Ban AI CP and AI Good Bad - Michigan 7 2024 JRY

Notes
Huge thank you to all those that contributed. For a lot of you it was your first-time cutting cards, and I am wildly impressed with the work you all did!
Alicia, Alison, Austin, Celena, Charles, Claire, Clyde, Emily, Kiran, Nethra, Patrick, Sahana, Tarang, Vedsai, Riley, Chelsea
File usage:
AFFs with a regulation good for AI innovation.
Probably needs a topic area DA as the NB.
Impact Turns – AI Bad
I/L – Regs Increase Adoption
Investor confidence
AFFs weak regulatory framework spurs AI innovation – only the CPs complete ban avoids it.
Tartaro et. al 23 (Alessio Tartaro University of Sassari Italy; Adam Leon Smith Dragonfly, Barcelona, Catalonia, Spain Chair, Fellows Technical Advisory Group, British Computer Society; Patricia Shaw Beyond Reach Consulting, United Kingdom Trustee Director and Chair, Society for Computers and Law Director, ITechLaw Association “ASSESSING THE IMPACT OF REGULATIONS AND STANDARDS ON INNOVATION IN THE FIELD OF AI”, 2023) RIP
In order to extend this analysis, it is useful to compare the draft EU AI regulation to the GDPR [22]. The GDPR is not supported by standards, however it does allow for third-party GDPR certifications to be awarded. These certification schemes do not provide presumption of conformity with GDPR [1]. Such certification schemes have also been slow to manifest, and there is only one that is endorsed by the European Data Privacy Board [20].
The complaint-based and enforcement-led approach of GDPR means that levels of compliance are determined by each organisation’s judgement of the risk and impact of fines and reputational damage, and limited by the number of resources available to data protection authorities. Additionally, there is growing evidence that GDPR has resulted in less competition in some areas, and favoured large online platforms [23]. One of the reasons for this is that companies are less willing to trust smaller companies with data, as it may impact their own compliance [24]. Consequently, the decision to use regulations without standards has had a substantial impact on innovation in the uncertain data sector. It should be acknowledged, however, that this impact has not only been negative. The emergence of privacy-enhancing technologies is linked to the GDPR, and represents a positive impact of the regulation on innovation.
Many innovative developments come from small enterprises, who have some advantages over larger companies [35] [8] [21]. However, one disadvantage that startups usually have is a lack of financial resources. As Forbes magazine pointed out [32], the legislation is deliberately vague — much like the EU’s proposed AI act it is vague in order to remain future-proof. The result of this is that many large firms are spending as much of 40% of their GDPR compliance budget on legal advice in order to interpret the meaning of many of the requirements. Clearly, smaller firms are less able to rely on legal advice, and less able to pay fines if the legal advice is not supported by decisions by the regulators.
In contrast, the proposed AI Act is based on conformity assessment processes prior to placing a product on the market. Conformity assessments require clear and unambiguous technical standards. Such standards are obtainable by businesses for a negligible fee. Conformity assessments come at a cost, but not a significant cost compared with the cost of actually complying with the requirements [19], or obtaining expensive legal advice.4
Innovative businesses that remain unclear about compliance can participate in regulatory sandboxes, expected within each member state, that allow a “safe space” to work with regulators on innovation. Since the UK Financial Conduct Authority launched its sandbox in 2016, it has supported more than 700 firms and increased their average speed to market by 40% compared with the regulator’s standard authorisation time [33]. However, sandboxes are limited by a number of factors. Firstly, the willingness of companies to subject themselves to regulatory scrutiny before they need to. Secondly, the ability of companies to share data with regulators. While the proposed EU AI Act modifies the GDPR to allow companies to process data in order to comply with aspects of the regulation, the sandboxes are expected to be in operation long before the proposed AI Act.
