Advantage Counterplans - Michigan 7 2024 BDKLS

5G Innovation
1NC---5G Plank
increase the availability of licensed full-power mid-band spectrum;
create a pipeline of licensed full power spectrum;
provide incentives for 5G innovation.
The CP solves 5G innovation – the key I/L is predictability.
Elbert et al. ‘23 [Val, Enrique Duarte Melo, Chi Hung Chong, and Johnny Henderson; April 17; Val Elbert is a core member of the Technology, Media & Telecommunications (TMT) practice at The Boston Consulting Group and leads that practice’s global commercial topic in M&A strategy and post-merger integration; BCG, “Accelerating the 5G Economy in the US,” https://www.bcg.com/publications/2023/accelerating-the-5g-economy-in-the-us]//av
The future of the 5G economy in the US will depend to a large extent on decisions made by government. Policymakers can help this essential technology expand by focusing on spectrum availability and the innovation ecosystem. We propose five key initiatives:
Increase the availability of licensed full-power mid-band spectrum for mobile use. Doing so will enable the US 5G economy to better support innovative use cases and further scale existing successes. As noted earlier, mid-band spectrum is in short supply, and more of it is sorely needed. Adding capacity in this range will provide the greatest benefit for users in urban, suburban, and rural locales. With additional exclusive-use licensed spectrum, telecom companies can provide a consistent, reliable, and robust experience for consumers and businesses through 5G connectivity.
Create a pipeline of licensed full-power spectrum. To provide predictability in this pipeline, policymakers should aim for clear visibility with regard to the future spectrum bands to be auctioned, the auction dates, and the availability dates. Predictability is essential to wireless service providers as they undertake their capex planning. It enables informed participation in spectrum auctions, efficient spectrum planning, and effective sequencing of network improvements to deliver the best experience for end users. It also will position the US to lead international spectrum allocation discussions, facilitating the nation’s ability to build international scale for the wireless ecosystem. This was extremely important in the development of 4G. Finally, spectrum predictability will give innovators the security they need to advance the US domestic tech ecosystem, cementing the US’s economic and national security.
Provide incentives for 5G innovation. Give businesses and organizations reasons to build and operate robust 5G innovation hubs and testing facilities to develop broad use cases. The expanded footprint would benefit wireless companies in several ways. It would centralize solutions, collocating innovation hubs and testing facilities. It would cultivate supply-side innovation before demand overtook capacity. It would encourage the co-creation of 5G solutions across industries. And it would leverage the next wave of solutions to productize and monetize new offerings with subsequent partners.
Develop a 5G partnership role for governments. At the federal, state, and local levels, specifically appointed officials should promote 5G development, coordinate collaborative efforts, and mitigate roadblocks. This would encourage collaborations, particularly on complex projects like smart-city initiatives. Using 5G connectivity to revamp traffic control patterns, for example, could benefit from an in-depth partnership between government and businesses from several sectors.
Promote and support the deployment of the IoT. This technology represents one of the highest-leverage ways to advance the 5G economy. Like many large-scale innovations, the IoT may need extra support when introduced in a city, before it achieves economies of scale or has been integrated into local operations.
US competitiveness depends on the nation’s ability to provide leading wireless communications networks and innovative applications and uses that enhance consumers’ lives and improve business efficiencies. With the rollout of the 5G economy, the US is poised not just to catch up with other countries, but also to establish a body of activity that promotes innovation and growth throughout the coming decade, and in the years that follow.
AI Regulation
1NC – Regulate AI
mandate robust third-party auditing and certification of artificial intelligence.
Third-party auditing and certification mitigate risks from emergent capabilities.
