WEBVTT 1 00:00:00.090 --> 00:00:01.410 In this lesson, 2 00:00:01.410 --> 00:00:05.130 we will learn about legal and privacy risks. 3 00:00:05.130 --> 00:00:07.110 Legal and privacy rules 4 00:00:07.110 --> 00:00:11.280 ensure that artificial intelligence or AI systems 5 00:00:11.280 --> 00:00:12.900 are designed and deployed 6 00:00:12.900 --> 00:00:15.960 in compliance with laws and regulations 7 00:00:15.960 --> 00:00:19.740 to protect individual rights and personal data. 8 00:00:19.740 --> 00:00:24.660 Legal and privacy implications include potential misuse 9 00:00:24.660 --> 00:00:29.400 and explainable verse non-explainable AI models. 10 00:00:29.400 --> 00:00:31.770 The potential misuse of AI 11 00:00:31.770 --> 00:00:35.100 arises when AI systems are used in ways 12 00:00:35.100 --> 00:00:37.320 that could lead to discrimination, 13 00:00:37.320 --> 00:00:41.370 privacy violations, or other harmful outcomes. 14 00:00:41.370 --> 00:00:44.670 This misuse is often exacerbated 15 00:00:44.670 --> 00:00:47.970 by the opacity of the models that are used, 16 00:00:47.970 --> 00:00:50.160 inferring that AI models 17 00:00:50.160 --> 00:00:54.029 may be explainable or non-explainable. 18 00:00:54.029 --> 00:00:57.360 Explainable models are those that provide clear, 19 00:00:57.360 --> 00:01:00.750 understandable reasons for their decisions. 20 00:01:00.750 --> 00:01:04.950 Non-explainable models like many deep learning algorithms 21 00:01:04.950 --> 00:01:08.970 can be difficult to interpret or understand. 22 00:01:08.970 --> 00:01:13.110 Let's learn more about the potential misuse of AI 23 00:01:13.110 --> 00:01:17.850 and explainable verse non-unexplainable AI models. 24 00:01:17.850 --> 00:01:20.580 First, we have the potential misuse 25 00:01:20.580 --> 00:01:23.760 of artificial intelligence or AI. 26 00:01:23.760 --> 00:01:27.660 The potential misuse of AI becomes a concern 27 00:01:27.660 --> 00:01:29.670 when systems are used in ways 28 00:01:29.670 --> 00:01:31.860 that can lead to discrimination, 29 00:01:31.860 --> 00:01:35.910 privacy breaches, or other harmful consequences. 30 00:01:35.910 --> 00:01:39.630 A notable example of potential AI misuse 31 00:01:39.630 --> 00:01:42.690 is in the area of predictive policing, 32 00:01:42.690 --> 00:01:47.040 where AI algorithms analyze historical crime data 33 00:01:47.040 --> 00:01:51.990 to identify areas or individuals deemed high risk. 34 00:01:51.990 --> 00:01:55.260 These models, if trained on biased data, 35 00:01:55.260 --> 00:01:58.830 may disproportionately target certain communities, 36 00:01:58.830 --> 00:02:01.500 reinforcing existing biases 37 00:02:01.500 --> 00:02:05.460 and leading to overpolicing in these areas. 38 00:02:05.460 --> 00:02:08.220 So without strict oversight, 39 00:02:08.220 --> 00:02:13.080 such AI applications can perpetuate discrimination. 40 00:02:13.080 --> 00:02:16.170 This highlights the need for robust legal 41 00:02:16.170 --> 00:02:18.000 and privacy standards 42 00:02:18.000 --> 00:02:21.990 to ensure AI respects individuals' rights 43 00:02:21.990 --> 00:02:26.010 and does not exacerbate societal inequalities. 44 00:02:26.010 --> 00:02:29.640 In healthcare, AI misuse could arise 45 00:02:29.640 --> 00:02:33.150 if algorithms are used to predict patient outcomes 46 00:02:33.150 --> 00:02:35.370 or optimize treatment plans 47 00:02:35.370 --> 00:02:38.700 and are trained on data that lacks diversity. 