1 00:00:00,050 --> 00:00:00,500 Lesson. 2 00:00:00,500 --> 00:00:03,800 Transparency, explainability and accountability in AI. 3 00:00:03,830 --> 00:00:05,030 Transparency. 4 00:00:05,030 --> 00:00:10,640 Explainability and accountability in artificial intelligence are critical components of responsible 5 00:00:10,670 --> 00:00:13,460 AI principles and trustworthy AI. 6 00:00:13,970 --> 00:00:19,280 These elements are essential for building and maintaining public trust in AI systems, ensuring that 7 00:00:19,280 --> 00:00:23,450 these systems are fair, ethical, and aligned with societal values. 8 00:00:23,780 --> 00:00:30,500 Transparency refers to the clarity and openness with which AI processes, decisions, and data are communicated. 9 00:00:30,530 --> 00:00:36,890 Explainability involves the ability to understand and interpret how AI systems reach their decisions. 10 00:00:37,580 --> 00:00:42,620 Accountability pertains to the mechanisms in place to ensure that entities developing and deploying 11 00:00:42,650 --> 00:00:47,990 AI systems are held responsible for their actions and the outcomes of these systems. 12 00:00:49,340 --> 00:00:56,180 Transparency in AI is paramount because it enables stakeholders to understand how AI systems operate 13 00:00:56,180 --> 00:00:57,590 and make decisions. 14 00:00:58,010 --> 00:01:03,970 This understanding is crucial for identifying potential biases and ensuring that AI systems adhere to 15 00:01:04,000 --> 00:01:05,230 ethical standards. 16 00:01:05,230 --> 00:01:10,930 For example, the European Union's General Data Protection Regulation mandates that individuals have 17 00:01:10,930 --> 00:01:15,310 the right to receive explanations for decisions made by automated systems. 18 00:01:15,340 --> 00:01:18,520 Highlighting the importance of transparency in AI. 19 00:01:18,820 --> 00:01:24,520 Transparency can be achieved through various means such as open source code, clear documentation, 20 00:01:24,520 --> 00:01:28,660 and communication of the data and algorithms used in AI systems. 21 00:01:29,740 --> 00:01:35,830 Explainability in AI is closely linked to transparency, but focuses more on the interpretability of 22 00:01:35,830 --> 00:01:36,940 AI decisions. 23 00:01:37,510 --> 00:01:40,090 Explainability is essential for several reasons. 24 00:01:40,120 --> 00:01:46,420 Firstly, it allows users to trust AI systems by providing insights into how decisions are made. 25 00:01:46,720 --> 00:01:51,100 Secondly, it helps identify and mitigate biases and errors in AI systems. 26 00:01:51,100 --> 00:01:57,070 For instance, a study by Ribeiro, Singh and Guestrin introduced the local Interpretable Model agnostic 27 00:01:57,070 --> 00:02:02,730 explanations framework, which provides explanations for individual predictions made by AI models. 28 00:02:03,540 --> 00:02:09,150 This framework helps users understand and trust the decisions of complex models such as deep learning 29 00:02:09,150 --> 00:02:10,110 algorithms. 30 00:02:10,500 --> 00:02:15,900 Explainability is particularly crucial in high stakes domains such as healthcare and criminal justice, 31 00:02:15,930 --> 00:02:20,580 where AI decisions can have significant consequences on individuals lives. 32 00:02:22,680 --> 00:02:28,770 Accountability in AI ensures that entities developing and deploying AI systems are responsible for their 33 00:02:28,800 --> 00:02:31,350 actions and the outcomes of these systems. 34 00:02:31,380 --> 00:02:37,680 Accountability mechanisms include legal and regulatory frameworks, ethical guidelines, and organizational 35 00:02:37,680 --> 00:02:38,610 policies. 36 00:02:38,910 --> 00:02:44,880 For instance, the E2E Global Initiative on Ethics of Autonomous and Intelligent Systems has developed 37 00:02:44,880 --> 00:02:49,890 a comprehensive set of ethical guidelines for AI emphasizing accountability. 38 00:02:50,580 --> 00:02:56,580 These guidelines recommend that organizations establish clear lines of responsibility, conduct regular 39 00:02:56,580 --> 00:03:00,620 audits, and ensure that AI systems align with ethical principles. 