Future of Life ’23 [Future of Life; Nonprofit organization which aims to steer transformative technology towards benefiting life and away from large-scale risks, with a focus on existential risk from advanced artificial intelligence; 04-12-2023; "Policymaking In The Pause"; Future of Life Institute; https://futureoflife.org/document/policymaking-in-the-pause/; Accessed 7-16-2024] KL
For some types of AI systems, the potential to impact the physical, mental, and financial wellbeing of individuals, communities, and society is obvious. For example, a credit scoring system could discriminate against certain ethnic groups. For other systems – in particular general-purpose AI systems6 – the applications and potential risks are often not immediately evident. General-purpose AI systems trained on massive datasets also have unexpected (and often unknown) emergent capabilities.7
In Europe, the draft AI Act already requires that, prior to deployment and upon any substantial modification, ‘high-risk’ AI systems undergo ‘conformity assessments’ in order to certify compliance with specified harmonized standards or other common specifications.8 In some cases, the Act requires such assessments to be carried out by independent third-parties to avoid conflicts of interest.
In contrast, the United States has thus far established only a general, voluntary framework for AI risk assessment.9 The National Institute of Standards and Technology (NIST) is developing so-called ‘profiles’ that will provide specific risk assessment and mitigation guidance for certain types of AI systems, but these profiles still allow organizations simply to ‘accept’ the risks that they create for society instead of addressing them. In other words, the United States does not require any third-party risk assessment or risk mitigation measures before a powerful AI system can be deployed at scale.
To ensure proper vetting of powerful AI systems before deployment, we recommend a robust independent auditing regime for models that are general-purpose, trained on large amounts of compute, or intended for use in circumstances likely to impact the rights or the wellbeing of individuals, communities, or society. This mandatory third-party auditing and certification scheme could be derived from the EU’s proposed ‘conformity assessments’ and should be adopted by jurisdictions worldwide10.
In particular, we recommend third-party auditing of such systems across a range of benchmarks for the assessment of risks11, including possible weaponization12 and unethical behaviors13 and mandatory certification by accredited third-party auditors before these high-risk systems can be deployed. Certification should only be granted if the developer of the system can demonstrate that appropriate measures have been taken to mitigate risk, and that any residual risks deemed tolerable are disclosed and are subject to established protocols for minimizing harm.
regulate access to computational power
expand know-your-customer requirements for AI companies and high-volume suppliers of compute components
Regulating access to computational power and expanding know-your-customer requirements forces compliance.
Future of Life ’23 [Future of Life; Nonprofit organization which aims to steer transformative technology towards benefiting life and away from large-scale risks, with a focus on existential risk from advanced artificial intelligence; 04-12-2023; "Policymaking In The Pause"; Future of Life Institute; https://futureoflife.org/document/policymaking-in-the-pause/; Accessed 7-16-2024] KL
At present, the most advanced AI systems are developed through training that requires an enormous amount of computational power – ‘compute’ for short. The amount of compute used to train a general-purpose system largely correlates with its capabilities, as well as the magnitude of its risks.
Today’s most advanced models, like OpenAI’s GPT-4 or Google’s PaLM, can only be trained with thousands of specialized chips running over a period of months. While chip innovation and better algorithms will reduce the resources required in the future, training the most powerful AI systems will likely remain prohibitively expensive to all but the best-resourced players.
In practical terms, compute is more easily monitored and governed than other AI inputs, such as talent, data, or algorithms. It can be measured relatively easily and the supply chain for advanced AI systems is highly centralized, which means governments can leverage such measures in order to limit the harms of large-scale models.14
To prevent reckless training of the highest risk models, we recommend that governments make access to large amounts of specialized computational power for AI conditional upon the completion of a comprehensive risk assessment. The risk assessment should include a detailed plan for minimizing risks to individuals, communities, and society, consider downstream risks in the value chain, and ensure that the AI labs conduct diligent know-your-customer checks.
