 120 00:03:58,480 --> 00:04:01,760 Or getting rid of hazardous materials properly. 121 00:04:01,760 --> 00:04:05,840 With AI, you might need to keep some data for legal reasons 122 00:04:05,840 --> 00:04:08,240 or make sure it's disconnected securely 123 00:04:08,240 --> 00:04:10,400 so you don't create vulnerabilities. 124 00:04:10,400 --> 00:04:12,960 So even turning it off requires planning. 125 00:04:12,960 --> 00:04:15,320 There's a lot more to this than I thought. 126 00:04:15,320 --> 00:04:17,720 And then you've got another layer, all the risks that come 127 00:04:17,720 --> 00:04:20,520 with using AI tech from someone else. 128 00:04:20,520 --> 00:04:22,840 Yeah, tons of companies aren't building their own AI. 129 00:04:22,840 --> 00:04:25,480 They're using stuff made by other companies. 130 00:04:25,480 --> 00:04:27,560 What does the playbook say about that? 131 00:04:27,560 --> 00:04:29,280 It's all about due diligence. 132 00:04:29,280 --> 00:04:31,680 You wouldn't just let anyone babysit your kids, right? 133 00:04:31,680 --> 00:04:33,840 You'd want to check them out, make sure they're experienced, 134 00:04:33,840 --> 00:04:35,600 see if their values align with yours. 135 00:04:35,600 --> 00:04:38,160 Same thing with these third party AI providers. 136 00:04:38,160 --> 00:04:40,600 You need to really understand their process, where 137 00:04:40,600 --> 00:04:43,280 their data comes from, how they handle risk themselves. 138 00:04:43,280 --> 00:04:45,640 So like a background check for AI. 139 00:04:45,640 --> 00:04:48,240 But what about those pre-trained models we talked about? 140 00:04:48,240 --> 00:04:50,160 Those are kind of a black box. 141 00:04:50,160 --> 00:04:53,920 Pre-trained models, they're like these super powerful tools, 142 00:04:53,920 --> 00:04:56,960 packed with knowledge from massive data sets. 143 00:04:56,960 --> 00:04:59,120 They can be incredibly useful, but it 144 00:04:59,120 --> 00:05:01,200 can be hard to figure out where they came from, 145 00:05:01,200 --> 00:05:02,960 what data was used to train them, if there's 146 00:05:02,960 --> 00:05:04,400 any hidden bias in there. 147 00:05:04,400 --> 00:05:07,320 It's like buying a used car with a souped up engine. 148 00:05:07,320 --> 00:05:09,920 You know it's powerful, but who knows what the previous owner 149 00:05:09,920 --> 00:05:10,840 was doing with it? 150 00:05:10,840 --> 00:05:11,680 That's risky. 151 00:05:11,680 --> 00:05:12,600 Exactly. 152 00:05:12,600 --> 00:05:14,160 That's why a whole section of the playbook 153 00:05:14,160 --> 00:05:17,560 is dedicated to managing those third party risks. 154 00:05:17,560 --> 00:05:20,040 Do your homework and keep a close eye on things. 155 00:05:20,040 --> 00:05:21,560 OK, so we've covered a lot here. 156 00:05:21,560 --> 00:05:23,960 It's pretty clear that managing AI risk is a big deal. 157 00:05:23,960 --> 00:05:26,520 It's complicated, and it's always changing. 158 00:05:26,520 --> 00:05:27,560 It is. 159 00:05:27,560 --> 00:05:29,360 But with all this talk about risk, 160 00:05:29,360 --> 00:05:31,600 it's important to remember the good stuff too, 161 00:05:31,600 --> 00:05:33,280 the potential benefits. 162 00:05:33,280 --> 00:05:35,400 This playbook doesn't ignore that. 163 00:05:35,400 --> 00:05:38,880 AI has the potential to totally transform industries, 164 00:05:38,880 --> 00:05:42,080 solve global problems, even create incredible things 165 00:05:42,080 --> 00:05:43,400 we can't even imagine yet. 166 00:05:43,400 --> 00:05:46,640 We're talking like curing diseases, tackling climate 167 00:05:46,640 --> 00:05:49,000 change, maybe even writing the next hit song. 168 00:05:49,000 --> 00:05:50,200 Exactly. 