Also, as previously noted, regulation in some areas has sparked innovation. It is likely similar innovations will result from AI regulation, this may be in technical compliance and post-market monitoring, and may be in the creation of new kinds of roles and professions to help govern AI systems. Early work by standardisation bodies to define the criteria for competent persons to assess AI systems indicates that no single person is likely to have all the skills required - as this encompasses a diverse set of socio-legal-technical competencies. Technical resources are unaccustomed with considering legal and ethical requirements, legal experts rarely have data science skills, and AI ethicists are more focused on the broad and long-term impacts of AI systems on individuals and society. Therefore, a comprehensive assessment of AI systems requires a team of experts from different disciplines, including technologists, legal and ethical experts. The development of this multi-disciplinary expertise and the establishment of new processes to combine and integrate them within organisations might be one of the major innovations brought about by the regulation of AI.
Social Buy-in
Regulation spur investment in AI - increases social trust and stifles decent.
Yadav 24 (Rahul Yadav is the chief technology officer at Milestone Systems. He holds a Global Executive MBA from INSEAD in Fontainebleau, France, and a Master of Technology (M.Tech.) in Digital Communication from the National Institute of Technology, Bhopal, India, 1-26-2024, "Balancing AI Innovation and Regulation: The Potential Impact on U.S. Security Companies and Practitioners", ASIS, https://www.asisonline.org/security-management-magazine/articles/2024/01/balancing-ai-innovation-and-regulation/) RIP
AI and machine learning technologies have become integral to many applications and services within the security industry. The adoption of these technologies in security continues to rise rapidly as the performance of these technologies improves and new applications emerge. The ability to extract insights from massive amounts of data provides security teams with augmented awareness of risks and events.
Video analytics relies on AI for tasks like pattern recognition, facial recognition, object classification, and anomaly detection within video data. Smart cameras equipped with machine learning can identify threats and suspicious behavior in real time. AI also unlocks valuable insights from security data through techniques like predictive analytics. AI technologies are finding use cases across access control, video security, alarm systems, and much more.
While stricter regulations may increase costs associated with developing, testing, and monitoring AI systems, well-crafted policies will push the security industry toward more responsible AI practices that consider ethics and potential harm. This will benefit customers, individuals, and businesses over the long term as potential negative impacts are minimized. Regulations can also improve public trust and adoption of AI solutions as the technologies become more accountable, fair, and transparent in their workings and decisions.
Polls show – majority of global citizens nervous about the future of AI
McClure ’24 – (Paul McClure, Bachelor of Nursing, Graduate Diplomas in Advanced Nursing and Psychological Studies, a Master of Laws (Juris Doctor), and a Bachelor of Arts in journalism and creative writing, previously worked as an intensive care nurse at one of Australia’s busiest hospital and a criminal defense lawyer, “The bad and ugly: AI is harmful, unreliable, and running out of data”, 4/22/24, New Atlas, https://newatlas.com/technology/ai-index-report-negatives/)
Public opinion has shifted: Half of us are nervous about AI's impact
According to surveys conducted by Ipsos, while 52% of the global public expressed nervousness about products and services that used AI, up from 39% in 2022, Australians were the most nervous, followed by Brits, Canadians, and Americans.
Globally, 57% of people expect AI to change how they do their jobs in the next five years, with more than a third (36%) expecting AI to replace them in the same time frame. Understandably, older generations are less concerned that AI will have a substantial effect than younger ones: 46% of boomers versus 66% of Gen Z.
Global Public Opinion on Artificial Intelligence (GPO-AI) data presented in the AI Impact report showed that 49% of global citizens were most concerned that, over the next few years, AI would be misused or used for nefarious purposes; 45% were concerned it would be used to violate an individual’s privacy. People were less concerned about unequal access to AI (26%) and its potential for bias and discrimination (24%).
Concerning the US specifically, data from the Pew Research Center showed that a far greater number of Americans were more concerned than excited about AI tech. The figures jumped from 37% in 2021 to 52% in 2023.
Impact---Disease
Bioterror
AI can be used to create deadly pathogens – extinction.
