1 00:00:00,050 --> 00:00:00,590 Lesson. 2 00:00:00,590 --> 00:00:03,680 Ethical dilemmas in AI governance and deployment. 3 00:00:04,040 --> 00:00:07,310 Ethical dilemmas in AI governance and deployment. 4 00:00:08,000 --> 00:00:13,430 Artificial intelligence has the potential to revolutionize numerous sectors, from health care to finance, 5 00:00:13,430 --> 00:00:15,290 transportation and beyond. 6 00:00:15,710 --> 00:00:21,230 However, the deployment and governance of AI systems are fraught with ethical dilemmas that necessitate 7 00:00:21,230 --> 00:00:22,640 rigorous scrutiny. 8 00:00:23,150 --> 00:00:27,890 One of the primary concerns in AI ethics is the issue of bias and fairness. 9 00:00:28,550 --> 00:00:34,460 AI systems learn from data, and if that data contains biases, the AI will likely perpetuate and even 10 00:00:34,460 --> 00:00:36,350 exacerbate those biases. 11 00:00:36,560 --> 00:00:39,080 For example, a study by Obermeyer et al. 12 00:00:39,080 --> 00:00:44,720 Found that an algorithm used in the US health care system to predict which patients would benefit from 13 00:00:44,720 --> 00:00:50,000 additional medical care systematically favored white patients over black patients. 14 00:00:50,570 --> 00:00:57,680 This bias arose because the algorithm used healthcare costs as a proxy for health needs, and historically, 15 00:00:57,680 --> 00:01:00,470 black patients have had less access to health care. 16 00:01:00,500 --> 00:01:06,900 This example underscores the importance of ensuring that AI systems are trained on diverse and representative 17 00:01:06,900 --> 00:01:10,530 data sets to avoid reinforcing existing inequalities. 18 00:01:12,000 --> 00:01:16,590 Another ethical dilemma in AI governance is the question of accountability. 19 00:01:16,740 --> 00:01:21,180 When an AI system makes a decision, who is responsible for the outcome? 20 00:01:21,420 --> 00:01:28,020 This issue is particularly pressing in high stakes domains such as autonomous driving or medical diagnostics. 21 00:01:28,260 --> 00:01:34,890 For instance, if an autonomous vehicle causes an accident, is the manufacturer liable or is the responsibility 22 00:01:34,890 --> 00:01:39,210 shared with the software developers, the data providers, or even the users? 23 00:01:39,660 --> 00:01:45,870 The complexity of AI systems, which often involve numerous stakeholders, makes it challenging to pinpoint 24 00:01:45,900 --> 00:01:49,200 accountability, according to Floridian Cowles. 25 00:01:49,230 --> 00:01:55,890 Establishing clear lines of accountability is crucial for maintaining public trust in AI technologies. 26 00:01:56,370 --> 00:02:01,380 They argue that AI governance frameworks should include mechanisms for tracing decisions back to the 27 00:02:01,380 --> 00:02:07,080 responsible parties and ensuring that those parties can be held accountable for their actions. 28 00:02:07,080 --> 00:02:07,220 Patients. 29 00:02:08,900 --> 00:02:12,770 Privacy is another significant ethical concern in AI deployment. 30 00:02:12,800 --> 00:02:18,500 AI systems often require vast amounts of data, much of which may be personal or sensitive. 31 00:02:18,950 --> 00:02:23,360 The use of AI in surveillance, for example, raises serious privacy issues. 32 00:02:23,720 --> 00:02:29,450 In China, AI powered surveillance systems are used to monitor and control the population, leading 33 00:02:29,480 --> 00:02:33,260 to widespread concerns about privacy and human rights violations. 34 00:02:33,590 --> 00:02:38,990 The European Union's General Data Protection Regulation is a step towards addressing these concerns 35 00:02:38,990 --> 00:02:45,110 by granting individuals greater control over their personal data and imposing strict requirements on 36 00:02:45,110 --> 00:02:46,910 data handling and processing. 37 00:02:47,210 --> 00:02:53,330 However, the global nature of AI deployment means that privacy protections must be harmonized across 38 00:02:53,330 --> 00:02:55,160 jurisdictions to be effective. 39 00:02:57,200 --> 00:03:01,220 Transparency is another key ethical issue in AI governance. 40 00:03:01,670 --> 00:03:08,510 AI systems, particularly those based on machine learning, often operate as black boxes whose internal 41 00:03:08,510 --> 00:03:11,950 workings are not easily understood even by their developers. 42 00:03:12,460 --> 00:03:17,800 This lack of transparency can make it difficult to understand how decisions are made, which in turn 43 00:03:17,830 --> 00:03:20,980 hampers efforts to ensure accountability and fairness. 44 00:03:21,280 --> 00:03:26,830 For example, a hiring algorithm developed by Amazon was found to be biased against women, but the 45 00:03:26,830 --> 00:03:32,590 exact reasons for this bias were not immediately clear because the algorithm's decision making process 46 00:03:32,590 --> 00:03:33,640 was opaque. 47 00:03:35,200 --> 00:03:41,380 To address this issue, AI governance frameworks should require that AI systems are designed and deployed 48 00:03:41,380 --> 00:03:46,360 in ways that make their decision making processes understandable and explainable. 49 00:03:47,020 --> 00:03:52,570 This could involve the use of white box models, which are inherently more interpretable, or the development 50 00:03:52,570 --> 00:03:58,000 of tools and techniques for explaining the decisions of more complex black box models. 51 00:03:59,260 --> 00:04:05,440 The ethical dilemmas in AI governance and deployment also extend to the potential for job displacement 52 00:04:05,440 --> 00:04:07,270 and economic inequality. 