1 00:00:00,050 --> 00:00:05,840 Case study ensuring ethical and effective deployment of high risk AI in medical diagnostics. 2 00:00:05,870 --> 00:00:12,140 The ethical, safe and effective deployment of high risk AI systems and foundation models demands rigorous 3 00:00:12,140 --> 00:00:13,490 governance frameworks. 4 00:00:13,910 --> 00:00:20,090 This narrative delves into a realistic scenario involving medical AI applications, reflecting the complex 5 00:00:20,090 --> 00:00:22,940 interplay between innovation and regulation. 6 00:00:24,320 --> 00:00:29,960 Doctor Emily Rodriguez, a leading researcher at a renowned tech company, spearheaded the development 7 00:00:29,960 --> 00:00:33,260 of an AI diagnostic system named ACU health. 8 00:00:33,650 --> 00:00:39,470 This tool was designed to analyze medical images and assist radiologists in identifying various conditions, 9 00:00:39,470 --> 00:00:41,420 such as tumors and fractures. 10 00:00:42,170 --> 00:00:47,210 Given the high stakes in health care, the deployment of ACU health required stringent adherence to 11 00:00:47,240 --> 00:00:49,970 regulatory standards and ethical guidelines. 12 00:00:51,410 --> 00:00:57,230 Doctor Rodriguez's team meticulously documented the dataset used for training ACU health, including 13 00:00:57,230 --> 00:01:01,190 patient demographics, imaging techniques, and potential biases. 14 00:01:01,670 --> 00:01:07,540 They were transparent about the algorithms employed and the decision making processes of the AI system. 15 00:01:07,810 --> 00:01:13,840 This level of transparency allowed stakeholders, including healthcare providers and patients, to comprehend 16 00:01:13,870 --> 00:01:17,980 the system's functioning and anticipate possible biases or errors. 17 00:01:18,550 --> 00:01:22,600 Could Doctor Rodriguez's team have done more to ensure transparency? 18 00:01:22,630 --> 00:01:28,120 They might have considered making the documentation publicly available, allowing independent researchers 19 00:01:28,120 --> 00:01:34,210 to scrutinize and validate their work to establish accountability. 20 00:01:34,240 --> 00:01:39,040 The team implemented robust monitoring mechanisms to track AQ Health's performance. 21 00:01:39,040 --> 00:01:40,210 Post-deployment. 22 00:01:40,720 --> 00:01:46,900 They partnered with several hospitals to collect real time data on the AI systems outcomes, and rectified 23 00:01:46,900 --> 00:01:48,850 any harmful impacts promptly. 24 00:01:49,540 --> 00:01:53,620 What would be the consequences if such monitoring systems were not in place? 25 00:01:54,250 --> 00:02:00,220 The absence of these mechanisms would likely erode trust among users and potentially lead to unchecked 26 00:02:00,220 --> 00:02:01,780 harmful consequences. 27 00:02:01,780 --> 00:02:05,200 Ultimately undermining the technology's credibility. 28 00:02:05,980 --> 00:02:08,860 Ethical considerations were integral to the project. 29 00:02:09,220 --> 00:02:15,040 The team adhered to principles of fairness, privacy and non-discrimination, ensuring that ACU health 30 00:02:15,040 --> 00:02:18,100 did not perpetuate existing health care disparities. 31 00:02:18,550 --> 00:02:24,040 They conducted rigorous testing across a diverse patient population to confirm the tool's accuracy and 32 00:02:24,040 --> 00:02:24,850 fairness. 33 00:02:25,720 --> 00:02:29,800 Is it possible to eliminate all biases in AI systems entirely? 34 00:02:29,830 --> 00:02:36,070 While complete elimination of bias is challenging, continuous efforts to identify and mitigate biases 35 00:02:36,070 --> 00:02:41,260 can significantly reduce their impact, thus enhancing the system's overall fairness. 36 00:02:42,250 --> 00:02:48,130 Regulatory bodies required third party audits and certifications to verify ACU Health's compliance with 37 00:02:48,130 --> 00:02:49,510 established standards. 38 00:02:49,870 --> 00:02:55,810 This included performance evaluations under various scenarios to ensure reliability and robustness. 39 00:02:57,100 --> 00:03:01,270 Why are third party audits crucial in validating high risk AI systems? 40 00:03:01,750 --> 00:03:07,300 Independent audits provide an unbiased assessment of the AI systems performance, thereby bolstering 41 00:03:07,330 --> 00:03:10,620 trust and ensuring adherence to regulatory standards. 