Egan and Rosenbach 23 (Janet Egan, public policy professional with a background in national security, Master of Public Policy at Harvard Kennedy School, Eric Rosenbach, Director, Defense, Emerging Technology, and Strategy Program, "Biosecurity in the Age of AI: What’s the Risk?", 6 November 2023, Belfer Center for Science and International Affairs, https://www.belfercenter.org/publication/biosecurity-age-ai-whats-risk)
“The biggest issue with AI is actually going to be … its use in biological conflict,” according to former Google CEO, Eric Schmidt.[1] And he’s not the only AI expert worried. In his testimony before the Senate Judiciary Committee Subcommittee on Privacy, Technology, and the Law, the CEO of Anthropic, Dario Amodei, warned that in just two to three years, AI has the potential to “greatly widen the range of actors with the technical capability to conduct a large-scale biological attack.”[2] OpenAI’s Sam Altman has called for regulation on AI models “that could help create novel biological agents.”[3]President Biden’s recent Executive Order on Safe, Secure and Trustworthy Development and Use of Artificial Intelligence explicitly tasks relevant agencies with assessing the ways in which AI can increase, and potentially help mitigate, biosecurity risks.[4]But what exactly has experts and officials so worried?
Biological weapons are not a new concern
The deliberate use of microorganisms like viruses or bacteria to cause disease or death[5] has a long and terrible history: Japan weaponized typhus and cholera in World War II,[6] and the Soviet Union’s bioweapon program throughout the Cold War included producing and stockpiling smallpox, anthrax, and drug-resistant plague.[7] The United States also developed its own bioweapon program in this period, including anthrax and Q-fever, until it was terminated by President Nixon in 1969.[8]
Today, the Centers for Disease Control warns that the bacteria that causes anthrax is one of the most likely agents to be used in a biological attack.[9] To date, the development, containment, and deployment of such weapons have required significant resourcing and expertise.[10] This does not mean such weapons have only been accessible to nation-states, but it has ensured that only a limited number of actors have had the capability to develop them.
Researchers are concerned AI might increase bioweapon knowhow
AI experts are concerned that highly capable AI models could assist non-experts in designing, synthesizing, and using these weapons, thus expanding the pool of actors that could access these dangerous capabilities. Concerns are increasingly centered around future capabilities, rather than those of the present day.
MIT students recently demonstrated how large language model (LLM) chatbots could be used to help non-experts understand the process of manufacturing risky pathogens.[11] Within one hour, students without science backgrounds had used the chatbots to list four viruses capable of causing a pandemic, identify reverse genetics as a means to manufacture them and suggest acquisition methods that could help bypass misuse screening.[12] But in this and other experiments, such as a recent report by RAND, LLMs have not yet generated explicit, directly actionable instructions on how to create bioweapons, and it is not clear that today’s LLMs offer significant advantages over what can already be gleaned from the internet.[13]
In the future, more advanced AI capabilities may cause greater concern, as LLMs increasingly enable the synthesis and production of sophisticated and accurate insights at an expert level.[14] Even less advanced models, when focused on biological data, might give rise to biological risks. In 2022, an experiment revealed how AI used in pharmaceutical design could be tweaked to design highly toxic chemicals instead.[15]
As AI models are increasingly trained and deployed to aid research in areas like pathogens and cancers, they may have the potential to be similarly co-opted to design new and more harmful pathogens. Given the potentially catastrophic impacts of biological agents, it will be necessary for AI, biology, and broader experts to collaborate on better understanding these risks.
Biological science capabilities also shape bioweapon risks
Advances in synthetic biology are making biological development tools more readily accessible. A DIY Bacterial Gene Engineering CRISPR kit is purchasable online for as little as $85 USD,[16] and scientists regularly order custom DNA sequences from commercial labs, only some of which have committed to screening for misuse potential.[17] But it is not clear that AI-aided know-how and currently available commercial tools are sufficient for bioweapon risks to materialize.
The process of developing, storing, and deploying a novel pathogen or bioweapon is further complicated by the danger of handling such materials while evading detection presents additional challenges. Further analysis is needed on the current risk mitigation mechanisms employed by the private sector and its level of risk awareness, as well as the impact of pandemic preparation and response capabilities.