169 00:05:50,200 --> 00:05:52,600 But the playbook makes it clear those potential benefits 170 00:05:52,600 --> 00:05:54,920 have to be balanced against the risks. 171 00:05:54,920 --> 00:05:58,640 So it's not a simple AI is good or AI is bad kind of thing. 172 00:05:58,640 --> 00:05:59,960 There's more to it than that. 173 00:05:59,960 --> 00:06:00,760 Absolutely. 174 00:06:00,760 --> 00:06:02,560 AI is a tool, and like any tool, 175 00:06:02,560 --> 00:06:04,120 it can be used for good or bad. 176 00:06:04,120 --> 00:06:06,520 The key is using it responsibly. 177 00:06:06,520 --> 00:06:09,520 And that brings us back to that idea we talked about before. 178 00:06:09,520 --> 00:06:10,840 Trust worthy AI. 179 00:06:10,840 --> 00:06:11,440 Right. 180 00:06:11,440 --> 00:06:13,840 Building that trust is crucial. 181 00:06:13,840 --> 00:06:15,440 And one thing that really struck me 182 00:06:15,440 --> 00:06:18,320 was the emphasis on accountability. 183 00:06:18,320 --> 00:06:21,160 Like who's responsible if things go wrong? 184 00:06:21,160 --> 00:06:22,960 That seems like a tricky situation. 185 00:06:22,960 --> 00:06:24,120 It doesn't have to be. 186 00:06:24,120 --> 00:06:25,520 Think about constructing a building. 187 00:06:25,520 --> 00:06:27,760 You've got your architects, your contractors, your inspectors, 188 00:06:27,760 --> 00:06:29,040 all playing their part. 189 00:06:29,040 --> 00:06:31,720 And if a wall collapses, you know who's responsible. 190 00:06:31,720 --> 00:06:32,000 Right. 191 00:06:32,000 --> 00:06:34,480 You have clear lines of responsibility. 192 00:06:34,480 --> 00:06:36,760 AI needs that same level of clarity. 193 00:06:36,760 --> 00:06:39,640 So it's not just having rules, but making sure everyone knows 194 00:06:39,640 --> 00:06:42,640 who's doing what and who takes the blame if something goes wrong. 195 00:06:42,640 --> 00:06:43,520 Precisely. 196 00:06:43,520 --> 00:06:46,120 And that starts with solid governance policies. 197 00:06:46,120 --> 00:06:48,440 Everyone's roles and responsibilities are spilled out, 198 00:06:48,440 --> 00:06:50,640 from the developers writing the code to the executives 199 00:06:50,640 --> 00:06:53,320 calling the shots to the people actually using the AI. 200 00:06:53,320 --> 00:06:53,760 OK. 201 00:06:53,760 --> 00:06:56,800 Clear roles, clear responsibilities. 202 00:06:56,800 --> 00:06:59,960 But how do you make sure everyone's on the same page? 203 00:06:59,960 --> 00:07:02,520 With AI, I imagine things can get lost in translation 204 00:07:02,520 --> 00:07:03,440 pretty easily. 205 00:07:03,440 --> 00:07:06,280 Communication and collaboration are essential. 206 00:07:06,280 --> 00:07:08,800 The people building it need to be talking to the people 207 00:07:08,800 --> 00:07:10,200 who will use it. 208 00:07:10,200 --> 00:07:12,240 And everyone needs to be in sync with the folks 209 00:07:12,240 --> 00:07:13,360 overseeing everything. 210 00:07:13,360 --> 00:07:14,440 Makes sense. 211 00:07:14,440 --> 00:07:16,960 Siloed teams can be a recipe for disaster, 212 00:07:16,960 --> 00:07:20,000 especially when you're dealing with something as powerful as AI. 213 00:07:20,000 --> 00:07:20,960 You got it. 214 00:07:20,960 --> 00:07:23,080 And to solidify that accountability, 215 00:07:23,080 --> 00:07:26,760 the playbook recommends this three lines of defense model. 