42 00:03:11,430 --> 00:03:16,740 Foundation models like the one underlying ACU health were trained on vast data sets sourced from the 43 00:03:16,740 --> 00:03:17,430 internet. 44 00:03:18,120 --> 00:03:24,720 This process introduced inherent biases, which the team addressed through robust data curation practices. 45 00:03:25,380 --> 00:03:30,300 They ensured that training data was representative and free from inappropriate content. 46 00:03:30,840 --> 00:03:36,780 Techniques such as differential privacy protected individual data points within the training datasets. 47 00:03:36,810 --> 00:03:39,090 Enhancing privacy protections can. 48 00:03:39,120 --> 00:03:43,830 Data curation practices fully mitigate the risks of bias in foundation models. 49 00:03:44,250 --> 00:03:49,740 While data curation significantly reduces the risks, ongoing vigilance and iterative improvements are 50 00:03:49,770 --> 00:03:53,130 necessary to continually address emerging biases. 51 00:03:54,360 --> 00:03:59,370 To prevent misuse, the team established clear usage guidelines for ACU health. 52 00:03:59,790 --> 00:04:05,670 These guidelines stipulated acceptable use cases and prohibited applications that could cause harm. 53 00:04:06,000 --> 00:04:11,130 For instance, the tool was not to be used for generating deepfakes or spreading misinformation. 54 00:04:11,670 --> 00:04:17,100 Developers provided tools for users to interpret and control the AI systems outputs, thus enhancing 55 00:04:17,130 --> 00:04:19,470 transparency and user agency. 56 00:04:19,500 --> 00:04:24,420 How do clear usage guidelines contribute to the ethical deployment of AI systems? 57 00:04:24,870 --> 00:04:31,020 Clear guidelines set boundaries for acceptable use, helping prevent misuse and ensuring that AI technologies 58 00:04:31,020 --> 00:04:33,420 serve their intended ethical purposes. 59 00:04:34,800 --> 00:04:40,590 Regulatory frameworks for high risk AI systems needed to be adaptive to keep pace with technological 60 00:04:40,590 --> 00:04:41,550 advancements. 61 00:04:41,940 --> 00:04:48,120 Policymakers engaged with AI experts, stakeholders, and the public to develop regulations that balanced 62 00:04:48,120 --> 00:04:49,710 innovation with safeguards. 63 00:04:49,740 --> 00:04:56,670 The European Union's AI act, for example, proposed a risk based approach to AI regulation requiring 64 00:04:56,670 --> 00:05:02,760 stringent obligations for high risk systems, including conformity assessments and continuous monitoring. 65 00:05:02,790 --> 00:05:07,800 How can adaptive regulatory frameworks benefit the deployment of AI technologies? 66 00:05:08,340 --> 00:05:13,620 Adaptive frameworks ensure that regulations remain relevant and effective amidst rapid technological 67 00:05:13,620 --> 00:05:14,310 changes. 68 00:05:14,310 --> 00:05:18,390 fostering innovation while safeguarding against potential harms. 69 00:05:19,800 --> 00:05:23,250 Statistics underscored the critical need for robust governance. 70 00:05:23,280 --> 00:05:25,230 A study by Obermeyer et al. 71 00:05:25,260 --> 00:05:31,710 Revealed racial bias in a widely used healthcare algorithm, highlighting the potential harms of unregulated 72 00:05:31,740 --> 00:05:32,970 AI systems. 73 00:05:33,450 --> 00:05:35,190 Similarly, Bender et al. 74 00:05:35,220 --> 00:05:40,950 Pointed out risks associated with large language models, including the amplification of biases and 75 00:05:40,950 --> 00:05:42,960 the generation of harmful content. 76 00:05:42,990 --> 00:05:48,210 These findings emphasize the importance of rigorous oversight to prevent adverse outcomes. 77 00:05:49,440 --> 00:05:55,800 A successful governance frameworks illustrated the feasibility of implementing robust requirements for 78 00:05:55,800 --> 00:05:57,570 high risk AI systems. 79 00:05:58,470 --> 00:06:03,480 The Food and Drug Administration in the United States had established guidelines for the approval of 80 00:06:03,480 --> 00:06:09,540 AI based medical devices, ensuring their safety and effectiveness before they reached patients. 81 00:06:09,810 --> 00:06:16,350 These guidelines included requirements for clinical validation, transparency of algorithms, and post-market 82 00:06:16,350 --> 00:06:17,250 surveillance. 