Successful implementation of this recommendation will require governments to monitor the use of compute at data centers within their respective jurisdictions.14.5 The supply chains for AI chips and other key components for high-performance computing will also need to be regulated such that chip firmware can alert regulators to unauthorized large training runs of advanced AI systems.15
Through passage of the CHIPS and Science Act of 2022, the United States has instituted licensing requirements15.5 for export of many of these components in an effort to monitor and control their global distribution. However, licensing is only required when exporting to certain destinations, limiting the capacity to monitor aggregation of equipment for unauthorized large training runs within the United States and outside the scope export restrictions. Companies within the specified destinations have also successfully skirted monitoring by training AI systems using compute leased from cloud providers.16 We recommend expansion of know-your-customer requirements to all high-volume suppliers for high-performance computing components, as well as providers that permit access to large amounts of cloud compute.
establish capable AI agencies at the national level
AI regulatory agencies consolidate expertise and replace a patchwork-based approach.
Future of Life ’23 [Future of Life; Nonprofit organization which aims to steer transformative technology towards benefiting life and away from large-scale risks, with a focus on existential risk from advanced artificial intelligence; 04-12-2023; "Policymaking In The Pause"; Future of Life Institute; https://futureoflife.org/document/policymaking-in-the-pause/; Accessed 7-16-2024] KL
AI is developing at a breakneck pace and governments need to catch up. The establishment of AI regulatory agencies helps to consolidate expertise and reduces the risk of a patchwork approach.
The UK has already established an Office for Artificial Intelligence and the EU is currently legislating for an AI Board. Similarly, in the US, Representative Ted Lieu has announced legislation to create a non-partisan AI Commission with the aim of establishing a regulatory agency. These efforts need to be sped up and taken up around the world.
We recommend that national AI agencies be established in line with a blueprint developed by Anton Korinek at Brookings. Korinek proposes that an AI agency have the power to:
Monitor public developments in AI progress and define a threshold for which types of advanced AI systems fall under the regulatory oversight of the agency (e.g. systems above a certain level of compute or that affect a particularly large group of people).
Mandate impact assessments of AI systems on various stakeholders, define reporting requirements for advanced AI companies and audit the impact on people’s rights, wellbeing, and society at large. For example, in systems used for biomedical research, auditors would be asked to evaluate the potential for these systems to create new pathogens.
Establish enforcement authority to act upon risks identified in impact assessments and to prevent abuse of AI systems.
Publish generalized lessons from the impact assessments such that consumers, workers and other AI developers know what problems to look out for. This transparency will also allow academics to study trends and propose solutions to common problems.
Beyond this blueprint, we also recommend that national agencies around the world mandate record-keeping of AI safety incidents, such as when a facial recognition system causes the arrest of an innocent person. Examples include the non-profit AI Incident Database and the forthcoming EU AI Database created under the European AI Act.
establish liability for AI-caused harms
A joint and several liability framework for AI holds actors accountable for harm.
Future of Life ’23 [Future of Life; Nonprofit organization which aims to steer transformative technology towards benefiting life and away from large-scale risks, with a focus on existential risk from advanced artificial intelligence; 04-12-2023; "Policymaking In The Pause"; Future of Life Institute; https://futureoflife.org/document/policymaking-in-the-pause/; Accessed 7-16-2024] KL
AI systems present a unique challenge in assigning liability. In contrast to typical commercial products or traditional software, AI systems can perform in ways that are not well understood by their developers, can learn and adapt after they are sold and are likely to be applied in unforeseen contexts. The ability for AI systems to interact with and learn from other AI systems is expected to expedite the emergence of unanticipated behaviors and capabilities, especially as the AI ecosystem becomes more expansive and interconnected.
Several plug-ins have already been developed that allow AI systems like ChatGPT to perform tasks through other online services (e.g. ordering food delivery, booking travel, making reservations), broadening the range of potential real-world harms that can result from their use and further complicating the assignment of liability.19 OpenAI’s GPT-4 system card references an instance of the system explicitly deceiving a human into bypassing a CAPTCHA bot-detection system using TaskRabbit, a service for soliciting freelance labor.20
When such systems make consequential decisions or perform tasks that cause harm, assigning responsibility for that harm is a complex legal challenge. Is the harmful decision the fault of the AI developer, deployer, owner, end-user, or the AI system itself?