216 00:07:26,760 --> 00:07:29,040 Three lines of defense sounds pretty serious. 217 00:07:29,040 --> 00:07:31,320 It's a pretty common framework for risk management, 218 00:07:31,320 --> 00:07:33,160 and it's actually quite logical. 219 00:07:33,160 --> 00:07:34,720 Think of it like a safety net. 220 00:07:34,720 --> 00:07:37,640 The first line is the team building and deploying the AI. 221 00:07:37,640 --> 00:07:40,400 They're responsible for catching any issues early on. 222 00:07:40,400 --> 00:07:43,120 The second line is an independent group, 223 00:07:43,120 --> 00:07:45,800 maybe a risk management team, or an ethics committee, 224 00:07:45,800 --> 00:07:47,760 adding an extra layer of scrutiny. 225 00:07:47,760 --> 00:07:49,880 And then the third line is an external audit, 226 00:07:49,880 --> 00:07:51,360 bringing in an outside perspective 227 00:07:51,360 --> 00:07:53,480 to evaluate the risks and the controls. 228 00:07:53,480 --> 00:07:55,840 So it's like checks and balances for AI, 229 00:07:55,840 --> 00:07:58,520 making sure everything's being developed and used the right way. 230 00:07:58,520 --> 00:07:59,480 Exactly. 231 00:07:59,480 --> 00:08:00,840 And it's not a one-time check. 232 00:08:00,840 --> 00:08:04,640 This is ongoing throughout the whole AI lifecycle, 233 00:08:04,640 --> 00:08:07,680 from the initial idea all the way to shutting it down. 234 00:08:07,680 --> 00:08:09,160 That's a lot of oversight. 235 00:08:09,160 --> 00:08:11,400 But I can see how it could help find problems 236 00:08:11,400 --> 00:08:13,280 before they become a huge mess. 237 00:08:13,280 --> 00:08:15,760 And that brings us to another important idea, 238 00:08:15,760 --> 00:08:17,760 proactive risk management. 239 00:08:17,760 --> 00:08:18,920 Proactive. 240 00:08:18,920 --> 00:08:21,240 So you're not just sitting around waiting for something bad 241 00:08:21,240 --> 00:08:22,560 to happen, you're trying to stop it 242 00:08:22,560 --> 00:08:23,960 from happening in the first place. 243 00:08:23,960 --> 00:08:25,040 You got it. 244 00:08:25,040 --> 00:08:29,320 The playbook is all about spotting potential problems early on 245 00:08:29,320 --> 00:08:31,280 and having a plan to deal with them. 246 00:08:31,280 --> 00:08:33,880 It's like preventative maintenance for your AI. 247 00:08:33,880 --> 00:08:36,160 That sounds a lot less stressful than dealing 248 00:08:36,160 --> 00:08:38,520 with a total AI meltdown. 249 00:08:38,520 --> 00:08:40,920 What kind of proactive things do they recommend? 250 00:08:40,920 --> 00:08:42,680 Impact assessments are a big one. 251 00:08:42,680 --> 00:08:44,840 They help you figure out how the AI could negatively 252 00:08:44,840 --> 00:08:48,080 affect individuals, communities, even society as a whole. 253 00:08:48,080 --> 00:08:49,480 So it's not just the technical stuff, 254 00:08:49,480 --> 00:08:52,200 but the social and ethical side of things too. 255 00:08:52,200 --> 00:08:53,320 Precisely. 256 00:08:53,320 --> 00:08:55,760 And they suggest getting all sorts of people involved 257 00:08:55,760 --> 00:08:56,800 in these assessments. 258 00:08:56,800 --> 00:08:59,640 Ethicists, social scientists, community representatives, 259 00:08:59,640 --> 00:09:01,280 even artists. 