40 00:03:01,580 --> 00:03:07,520 Accountability also involves the ability to attribute responsibility for I decisions to human actors, 41 00:03:07,520 --> 00:03:12,470 ensuring that there is always a clear point of contact for addressing issues and concerns. 42 00:03:14,030 --> 00:03:20,120 The interplay between transparency, explainability, and accountability is crucial for fostering trust 43 00:03:20,120 --> 00:03:21,440 in AI systems. 44 00:03:22,190 --> 00:03:28,010 A lack of transparency can lead to mistrust and skepticism, as stakeholders may perceive AI systems 45 00:03:28,010 --> 00:03:30,830 as black boxes that operate without oversight. 46 00:03:31,430 --> 00:03:36,860 This mistrust can be exacerbated by the complexity of AI algorithms, which often involve intricate 47 00:03:36,860 --> 00:03:40,610 mathematical models that are difficult for non-experts to understand. 48 00:03:41,240 --> 00:03:47,210 Explainability addresses this issue by providing insights into how AI systems make decisions, thereby 49 00:03:47,210 --> 00:03:48,860 enhancing transparency. 50 00:03:49,400 --> 00:03:55,340 However, explainability alone is insufficient without accountability mechanisms to ensure that entities 51 00:03:55,340 --> 00:03:59,600 developing and deploying AI systems are held responsible for their actions. 52 00:04:01,150 --> 00:04:06,400 Real world examples illustrate the importance of these principles in AI governance. 53 00:04:06,820 --> 00:04:12,490 One notable case is the Compas algorithm, used in the US criminal justice system to assess the risk 54 00:04:12,490 --> 00:04:13,570 of recidivism. 55 00:04:14,050 --> 00:04:19,660 A ProPublica investigation revealed that Compas was biased against African American defendants, who 56 00:04:19,690 --> 00:04:24,700 were more likely to be incorrectly judged as high risk compared to white defendants. 57 00:04:25,480 --> 00:04:31,570 This case highlights the need for transparency and explainability in AI systems to identify and mitigate 58 00:04:31,570 --> 00:04:32,440 biases. 59 00:04:33,010 --> 00:04:38,200 Additionally, it underscores the importance of accountability as the developers and users of Compas 60 00:04:38,200 --> 00:04:42,370 must be held responsible for the algorithm's impact on individuals lives. 61 00:04:44,380 --> 00:04:47,920 Statistics also underscore the significance of these principles. 62 00:04:48,340 --> 00:04:54,490 A survey by the Pew Research Center found that 58% of Americans believe that AI and automation will 63 00:04:54,490 --> 00:04:57,790 have a significant impact on their lives in the coming decades. 64 00:04:58,120 --> 00:05:05,030 However, only 25% of respondents expressed confidence that AI developers would prioritize the public 65 00:05:05,030 --> 00:05:06,440 good over profit. 66 00:05:07,130 --> 00:05:12,920 This lack of confidence underscores the need for transparency, explainability, and accountability 67 00:05:12,920 --> 00:05:18,470 in AI to build public trust and ensure that AI systems are aligned with societal values. 68 00:05:19,370 --> 00:05:24,920 In addition to legal and regulatory frameworks, organizations play a crucial role in promoting these 69 00:05:24,920 --> 00:05:25,790 principles. 70 00:05:26,330 --> 00:05:32,030 For example, Google has established an AI principles framework which outlines the company's commitment 71 00:05:32,030 --> 00:05:35,510 to transparency, explainability, and accountability. 72 00:05:36,320 --> 00:05:42,110 This framework includes principles such as avoiding creating or reinforcing unfair bias, providing 73 00:05:42,110 --> 00:05:47,210 explanations for AI decisions, and ensuring accountability through human oversight. 74 00:05:47,210 --> 00:05:52,730 By adhering to these principles, organizations can demonstrate their commitment to responsible AI and 75 00:05:52,730 --> 00:05:54,710 build trust with stakeholders. 