48 00:02:38.700 --> 00:02:40.140 For instance, 49 00:02:40.140 --> 00:02:44.010 if a predictive model is trained primarily on data 50 00:02:44.010 --> 00:02:45.810 from one demographic, 51 00:02:45.810 --> 00:02:50.010 it may not perform well for patients with other backgrounds, 52 00:02:50.010 --> 00:02:54.900 potentially leading to misdiagnoses or inadequate care. 53 00:02:54.900 --> 00:02:59.190 Such bias in healthcare can have serious consequences 54 00:02:59.190 --> 00:03:02.190 for patient safety and equality, 55 00:03:02.190 --> 00:03:05.910 underscoring the importance of regulatory guidelines 56 00:03:05.910 --> 00:03:09.360 that require diverse and representative data 57 00:03:09.360 --> 00:03:14.040 to prevent harm and ensure ethical AI use. 58 00:03:14.040 --> 00:03:17.250 So the potential misuse of AI 59 00:03:17.250 --> 00:03:19.800 raises significant concerns, 60 00:03:19.800 --> 00:03:23.760 especially when systems operate on biased data 61 00:03:23.760 --> 00:03:27.300 that could lead to discrimination, privacy issues, 62 00:03:27.300 --> 00:03:29.250 or unfair treatment. 63 00:03:29.250 --> 00:03:32.790 In areas like predictive policing and healthcare, 64 00:03:32.790 --> 00:03:36.090 biased algorithms may disproportionately target 65 00:03:36.090 --> 00:03:40.530 certain communities or fail to provide equitable care, 66 00:03:40.530 --> 00:03:43.380 highlighting the need for strict oversight 67 00:03:43.380 --> 00:03:47.700 and regulatory standards to protect individual rights 68 00:03:47.700 --> 00:03:51.030 and ensure ethical AI deployment. 69 00:03:51.030 --> 00:03:55.680 Second, we have explainable versus non-explainable 70 00:03:55.680 --> 00:03:59.310 artificial intelligence or AI models. 71 00:03:59.310 --> 00:04:03.030 Explainable and non-explainable AI models 72 00:04:03.030 --> 00:04:05.250 represent two different approaches 73 00:04:05.250 --> 00:04:09.270 to AI transparency and interpretation. 74 00:04:09.270 --> 00:04:14.190 Explainable AI models provide clear, understandable reasons 75 00:04:14.190 --> 00:04:15.780 for their decisions, 76 00:04:15.780 --> 00:04:20.610 allowing users to follow the logic behind each outcome. 77 00:04:20.610 --> 00:04:25.470 For instance, a decision tree model used in loan approval 78 00:04:25.470 --> 00:04:27.900 could show how specific factors 79 00:04:27.900 --> 00:04:32.370 like income or credit history influenced the decision. 80 00:04:32.370 --> 00:04:35.820 This transparency helps users understand 81 00:04:35.820 --> 00:04:39.810 why an AI system made a certain choice, 82 00:04:39.810 --> 00:04:41.340 increasing trust 83 00:04:41.340 --> 00:04:45.090 and allowing organizations to meet legal requirements 84 00:04:45.090 --> 00:04:46.770 for fair treatment. 85 00:04:46.770 --> 00:04:50.220 However, creating an explainable model 86 00:04:50.220 --> 00:04:52.230 isn't always possible, 87 00:04:52.230 --> 00:04:55.080 especially for complex applications 88 00:04:55.080 --> 00:04:58.980 that use deep learning or other advanced techniques. 89 00:04:58.980 --> 00:05:00.360 In such cases, 90 00:05:00.360 --> 00:05:02.970 the model's decision making process 91 00:05:02.970 --> 00:05:07.230 involves intricate patterns that are hard to interpret, 92 00:05:07.230 --> 00:05:10.710 often leading to black box models. 93 00:05:10.710 --> 00:05:13.440 While methods like model simplification 94 00:05:13.440 --> 00:05:17.880 or feature attribution can offer partial explanations, 95 00:05:17.880 --> 00:05:21.030 they may not achieve full transparency. 96 00:05:21.030 --> 00:05:25.080 So balancing performance with interpretability 97 00:05:25.080 --> 00:05:27.600 remains a significant challenge, 98 00:05:27.600 --> 00:05:31.140 particularly in fields requiring accountability 99 00:05:31.140 --> 00:05:33.180 and legal compliance. 100 00:05:33.180 --> 00:05:36.420 In some cases, non-explainable models 101 00:05:36.420 --> 00:05:39.000 have led to unintended bias 102 00:05:39.000 --> 00:05:41.400 because their complex structure 103 00:05:41.400 --> 00:05:45.570 makes it difficult to identify and correct issues. 