260 00:09:01,280 --> 00:09:03,360 The more viewpoints you have, the better 261 00:09:03,360 --> 00:09:05,440 you'll be at spotting potential blind spots. 262 00:09:05,440 --> 00:09:07,800 It's like crowdsourcing your risk management, 263 00:09:07,800 --> 00:09:09,040 but with experts. 264 00:09:09,040 --> 00:09:10,920 And along with these impact assessments, 265 00:09:10,920 --> 00:09:14,600 you need to have solid risk mitigation plans in place. 266 00:09:14,600 --> 00:09:16,240 So once you've identified the risks, 267 00:09:16,240 --> 00:09:18,600 you've got a strategy for dealing with them. 268 00:09:18,600 --> 00:09:19,640 Exactly. 269 00:09:19,640 --> 00:09:21,240 These plans lay out the steps you'll 270 00:09:21,240 --> 00:09:23,280 take to address each risk. 271 00:09:23,280 --> 00:09:25,360 And they need to be reviewed and updated 272 00:09:25,360 --> 00:09:28,400 as you learn more about the AI and its potential impact. 273 00:09:28,400 --> 00:09:30,560 It sounds like a lot of what if scenarios. 274 00:09:30,560 --> 00:09:32,440 But that's what risk management is all about, right? 275 00:09:32,440 --> 00:09:33,600 Being ready for anything. 276 00:09:33,600 --> 00:09:34,280 Right. 277 00:09:34,280 --> 00:09:35,760 But you can't predict everything, 278 00:09:35,760 --> 00:09:37,440 no matter how much you plan. 279 00:09:37,440 --> 00:09:40,760 So the playbook also emphasizes having strong incident 280 00:09:40,760 --> 00:09:41,760 response plans. 281 00:09:41,760 --> 00:09:43,840 Like a fire drill for your AI. 282 00:09:43,840 --> 00:09:44,760 Exactly. 283 00:09:44,760 --> 00:09:48,400 These plans outline what to do if something unexpected happens. 284 00:09:48,400 --> 00:09:51,080 You have a clear protocol for containing any damage, 285 00:09:51,080 --> 00:09:53,440 mitigating risks, and learning from the experience 286 00:09:53,440 --> 00:09:54,680 so it doesn't happen again. 287 00:09:54,680 --> 00:09:57,320 So if the AI starts acting up, you don't have to panic. 288 00:09:57,320 --> 00:09:59,680 You've got a plan to get things back on track. 289 00:09:59,680 --> 00:10:00,640 Exactly. 290 00:10:00,640 --> 00:10:02,760 An incident response isn't just about reacting. 291 00:10:02,760 --> 00:10:04,920 It's about learning and improving. 292 00:10:04,920 --> 00:10:08,480 Every incident gives you a chance to refine your approach 293 00:10:08,480 --> 00:10:10,800 and make your AI systems more robust. 294 00:10:10,800 --> 00:10:14,080 So it's a continuous cycle of planning, testing, monitoring, 295 00:10:14,080 --> 00:10:15,400 responding, and learning. 296 00:10:15,400 --> 00:10:16,560 Exactly. 297 00:10:16,560 --> 00:10:19,640 Managing AI risk is a constant process. 298 00:10:19,640 --> 00:10:23,120 And one of the most important parts of this is communication. 299 00:10:23,120 --> 00:10:26,360 Talking about the risks, who are we communicating with? 300 00:10:26,360 --> 00:10:27,200 Everyone. 301 00:10:27,200 --> 00:10:29,760 Inside the organization, that means developers, users, 302 00:10:29,760 --> 00:10:32,520 executives, anyone involved with the AI. 303 00:10:32,520 --> 00:10:35,800 Outside, it could be regulators, the media, even the public. 304 00:10:35,800 --> 00:10:37,760 Why is communication so important here? 305 00:10:37,760 --> 00:10:39,760 Transparency builds trust. 306 00:10:39,760 --> 00:10:42,920 When people understand how the AI works, what the risks are, 307 00:10:42,920 --> 00:10:44,920 and how those risks are being handled, 308 00:10:44,920 --> 00:10:46,440 they're more likely to be OK with it. 309 00:10:46,440 --> 00:10:47,040 Makes sense. 