1 00:00:00,050 --> 00:00:06,140 Lesson understanding AI failures, bias, hallucinations and errors, understanding AI failures, bias, 2 00:00:06,140 --> 00:00:07,850 hallucinations, and errors. 3 00:00:08,390 --> 00:00:13,490 Artificial intelligence systems have become integral to various sectors, from healthcare and finance 4 00:00:13,490 --> 00:00:15,740 to transportation and entertainment. 5 00:00:16,100 --> 00:00:21,710 Despite their vast potential, AI systems are not infallible and can exhibit failures such as bias, 6 00:00:21,710 --> 00:00:23,690 hallucinations, and errors. 7 00:00:24,260 --> 00:00:29,870 Understanding these failures is crucial for AI governance professionals to ensure ethical and accountable 8 00:00:29,900 --> 00:00:31,130 AI deployment. 9 00:00:33,350 --> 00:00:38,030 Bias in AI systems often stems from the data used to train the models. 10 00:00:38,360 --> 00:00:44,210 When training data reflects existing societal biases, the AI system can inadvertently perpetuate these 11 00:00:44,210 --> 00:00:46,880 biases, leading to unfair outcomes. 12 00:00:47,210 --> 00:00:53,570 For example, a study by Buolamwini and Gebru highlighted significant racial and gender biases in commercial 13 00:00:53,570 --> 00:00:58,820 facial recognition systems, where error rates for darker skinned women were much higher than for lighter 14 00:00:58,820 --> 00:00:59,600 skinned men. 15 00:01:00,470 --> 00:01:06,530 This disparity can lead to discriminatory practices in areas like law enforcement, Meant hiring and 16 00:01:06,530 --> 00:01:07,910 personal finance. 17 00:01:08,690 --> 00:01:13,880 The root cause lies in the underrepresentation of certain demographic groups in the training data sets, 18 00:01:13,880 --> 00:01:18,380 which skews the model's ability to generalize across diverse populations. 19 00:01:18,920 --> 00:01:24,770 Addressing this issue requires a conscientious effort to curate balanced and representative data sets. 20 00:01:24,800 --> 00:01:30,080 Along with implementing fairness aware algorithms that can mitigate bias during the learning process. 21 00:01:32,240 --> 00:01:38,150 Another critical failure in AI systems is hallucination, particularly in natural language processing 22 00:01:38,150 --> 00:01:38,810 models. 23 00:01:39,470 --> 00:01:45,410 Hallucination occurs when an AI system generates outputs that are not grounded in the input data, essentially 24 00:01:45,410 --> 00:01:47,120 fabricating information. 25 00:01:47,330 --> 00:01:53,060 This phenomenon is prevalent in generative models like GPT three, where the model might produce plausible 26 00:01:53,060 --> 00:01:55,940 but incorrect or nonsensical responses. 27 00:01:56,570 --> 00:02:02,390 For instance, an AI might fabricate details about a historical event or generate fictitious medical 28 00:02:02,390 --> 00:02:05,210 advice posing significant ethical risks. 29 00:02:05,990 --> 00:02:11,470 The underlying cause of hallucinations is often linked to the model's training objective, which prioritizes 30 00:02:11,470 --> 00:02:14,320 fluency and coherence over factual accuracy. 31 00:02:15,250 --> 00:02:21,160 To mitigate hallucinations, researchers are exploring techniques such as incorporating factual verification 32 00:02:21,160 --> 00:02:27,340 mechanisms and leveraging human in the loop approaches to ensure the generated content's reliability. 33 00:02:28,840 --> 00:02:35,800 Errors in AI systems can manifest in various forms, including prediction inaccuracies, system failures, 34 00:02:35,800 --> 00:02:37,900 and unintended consequences. 35 00:02:37,930 --> 00:02:42,970 These errors can have profound implications, especially in high stakes domains like healthcare. 36 00:02:43,000 --> 00:02:49,630 For example, a flawed AI diagnostic tool might misinterpret medical images leading to incorrect diagnoses 37 00:02:49,630 --> 00:02:50,500 and treatments. 38 00:02:50,890 --> 00:02:57,580 The challenge of ensuring accuracy is compounded by the complexity and opacity of many AI models, particularly 39 00:02:57,580 --> 00:03:02,710 deep learning networks, which operate as black boxes with limited interpretability. 40 00:03:02,980 --> 00:03:08,950 Enhancing transparency and interpretability is essential for building trust and accountability in AI 41 00:03:08,980 --> 00:03:09,850 systems. 42 00:03:10,150 --> 00:03:15,700 Techniques such as model agnostic interpretability methods and the development of inherently interpretable 43 00:03:15,730 --> 00:03:21,850 models can provide insights into the decision making process, allowing stakeholders to identify and 44 00:03:21,850 --> 00:03:24,100 rectify errors more effectively. 45 00:03:25,750 --> 00:03:32,350 Moreover, the deployment of AI systems in dynamic and unpredictable real world environments introduces 46 00:03:32,380 --> 00:03:34,810 additional risks of errors and failures. 47 00:03:35,140 --> 00:03:41,890 For instance, autonomous vehicles must navigate complex and ever changing traffic scenarios where unexpected 48 00:03:41,890 --> 00:03:44,440 events can lead to catastrophic outcomes. 49 00:03:45,190 --> 00:03:51,580 The Uber self-driving car fatality in 2018 is a stark reminder of the potential dangers associated with 50 00:03:51,580 --> 00:03:54,250 AI errors in safety critical applications. 51 00:03:54,250 --> 00:04:00,490 Ensuring robust performance in such contexts requires extensive testing, validation, and continuous 52 00:04:00,490 --> 00:04:03,460 monitoring to detect and address anomalies promptly. 53 00:04:04,720 --> 00:04:10,240 The ethical implications of AI failures extend beyond technical considerations, encompassing broader 54 00:04:10,240 --> 00:04:17,800 societal impacts, bias, hallucinations, and errors in AI systems can exacerbate inequalities, undermine 55 00:04:17,800 --> 00:04:20,970 trust, and erode public confidence in technology. 56 00:04:20,970 --> 00:04:26,580 Therefore, a comprehensive approach to AI governance must integrate ethical principles with technical 57 00:04:26,580 --> 00:04:27,450 solutions. 58 00:04:27,900 --> 00:04:33,510 This includes establishing accountability mechanisms such as transparent documentation of AI development 59 00:04:33,510 --> 00:04:39,750 processes, rigorous auditing practices, and clear channels for reporting and addressing grievances. 60 00:04:40,770 --> 00:04:46,950 Additionally, fostering interdisciplinary collaboration among technologists, ethicists, legal experts, 61 00:04:46,950 --> 00:04:52,740 and affected communities can help ensure that AI systems are designed and deployed in ways that align 62 00:04:52,740 --> 00:04:54,870 with societal values and norms. 63 00:04:55,980 --> 00:05:02,580 In conclusion, understanding AI failures, bias, hallucinations, and errors is essential for developing 64 00:05:02,580 --> 00:05:04,980 ethical and accountable AI systems. 65 00:05:05,460 --> 00:05:10,470 Addressing these challenges requires a multifaceted approach that combines technical innovations with 66 00:05:10,470 --> 00:05:16,230 robust governance frameworks by prioritizing fairness, transparency, and collaboration. 67 00:05:16,260 --> 00:05:22,260 AI governance professionals can help mitigate the risks associated with AI failures and harness the 68 00:05:22,260 --> 00:05:25,710 technology's potential for positive societal impact.