Key among measures to better incentivize responsible AI development is a coherent liability framework that allows those who develop and deploy these systems to be held responsible for resulting harms. Such a proposal should impose a financial cost for failing to exercise necessary diligence in identifying and mitigating risks, shifting profit incentives away from reckless empowerment of poorly-understood systems toward emphasizing the safety and wellbeing of individuals, communities, and society as a whole.
To provide the necessary financial incentives for profit-driven AI developers to exercise abundant caution, we recommend the urgent adoption of a framework for liability for AI-derived harms. At a minimum, this framework should hold developers of general-purpose AI systems and AI systems likely to be deployed for critical functions21 strictly liable for resulting harms to individuals, property, communities, and society. It should also allow for joint and several liability for developers and downstream deployers when deployment of an AI system that was explicitly or implicitly authorized by the developer results in harm.
introduce measures to prevent and track AI model leaks
Watermarking models prevent malicious actors from obtaining powerful models.
Future of Life ’23 [Future of Life; Nonprofit organization which aims to steer transformative technology towards benefiting life and away from large-scale risks, with a focus on existential risk from advanced artificial intelligence; 04-12-2023; "Policymaking In The Pause"; Future of Life Institute; https://futureoflife.org/document/policymaking-in-the-pause/; Accessed 7-17-2024] KL
Commercial actors may not have sufficient incentives to protect their models, and their cyberdefense measures can often be insufficient. In early March 2023, Meta demonstrated that this is not a theoretical concern, when their model known as LLaMa was leaked to the internet.22 As of the date of this publication, Meta has been unable to determine who leaked the model. This lab leak allowed anyone to copy the model and represented the first time that a major tech firm’s restricted-access large language model was released to the public.
Watermarking of AI models provides effective protection against stealing, illegitimate redistribution and unauthorized application, because this practice enables legal action against identifiable leakers. Many digital media are already protected by watermarking – for example through the embedding of company logos in images or videos. A similar process23 can be applied to advanced AI models, either by inserting information directly into the model parameters or by training it on specific trigger data.
We recommend that governments mandate watermarking for AI models, which will make it easier for AI developers to take action against illegitimate distribution.
expand technical AI safety research funding
Future of Life ’23 [Future of Life; Nonprofit organization which aims to steer transformative technology towards benefiting life and away from large-scale risks, with a focus on existential risk from advanced artificial intelligence; 04-12-2023; "Policymaking In The Pause"; Future of Life Institute; https://futureoflife.org/document/policymaking-in-the-pause/; Accessed 7-17-2024] KL
The private sector under-invests in research that ensures that AI systems are safe and secure. Despite nearly USD 100 billion of private investment in AI in 2022 alone, it is estimated that only about 100 full-time researchers worldwide are specifically working to ensure AI is safe and properly aligned with human values and intentions.24
In recent months, companies developing the most powerful AI systems have either downsized or entirely abolished their respective ‘responsible AI’ teams.25 While this partly reflects a broader trend of mass layoffs across the technology sector, it nonetheless reveals the relative de-prioritization of safety and ethics considerations in the race to put new systems on the market.
Governments have also invested in AI safety and ethics research, but these investments have primarily focused on narrow applications rather than on the impact of more general AI systems like those that have recently been released by the private sector. The US National Science Foundation (NSF), for example, has established ‘AI Research Institutes’ across a broad range of disciplines. However, none of these institutes are specifically working on the large-scale, societal, or aggregate risks presented by powerful AI systems.
To ensure that our capacity to control AI systems keeps pace with the growing risk that they pose, we recommend a significant increase in public funding for technical AI safety research in the following research domains:
Alignment: development of technical mechanisms for ensuring AI systems learn and perform in accordance with intended expectations, intentions, and values.
Robustness and assur

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