310 00:10:47,040 --> 00:10:49,200 No one wants to feel like things are being kept secret, 311 00:10:49,200 --> 00:10:50,720 especially when it comes to AI. 312 00:10:50,720 --> 00:10:53,240 And beyond trust, clear communication 313 00:10:53,240 --> 00:10:55,320 can actually make the AI better. 314 00:10:55,320 --> 00:10:57,720 When developers are open about their design choices 315 00:10:57,720 --> 00:10:59,640 and how they're mitigating risk, 316 00:10:59,640 --> 00:11:01,840 it lets others give feedback. 317 00:11:01,840 --> 00:11:03,640 They might spot problems that were missed. 318 00:11:03,640 --> 00:11:05,960 It's like getting a second opinion from a specialist 319 00:11:05,960 --> 00:11:07,880 but for your AI. 320 00:11:07,880 --> 00:11:08,920 Exactly. 321 00:11:08,920 --> 00:11:12,400 This feedback loop can lead to stronger, more reliable systems. 322 00:11:12,400 --> 00:11:14,840 It's like having a team of experts checking your work 323 00:11:14,840 --> 00:11:16,360 and giving you suggestions. 324 00:11:16,360 --> 00:11:18,960 We've talked a lot about managing risk. 325 00:11:18,960 --> 00:11:21,840 What about the good stuff, the benefits of AI? 326 00:11:21,840 --> 00:11:24,240 The playbook doesn't just focus on the negative, does it? 327 00:11:24,240 --> 00:11:25,320 Not at all. 328 00:11:25,320 --> 00:11:27,560 It acknowledges that AI has the potential 329 00:11:27,560 --> 00:11:29,840 to revolutionize so many industries 330 00:11:29,840 --> 00:11:32,760 and help us solve some of the biggest problems we face. 331 00:11:32,760 --> 00:11:35,480 But it also emphasizes that those benefits have to be carefully 332 00:11:35,480 --> 00:11:37,400 weighed against the potential risks. 333 00:11:37,400 --> 00:11:40,560 So it's not as simple as AI is good or AI is bad. 334 00:11:40,560 --> 00:11:42,360 It's about finding the right balance. 335 00:11:42,360 --> 00:11:43,560 Precisely. 336 00:11:43,560 --> 00:11:44,880 AI is a tool. 337 00:11:44,880 --> 00:11:47,320 Like any tool, it can be used for good or bad. 338 00:11:47,320 --> 00:11:49,200 The key is using it responsibly. 339 00:11:49,200 --> 00:11:51,200 And that's what this playbook is all about. 340 00:11:51,200 --> 00:11:54,200 It's a guide to navigating the world AI risk management 341 00:11:54,200 --> 00:11:57,120 and making sure this technology is used to benefit everyone. 342 00:11:57,120 --> 00:12:00,360 OK, so responsible AI, finding that balance 343 00:12:00,360 --> 00:12:02,960 between risks and rewards. 344 00:12:02,960 --> 00:12:06,120 The playbook also mentions this context mapping thing 345 00:12:06,120 --> 00:12:08,240 and how crucial that is. 346 00:12:08,240 --> 00:12:09,480 It sounds kind of vague, though. 347 00:12:09,480 --> 00:12:10,800 What does that even mean? 348 00:12:10,800 --> 00:12:13,440 Think of contexts like the environment where your AI is 349 00:12:13,440 --> 00:12:14,800 going to be working. 350 00:12:14,800 --> 00:12:17,760 An AI that's perfect when one situation might totally 351 00:12:17,760 --> 00:12:19,080 bomb in another. 352 00:12:19,080 --> 00:12:21,040 It's like taking a fish out of water. 353 00:12:21,040 --> 00:12:23,800 It can't survive if you change its surroundings. 354 00:12:23,800 --> 00:12:25,200 OK, yeah, I get that. 355 00:12:25,200 --> 00:12:27,520 So context mapping is about figuring out 356 00:12:27,520 --> 00:12:30,720 the specific environment where the AI will operate. 