76 00:05:56,480 --> 00:06:02,680 The challenges associated with achieving Transparency, explainability and accountability in AI are 77 00:06:02,680 --> 00:06:05,110 significant but not insurmountable. 78 00:06:05,920 --> 00:06:11,650 One challenge is the inherent complexity of AI algorithms, particularly deep learning models, which 79 00:06:11,650 --> 00:06:16,030 can involve millions of parameters and intricate mathematical operations. 80 00:06:16,720 --> 00:06:21,940 Researchers and practitioners are developing techniques to enhance the interpretability of these models, 81 00:06:21,940 --> 00:06:26,260 such as the aforementioned Lime framework and other model agnostic methods. 82 00:06:26,770 --> 00:06:32,410 Another challenge is the potential trade off between accuracy and interpretability, as simpler models 83 00:06:32,410 --> 00:06:35,920 may be more interpretable but less accurate than complex models. 84 00:06:36,370 --> 00:06:42,040 Balancing these trade offs requires careful consideration of the specific context and the potential 85 00:06:42,040 --> 00:06:43,840 impact of AI decisions. 86 00:06:45,700 --> 00:06:50,740 The role of interdisciplinary collaboration is also essential in addressing these challenges. 87 00:06:51,070 --> 00:06:57,340 Experts from fields such as computer science, ethics, law, and social sciences must work together 88 00:06:57,340 --> 00:07:03,210 to develop and implement effective transparency, explainability, and accountability mechanisms. 89 00:07:03,660 --> 00:07:09,510 For instance, legal scholars can provide insights into regulatory frameworks, while ethicists can 90 00:07:09,510 --> 00:07:13,110 offer guidance on aligning AI systems with ethical principles. 91 00:07:13,230 --> 00:07:18,300 This collaborative approach ensures that diverse perspectives are considered and that AI systems are 92 00:07:18,300 --> 00:07:20,460 developed and deployed responsibly. 93 00:07:22,770 --> 00:07:29,550 Education and training are also vital components of promoting transparency, explainability, and accountability 94 00:07:29,550 --> 00:07:30,300 in AI. 95 00:07:30,600 --> 00:07:36,570 By equipping AI developers, policymakers, and other stakeholders with the knowledge and skills needed 96 00:07:36,570 --> 00:07:42,750 to understand and implement these principles, we can foster a culture of responsibility and trust in 97 00:07:42,750 --> 00:07:43,410 AI. 98 00:07:43,890 --> 00:07:49,680 Educational initiatives should include courses, workshops and certifications such as the AI Governance 99 00:07:49,680 --> 00:07:54,990 Professional Certification, which focuses on responsible AI principles and trustworthy AI. 100 00:07:55,020 --> 00:08:00,590 These initiatives should emphasize the importance of transparency, Explainability and accountability, 101 00:08:00,590 --> 00:08:04,400 and provide practical tools and techniques for achieving these principles. 102 00:08:07,010 --> 00:08:13,160 In conclusion, transparency, explainability, and accountability are essential components of responsible 103 00:08:13,190 --> 00:08:15,710 AI principles and trustworthy AI. 104 00:08:16,220 --> 00:08:21,290 These principles are critical for building and maintaining public trust in AI systems, ensuring that 105 00:08:21,290 --> 00:08:25,820 these systems are fair, ethical, and aligned with societal values. 106 00:08:26,360 --> 00:08:32,810 Transparency enables stakeholders to understand how AI systems operate and make decisions, while explainability 107 00:08:32,810 --> 00:08:36,350 provides insights into the interpretability of AI decisions. 108 00:08:36,530 --> 00:08:42,530 Accountability ensures that entities developing and deploying AI systems are held responsible for their 109 00:08:42,530 --> 00:08:45,320 actions and the outcomes of these systems. 110 00:08:45,770 --> 00:08:50,900 By addressing the challenges associated with these principles through interdisciplinary collaboration, 111 00:08:50,900 --> 00:08:57,890 education and training, we can promote responsible AI governance and foster a culture of trust in AI. 112 00:08:58,250 --> 00:08:59,240 Arthur.