Deterrence and detection programs may need revision
If future AI and biological capabilities expand access to bioweapons, existing detection and deterrence arrangements may fall short. Some key methods of limiting biochemical weapon proliferation are aimed at nation-state actors. For example, international treaties like the Biochemical Weapons Convention aim to establish international norms against the manufacture, trade, or use of such weapons, and generate international pressure (including through complaints to the United Nations Security Council) to promote compliance. Yet this scheme would be irrelevant to many adversarial state and non-state actors, including apocalyptic groups, that may also not care about retaliation or blowback effects.[18]
New or novel biological agents may also potentially evade current detection and early warning mechanisms. The recent Executive Order takes some action on this front, tasking agencies to develop stronger standards for the screening of biological synthesis requests.[19] But further updates may be required. For example, the US Department of Homeland Security Biowatch Program, introduced in 2003 in response to the 2001 anthrax attacks,[20] operates in 30 jurisdictions across the US.[21] It monitors the air for select biological agents, such as the smallpox virus,[22] but would not necessarily be primed to pick up a new biological compound that AI might enable. Any evaluation of emerging risks should consider whether further updates to such regimes are warranted.
Conclusion
Developing a nuanced understanding of how technologies are changing biosecurity risks requires multidisciplinary expertise. The Defense, Emerging Technology, and Strategy Program at the Belfer Center will bring together experts across technology, biology, security, health, and international policy domains to grapple with these issues. Proactively understanding and addressing AI-biosecurity risks is key to mitigating catastrophic risk, as well as enabling responsible AI innovation.
Impact---Enviro
General
AI will be responsible for 14% of all global emissions – e-waste, electricity demand, water supply, and chemical leakage.
Kanungo ’23 – [Alokya, Ongoing Ph.D. in “Scientific Correlation between the Impact of Climate Change on Soil Health and Sustainable Agriculture” from K.I.I.T University, M.Phil in Geography, Former Research Associate. “The Green Dilemma: Can AI Fulfil Its Potential Without Harming the Environment?,” 7-18-2023, Earth, https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/, Accessed; 7-26-2024, AC]
Artificial Intelligence (AI) is hailed as a game-changer but beneath its transformative potential lies a pressing concern: its environmental impact. The development, maintenance, and disposal of AI technology all come with a large carbon footprint.
In this article, we dive deep into the hidden environmental dilemma surrounding AI and explore the urgent need for proactive measures. A sustainable future may be secured, and the risks AI brings to our world can be reduced by being aware of the effects and acting responsibly.
Behind the scenes of AI’s brilliance lies an energy-intensive process with a staggering carbon footprint.
As datasets and models become more complex, the energy needed to train and run AI models becomes enormous. This increase in energy use directly affects greenhouse gas emissions, aggravating climate change. According to OpenAI researchers, since 2012, the amount of computing power required to train cutting-edge AI models has doubled every 3.4 months. By 2040, it is expected that the emissions from the Information and Communications Technology (ICT) industry as a whole will reach 14% of the global emissions, with the majority of those emissions coming from the ICT infrastructure, particularly data centres and communication networks. These data demonstrate the urgent need to address AI’s carbon footprint and role in environmental deterioration.
Recently, a study was conducted by researchers at the University of Massachusetts to determine how much energy is used to train certain popular large AI models. According to the results, training can produce about 626,000 pounds of carbon dioxide, or the equivalent of around 300 round-trip flights between New York and San Francisco – nearly 5 times the lifetime emissions of the average car.
The e-waste produced by AI technology poses a serious environmental challenge. E-waste contains hazardous chemicals, including lead, mercury, and cadmium, that can contaminate soil and water supplies and endanger both human health and the environment.
By 2050, the World Economic Forum (WEF) projects that the total amount of e-waste generated will have surpassed 120 million metric tonnes. To avoid environmental harm and minimise the release of dangerous compounds, proper e-waste management and recycling are essential. To ensure the secure processing and recycling of AI-related electronic waste and significantly reduce its negative environmental effects, more stringent laws and ethical disposal practices are required.
AI applications like driverless automobiles and delivery drones pose a threat to animals and natural environments. Automation fuelled by AI may result in greater consumption as well as increasing waste in certain sectors, such as the e-commerce industry, which has normalised the rapid and frequent delivery of goods.
The rising use of 

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