357 00:12:30,720 --> 00:12:32,840 What kinds of things should we be thinking about? 358 00:12:32,840 --> 00:12:35,240 First, you really need to nail down what the AI is supposed 359 00:12:35,240 --> 00:12:36,880 to be doing and who's going to be using it, 360 00:12:36,880 --> 00:12:39,480 what problems it's solving, what will it actually do, 361 00:12:39,480 --> 00:12:40,200 who are the users. 362 00:12:40,200 --> 00:12:41,440 Seems pretty straightforward, but I 363 00:12:41,440 --> 00:12:42,640 bet it gets more complicated. 364 00:12:42,640 --> 00:12:43,640 Oh, absolutely. 365 00:12:43,640 --> 00:12:47,080 The playbook wants us to look beyond those intended uses 366 00:12:47,080 --> 00:12:49,320 and think about how it could be misused 367 00:12:49,320 --> 00:12:51,920 or what unintended consequences might pop up. 368 00:12:51,920 --> 00:12:54,240 So even if an AI is built for good, 369 00:12:54,240 --> 00:12:56,120 it could still be used for something bad. 370 00:12:56,120 --> 00:12:57,120 Unfortunately, yeah. 371 00:12:57,120 --> 00:12:59,320 Take a facial recognition system, for example. 372 00:12:59,320 --> 00:13:01,560 Maybe it was designed to improve security, 373 00:13:01,560 --> 00:13:02,960 but in the wrong hands, it could be 374 00:13:02,960 --> 00:13:05,920 used for surveillance or even discrimination, 375 00:13:05,920 --> 00:13:09,840 or an AI hiring tool that's supposed to eliminate bias. 376 00:13:09,840 --> 00:13:11,920 If it's not designed really carefully, 377 00:13:11,920 --> 00:13:14,600 it could actually end up reinforcing those inequalities. 378 00:13:14,600 --> 00:13:15,600 That's kind of disturbing. 379 00:13:15,600 --> 00:13:17,360 So context mapping is about trying 380 00:13:17,360 --> 00:13:20,760 to anticipate how it could be misused, even abused. 381 00:13:20,760 --> 00:13:21,800 Exactly. 382 00:13:21,800 --> 00:13:23,440 You have to see the bigger picture. 383 00:13:23,440 --> 00:13:26,480 What are the social and ethical implications? 384 00:13:26,480 --> 00:13:29,120 How is this AI going to affect people, communities, 385 00:13:29,120 --> 00:13:30,680 even society as a whole? 386 00:13:30,680 --> 00:13:32,560 What could go wrong that we haven't even thought of? 387 00:13:32,560 --> 00:13:34,120 Whoa, those are some good questions. 388 00:13:34,120 --> 00:13:36,080 How do we even begin to tackle those? 389 00:13:36,080 --> 00:13:38,040 They recommend getting a diverse group of people 390 00:13:38,040 --> 00:13:40,160 to help out with this context mapping. 391 00:13:40,160 --> 00:13:43,920 Think ethicists, social scientists, legal experts, 392 00:13:43,920 --> 00:13:46,120 community reps, even artists. 393 00:13:46,120 --> 00:13:47,680 Wow, that is a diverse group. 394 00:13:47,680 --> 00:13:49,440 I hadn't thought about including artists before. 395 00:13:49,440 --> 00:13:50,880 What do they bring to the table? 396 00:13:50,880 --> 00:13:53,560 Artists are really good at thinking outside the box. 397 00:13:53,560 --> 00:13:56,360 They can imagine possibilities that other people might miss. 398 00:13:56,360 --> 00:13:59,680 They can help us visualize the impact and the consequences 399 00:13:59,680 --> 00:14:01,680 that might not be obvious right away. 400 00:14:01,680 --> 00:14:02,520 That's really cool. 401 00:14:02,520 --> 00:14:04,720 So the more diverse the group, the better chance 402 00:14:04,720 --> 00:14:06,680 you have of finding those blind spots 403 00:14:06,680 --> 00:14:10,080 and really understanding the whole context of the AI system. 