83 00:06:17,380 --> 00:06:21,580 Providing a comprehensive framework for AI governance in health care. 84 00:06:21,910 --> 00:06:25,600 How can other sectors learn from the FDA's approach to AI governance? 85 00:06:26,020 --> 00:06:32,170 Other sectors can adopt similar comprehensive frameworks emphasizing clinical validation, transparency, 86 00:06:32,170 --> 00:06:39,910 and continuous monitoring to ensure the safe and effective deployment of AI technologies in the private 87 00:06:39,910 --> 00:06:40,450 sector. 88 00:06:40,450 --> 00:06:46,090 Companies like Google developed internal guidelines for the ethical development and deployment of AI. 89 00:06:46,570 --> 00:06:52,540 Google's AI principles emphasized fairness, privacy, and accountability, guiding the company's approach 90 00:06:52,540 --> 00:06:55,990 to AI governance by adhering to these principles. 91 00:06:56,020 --> 00:07:01,660 Google aimed to ensure that its AI technologies were developed responsibly and used for the benefit 92 00:07:01,660 --> 00:07:02,620 of society. 93 00:07:03,580 --> 00:07:08,230 What role do internal guidelines play in the ethical deployment of AI technologies? 94 00:07:08,980 --> 00:07:14,890 Internal guidelines reflect a company's commitment to ethical principles and provide a structured approach 95 00:07:14,890 --> 00:07:18,010 to responsible AI development and deployment. 96 00:07:19,240 --> 00:07:25,680 In conclusion, the deployment of high risk AI systems and foundation models such as ACU health, involves 97 00:07:25,680 --> 00:07:32,550 multifaceted requirements that encompass transparency, accountability, ethical considerations, rigorous 98 00:07:32,550 --> 00:07:34,920 testing, and adaptive regulation. 99 00:07:35,310 --> 00:07:40,710 Transparency is achieved through meticulous documentation and stakeholder engagement, allowing for 100 00:07:40,710 --> 00:07:43,800 the identification and mitigation of biases and errors. 101 00:07:43,830 --> 00:07:49,770 Accountability is ensured through robust monitoring mechanisms, providing a means to rectify harmful 102 00:07:49,770 --> 00:07:52,500 impacts and maintain trust among users. 103 00:07:53,160 --> 00:07:58,470 Ethical considerations are integral, requiring adherence to principles of fairness, privacy, and 104 00:07:58,470 --> 00:07:59,700 non-discrimination. 105 00:08:00,270 --> 00:08:05,970 Rigorous testing and validation across diverse scenarios confirm the reliability and robustness of AI 106 00:08:06,000 --> 00:08:06,810 systems. 107 00:08:06,810 --> 00:08:12,270 While third party audits provide independent verification of compliance with regulatory standards. 108 00:08:13,290 --> 00:08:19,320 Adaptive regulatory frameworks developed through engagement with AI experts, stakeholders and the public 109 00:08:19,320 --> 00:08:26,970 ensure that regulations remain relevant and effective amidst technological advancements, the deployment 110 00:08:26,970 --> 00:08:32,130 of foundation models necessitates comprehensive governance to safeguard against misuse. 111 00:08:32,160 --> 00:08:37,800 Robust data curation practices and techniques like differential privacy, enhanced privacy protections 112 00:08:37,800 --> 00:08:39,180 and mitigate biases. 113 00:08:39,810 --> 00:08:46,350 Clear usage guidelines prevent misuse and ensure that AI technologies serve their ethical purposes. 114 00:08:47,280 --> 00:08:52,620 Successful governance frameworks, such as those implemented by the FDA and companies like Google, 115 00:08:52,620 --> 00:08:57,240 provide structured approaches to responsible AI development and deployment. 116 00:08:58,260 --> 00:09:03,480 Through this detailed case study, students can compare their responses to the thought provoking questions 117 00:09:03,480 --> 00:09:07,530 and enhance their understanding and application of the lesson material. 118 00:09:08,070 --> 00:09:13,920 The integration of robust oversight mechanisms, clear guidelines, and continuous engagement with the 119 00:09:13,920 --> 00:09:20,340 public is crucial in achieving a balance between innovation and safeguards, ensuring the safe, ethical 120 00:09:20,340 --> 00:09:25,290 and effective deployment of high risk AI systems and foundation models.