104 00:05:45.570 --> 00:05:48.870 For example, when used in credit scoring, 105 00:05:48.870 --> 00:05:52.140 a non-explainable model might deny loans 106 00:05:52.140 --> 00:05:54.270 based on patterns in data 107 00:05:54.270 --> 00:05:57.360 that reflect historical discrimination. 108 00:05:57.360 --> 00:06:01.200 Since the reasoning behind these decisions is opaque, 109 00:06:01.200 --> 00:06:04.710 affected individuals have no way to challenge 110 00:06:04.710 --> 00:06:07.920 or understand the AI's decision, 111 00:06:07.920 --> 00:06:10.500 potentially leading to legal issues 112 00:06:10.500 --> 00:06:13.530 related to anti-discrimination laws. 113 00:06:13.530 --> 00:06:17.640 This is why many sectors with regulatory requirements 114 00:06:17.640 --> 00:06:20.130 favor explainable AI, 115 00:06:20.130 --> 00:06:23.970 which can provide a clear decision-making process. 116 00:06:23.970 --> 00:06:27.060 So the difference between explainable 117 00:06:27.060 --> 00:06:29.220 and non-explainable models 118 00:06:29.220 --> 00:06:33.300 becomes important in legal regulatory contexts, 119 00:06:33.300 --> 00:06:35.730 where explainable AI models 120 00:06:35.730 --> 00:06:40.380 allow organizations to meet legal transparency requirements, 121 00:06:40.380 --> 00:06:44.850 helping users understand the AI's decision-making process 122 00:06:44.850 --> 00:06:48.660 and providing avenues for recourse if needed. 123 00:06:48.660 --> 00:06:51.600 While non-explainable models can conflict 124 00:06:51.600 --> 00:06:53.310 with these requirements, 125 00:06:53.310 --> 00:06:56.580 especially when used in high stakes areas 126 00:06:56.580 --> 00:06:58.920 like hiring or healthcare. 127 00:06:58.920 --> 00:07:03.330 As a result, balancing the use of non-explainable models 128 00:07:03.330 --> 00:07:07.350 for their accuracy with the need for transparency 129 00:07:07.350 --> 00:07:12.210 has become a central issue in AI governance and regulation. 130 00:07:12.210 --> 00:07:14.520 The difference between explainable 131 00:07:14.520 --> 00:07:16.680 and non-explainable models 132 00:07:16.680 --> 00:07:20.700 becomes important in legal regulatory contexts. 133 00:07:20.700 --> 00:07:24.960 So remember, legal and privacy rules 134 00:07:24.960 --> 00:07:29.960 are crucial to ensure artificial intelligence or AI systems 135 00:07:30.270 --> 00:07:33.270 operate within regulatory frameworks, 136 00:07:33.270 --> 00:07:37.260 protecting individual rights and personal data. 137 00:07:37.260 --> 00:07:42.000 Legal and privacy concepts include the misuse of AI 138 00:07:42.000 --> 00:07:47.000 as well as explainable versus non-explainable AI models. 139 00:07:47.340 --> 00:07:50.940 The misuse of AI can lead to discrimination, 140 00:07:50.940 --> 00:07:54.990 privacy violations, or other harmful outcomes, 141 00:07:54.990 --> 00:07:59.700 especially when models are opaque or lack transparency. 142 00:07:59.700 --> 00:08:02.520 Next, explainable AI models 143 00:08:02.520 --> 00:08:05.700 offer clear reasons for their decisions, 144 00:08:05.700 --> 00:08:10.680 allowing users to understand the factors behind outcomes 145 00:08:10.680 --> 00:08:12.720 and enhancing trust. 146 00:08:12.720 --> 00:08:16.890 Finally, non-explainable models make it challenging 147 00:08:16.890 --> 00:08:20.010 to interpret their decision-making process, 148 00:08:20.010 --> 00:08:23.820 which can create legal and ethical concerns. 149 00:08:23.820 --> 00:08:28.820 So balancing accuracy with transparency is important 150 00:08:28.920 --> 00:08:33.030 as explainable models meet legal requirements for fairness, 151 00:08:33.030 --> 00:08:35.070 while non-explainable models 152 00:08:35.070 --> 00:08:37.890 may struggle to align with these standards 153 00:08:37.890 --> 00:08:39.783 in high stakes areas.