404 00:14:10,080 --> 00:14:10,920 Exactly. 405 00:14:10,920 --> 00:14:12,360 And it's not a one and done thing. 406 00:14:12,360 --> 00:14:15,600 The environment around an AI, it can change. 407 00:14:15,600 --> 00:14:19,560 So it's important to revisit and update that context map 408 00:14:19,560 --> 00:14:20,400 pretty regularly. 409 00:14:20,400 --> 00:14:22,320 So it's like an ongoing discussion, 410 00:14:22,320 --> 00:14:25,080 always reassessing and adapting as things change. 411 00:14:25,080 --> 00:14:26,200 Precisely. 412 00:14:26,200 --> 00:14:28,280 And it's crucial to write all of this down 413 00:14:28,280 --> 00:14:30,040 and share it with everyone involved. 414 00:14:30,040 --> 00:14:32,440 Why is documentation so important? 415 00:14:32,440 --> 00:14:34,360 It seems like it would just add more red peep 416 00:14:34,360 --> 00:14:36,400 to an already complicated process. 417 00:14:36,400 --> 00:14:38,960 Documentation actually serves a few purposes. 418 00:14:38,960 --> 00:14:42,000 First, it shows that you've done your due diligence. 419 00:14:42,000 --> 00:14:44,720 If someone questions your risk management process, 420 00:14:44,720 --> 00:14:46,760 you've got proof that you've thought about the potential 421 00:14:46,760 --> 00:14:47,920 risks and impacts. 422 00:14:47,920 --> 00:14:51,160 So it's like a paper trail for responsible AI development. 423 00:14:51,160 --> 00:14:51,880 Exactly. 424 00:14:51,880 --> 00:14:54,520 It also keeps everyone on the same page, you know. 425 00:14:54,520 --> 00:14:56,960 Ensures consistency over time. 426 00:14:56,960 --> 00:15:00,720 If new people join the team, or if you update the AI, 427 00:15:00,720 --> 00:15:02,560 the documentation provides a roadmap 428 00:15:02,560 --> 00:15:05,280 for understanding the context and the risks. 429 00:15:05,280 --> 00:15:06,040 That makes sense. 430 00:15:06,040 --> 00:15:08,680 No one wants to start from scratch every time something changes. 431 00:15:08,680 --> 00:15:10,760 And last but not least, documentation 432 00:15:10,760 --> 00:15:12,760 helps with communication and collaboration 433 00:15:12,760 --> 00:15:14,080 across different teams. 434 00:15:14,080 --> 00:15:17,040 It's something everyone can refer to and contribute to. 435 00:15:17,040 --> 00:15:19,320 OK, so documentation is not just a formality. 436 00:15:19,320 --> 00:15:23,160 It's a real tool for making sure AI is developed responsibly. 437 00:15:23,160 --> 00:15:23,680 Right. 438 00:15:23,680 --> 00:15:25,440 And speaking of responsible development, 439 00:15:25,440 --> 00:15:29,040 the playbook also talks about being up front about the AI's 440 00:15:29,040 --> 00:15:29,640 limitations. 441 00:15:29,640 --> 00:15:31,120 So don't over-hype it. 442 00:15:31,120 --> 00:15:31,880 Exactly. 443 00:15:31,880 --> 00:15:33,480 Manage those expectations. 444 00:15:33,480 --> 00:15:35,800 Be honest about what the AI can and can't do. 445 00:15:35,800 --> 00:15:37,960 It's easy to get caught up thinking about AI 446 00:15:37,960 --> 00:15:40,200 as this magical thing that can solve any problem. 447 00:15:40,200 --> 00:15:41,960 But we've got to remember, it's still 448 00:15:41,960 --> 00:15:43,640 a tool at the end of the day. 449 00:15:43,640 --> 00:15:47,240 And like any tool, it has its strengths and its weaknesses. 