53 00:04:07,900 --> 00:04:13,780 As AI systems become more capable, there is a risk that they will replace human workers in a wide range 54 00:04:13,780 --> 00:04:19,910 of jobs, from manufacturing to customer service and even professional roles such as legal and medical 55 00:04:19,910 --> 00:04:20,870 analysis. 56 00:04:21,290 --> 00:04:28,100 According to a report by the McKinsey Global Institute, up to 375 million workers worldwide may need 57 00:04:28,100 --> 00:04:33,770 to switch occupations or acquire new skills by 2030 due to automation and AI. 58 00:04:34,370 --> 00:04:39,710 This potential for job displacement raises ethical questions about the responsibility of governments, 59 00:04:39,710 --> 00:04:46,280 businesses and society at large to support affected workers through retraining programs, social safety 60 00:04:46,310 --> 00:04:52,730 nets, and other measures, ensuring that the benefits of AI are broadly shared rather than concentrated 61 00:04:52,730 --> 00:04:54,020 in the hands of a few. 62 00:04:54,050 --> 00:04:57,560 Is essential for promoting social and economic justice. 63 00:04:58,400 --> 00:05:03,980 Another profound ethical dilemma in AI deployment is the potential for AI to be used in ways that harm 64 00:05:03,980 --> 00:05:05,780 individuals or society. 65 00:05:06,230 --> 00:05:12,740 For example, AI technologies can be used to create deepfakes highly realistic, but fake videos or 66 00:05:12,740 --> 00:05:18,270 audio recordings that can be used to spread misinformation, commit fraud, or harass individuals. 67 00:05:18,780 --> 00:05:24,030 The proliferation of deepfakes poses significant risks to public trust and social cohesion. 68 00:05:24,330 --> 00:05:30,180 Moreover, AI can be weaponized in military applications, raising ethical questions about the development 69 00:05:30,180 --> 00:05:36,690 and use of autonomous weapons systems that can make life and death decisions without human intervention. 70 00:05:37,290 --> 00:05:43,080 The international community is grappling with how to regulate such uses of AI to prevent harm, and 71 00:05:43,080 --> 00:05:48,150 ensure that these technologies are used in ways that align with humanitarian principles. 72 00:05:50,310 --> 00:05:55,800 Ethical dilemmas in AI governance and deployment are not limited to negative outcomes. 73 00:05:56,190 --> 00:05:59,550 They also include the challenge of balancing competing values. 74 00:05:59,730 --> 00:06:06,420 For instance, while transparency and explainability are important for ensuring accountability and fairness, 75 00:06:06,420 --> 00:06:13,140 they may come into conflict with other values such as privacy and intellectual property rights, requiring 76 00:06:13,140 --> 00:06:19,050 companies to disclose the inner workings of their AI systems could expose proprietary information and 77 00:06:19,050 --> 00:06:21,460 potentially undermine innovation. 78 00:06:21,820 --> 00:06:28,750 Similarly, efforts to protect privacy by anonymizing data can sometimes reduce the accuracy and effectiveness 79 00:06:28,750 --> 00:06:32,920 of AI systems, leading to trade offs between privacy and utility. 80 00:06:33,370 --> 00:06:38,980 Navigating these tradeoffs requires careful consideration of the specific context and the values at 81 00:06:38,980 --> 00:06:44,110 stake, as well as the development of governance frameworks that can accommodate diverse and sometimes 82 00:06:44,110 --> 00:06:45,460 conflicting interests. 83 00:06:47,620 --> 00:06:53,500 To address the ethical dilemmas in AI governance and deployment, a multi-stakeholder approach is essential. 84 00:06:54,100 --> 00:06:59,740 This involves collaboration between governments, businesses, civil society organizations, and the 85 00:06:59,740 --> 00:07:05,350 academic community to develop and implement ethical guidelines and regulatory frameworks. 86 00:07:06,580 --> 00:07:12,550 For example, the E2E Global Initiative on Ethics of Autonomous and Intelligent Systems has developed 87 00:07:12,550 --> 00:07:18,970 a set of principles for ethical AI, emphasizing the importance of human rights, well-being, accountability, 88 00:07:18,970 --> 00:07:20,260 and transparency. 89 00:07:20,920 --> 00:07:26,410 Similarly, the European Commission's High level expert Group on Artificial Intelligence has proposed 90 00:07:26,410 --> 00:07:32,140 ethical guidelines for trustworthy AI, which include principles such as respect for human autonomy, 91 00:07:32,140 --> 00:07:35,740 prevention of harm, fairness and explicability. 92 00:07:36,370 --> 00:07:42,100 These initiatives represent important steps towards ensuring that AI is developed and deployed in ways 93 00:07:42,100 --> 00:07:44,830 that are ethical and socially responsible. 94 00:07:45,640 --> 00:07:51,250 In conclusion, the ethical dilemmas in AI governance and deployment are complex and multifaceted, 95 00:07:51,250 --> 00:07:57,400 encompassing issues of bias and fairness, accountability, privacy, transparency, job displacement, 96 00:07:57,400 --> 00:07:58,780 and potential harm. 97 00:07:59,380 --> 00:08:04,120 Addressing these dilemmas requires a comprehensive and nuanced approach that takes into account the 98 00:08:04,120 --> 00:08:07,030 diverse and sometimes conflicting values at stake. 99 00:08:08,350 --> 00:08:13,870 By fostering collaboration between stakeholders and developing robust ethical guidelines and regulatory 100 00:08:13,870 --> 00:08:19,630 frameworks, it is possible to harness the benefits of AI while mitigating its risks and ensuring that 101 00:08:19,630 --> 00:08:25,210 its development and deployment align with principles of justice, fairness, and human well-being.