450 00:15:47,240 --> 00:15:49,240 The playbook really encourages organizations 451 00:15:49,240 --> 00:15:51,680 to be transparent about those limitations, 452 00:15:51,680 --> 00:15:53,440 both internally and externally. 453 00:15:53,440 --> 00:15:55,240 But wouldn't pointing out limitations 454 00:15:55,240 --> 00:15:57,680 make people less likely to use the AI? 455 00:15:57,680 --> 00:15:59,440 Actually, it's the opposite. 456 00:15:59,440 --> 00:16:02,280 When people get what the AI can and can't do, 457 00:16:02,280 --> 00:16:05,400 they tend to trust it more and use it in the right way. 458 00:16:05,400 --> 00:16:07,520 Plus, being open about limitations 459 00:16:07,520 --> 00:16:10,600 actually leads to better AI systems down the line. 460 00:16:10,600 --> 00:16:11,280 How so? 461 00:16:11,280 --> 00:16:14,800 When developers are up front about the AI's capabilities, 462 00:16:14,800 --> 00:16:17,320 it allows users to give feedback and point out 463 00:16:17,320 --> 00:16:19,000 areas that need improvement. 464 00:16:19,000 --> 00:16:20,600 This kind of feedback loop can lead 465 00:16:20,600 --> 00:16:23,560 to systems that are more robust, more reliable, 466 00:16:23,560 --> 00:16:25,920 and actually meet the user's needs better. 467 00:16:25,920 --> 00:16:26,720 That's a good point. 468 00:16:26,720 --> 00:16:29,480 It's about creating a culture of open communication 469 00:16:29,480 --> 00:16:31,480 and collaboration around AI. 470 00:16:31,480 --> 00:16:35,000 So after this deep dive into the AI-RMF playbook, 471 00:16:35,000 --> 00:16:37,080 what's the big takeaway for our listeners? 472 00:16:37,080 --> 00:16:39,600 This playbook is a fantastic place to start. 473 00:16:39,600 --> 00:16:42,000 But the real work happens when you put these principles 474 00:16:42,000 --> 00:16:42,960 into action. 475 00:16:42,960 --> 00:16:46,000 And remember, managing AI risk isn't a one-time thing. 476 00:16:46,000 --> 00:16:47,640 It's an ongoing process. 477 00:16:47,640 --> 00:16:50,120 You have to constantly learn, adapt, and work together. 478 00:16:50,120 --> 00:16:51,920 It sounds like a lot of work, but it's 479 00:16:51,920 --> 00:16:54,560 got to be worth it to make sure we're using AI responsibly 480 00:16:54,560 --> 00:16:56,240 and getting the most out of its potential 481 00:16:56,240 --> 00:16:57,680 to benefit humanity. 482 00:16:57,680 --> 00:16:58,680 Absolutely. 483 00:16:58,680 --> 00:17:00,480 And AI is only going to keep evolving, 484 00:17:00,480 --> 00:17:03,880 so we need to keep refining our approach to managing risk. 485 00:17:03,880 --> 00:17:06,560 This playbook gives us a strong foundation to build on. 486 00:17:06,560 --> 00:17:08,320 This has been incredibly insightful. 487 00:17:08,320 --> 00:17:10,960 Thanks for walking us through this complex topic. 488 00:17:10,960 --> 00:17:13,520 We hope our listeners feel ready to tackle the world of AI 489 00:17:13,520 --> 00:17:14,480 risk management. 490 00:17:14,480 --> 00:17:15,360 My pleasure. 491 00:17:15,360 --> 00:17:19,360 And remember, building trustworthy AI is a team effort. 492 00:17:19,360 --> 00:17:21,200 We all have a part to play in making sure 493 00:17:21,200 --> 00:17:23,760 this powerful technology is used for good. 494 00:17:23,760 --> 00:17:25,480 That's a great point to wrap up on. 495 00:17:25,480 --> 00:17:28,320 Thanks for joining us on this deep dive into the AI-RMF 496 00:17:28,320 --> 00:17:28,920 playbook. 497 00:17:28,920 --> 00:17:38,560 Until next time.