AI Regulation in the Public Sector: Regulating Governments Use of AI

This means that data can be transferred between different cloud environments without having to go through the public internet, which is vulnerable to bad actors. A secure cloud fabric allows for agencies to connect to other federal agencies that can connect to other state agencies, as well as connecting to various commercial partners easily and effectively within the system. One major challenge is ensuring that the data is transmitted securely between different government agencies and stakeholders. With so many parties involved in managing and analyzing government data, it’s essential to have a secure, private connection that can ensure the confidentiality and integrity of the data as it’s shared. However, establishing and maintaining such connections can be a complex and costly process, especially as the volume of data being transmitted continues to grow. To prepare the data stored in these lakes for analysis and use, data scientists and analysts need protected access.

Government intervention requires a high bar of evidence, so understanding the scientific basis is critical — as it is with the physics underlying uranium regulation. What’s more, this cut-off level is prone to rapid obsolescence due to computing’s fast pace. The AI Safety Summit in the UK and the Executive Order on Safe, Secure and Trustworthy Artificial Intelligence in the US signal an intensification of government intervention in artificial intelligence. Both events demonstrate a growing commitment to address concerns about AI risks that have entered the public consciousness. The term artificial intelligence, or AI, dates back to the 1950s and centered on the idea that the human brain could be mechanized. Over the decades since, scientists have studied how humans think and learn and tried to apply those same methods to machines and data.

Advancing Government Services With Responsible Generative AI

In addition to these national regulations, there are also international agreements geared at supporting data privacy and security. A ready-on-mind example is the Council of Europe’s Convention 108+ which offers a framework to protect personal data across borders. As part of this work, the Secretary of State and the https://www.metadialog.com/governments/ Administrator of the United States Agency for International Development shall draw on lessons learned from programmatic uses of AI in global development. A new set of AI security guidelines has been published by a consortium of international governments, urging AI companies to follow a “secure by default” approach.

In the coming year, a new generation of generally capable models similar to GPT-4—trained using record amounts of computational power—will likely hit the market. Because it is currently believed that the widely-used AI algorithms are vulnerable to attack, companies will of course not be able to exhaustively protect against AI attacks, just as they are not expected to exhaustively protect against traditional cyberattacks. In the private sector, regulators should make compliance mandatory for high-risk uses of AI where attacks would have severe societal and public safety consequences. This report has identified examples of private sector high-risk uses of AI, including content filters and self-driving vehicles. For example, in the context of the relatively unregulated space of social networks, there is a call from both legislators and industry itself for additional regulation.

Capabilities

This option has a significantly lower initial investment and ongoing expenses compared to building an in-house solution. Moreover, purchasing a ready-made solution allows for quicker implementation and requires less expertise. By implementing generative AI for your content, you can achieve considerable cost savings, increase productivity, improve customer engagement, and gain a competitive advantage.

Discovering poisoned data in order to stop poisoning attacks can be very difficult due to the scale of the datasets. These samples many times come from public sources rather than private collection efforts. Even in the case when the dataset is collected privately and verified, an attacker may hack into the system where the data is being stored and introduce poisoned samples, or seek to corrupt otherwise valid samples. Even in cases where the attacker does not have the model, it is still possible to mount an input attack. If attackers have access to the dataset used to train the model, they can use it to build their own copy of the model, and use this “copy model” to craft their attack.

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Further, by not pooling dataset resources, a dataset breach will have limited consequences. As these AI-based law enforcement systems become more widespread, they will naturally become attack targets for criminals. One could imagine AI attacks on facial recognition systems as the 21st century version of the time-honored strategy of cutting or dyeing one’s hair to avoid law enforcement recognition. For example, attack patterns can be added in imperceivable ways to a physical object itself. Researchers have shown that a 3D-printed turtle with an imperceivable input attack pattern could fool AI-based object detectors.15  While turtle detection may not have life and death consequences (yet…), the same strategy applied to a 3D-printed gun may. In the audio domain, high pitch sounds that are imperceivable to human ears but able to be picked up by microphones can be used to attack audio-based AI systems, such as digital assistants.

Engaging with elected officials through letters or participation in public forums demonstrates support for stricter regulations regarding privacy rights. In summary, the responsibility falls on both governments and individual citizens alike when it comes to ensuring data privacy and security in a government powered by AI. By taking a proactive approach towards protecting personal information online while also advocating for stronger policies at a larger scale, citizens play a significant role in the creation of a safer digital environment for all. International cooperation develops trust among nations by showing the necessary commitment to safeguarding individuals’ rights while harnessing the potential benefits of AI technology. It enables governments worldwide to work towards a collective goal of ensuring that data privacy and security remain paramount in an increasingly interconnected world driven by artificial intelligence.

In addition to a technical focus on securing models, research attention should also focus on creating testing frameworks that can be shared with industry, government, and military AI system operators. In a similar manner to how automobiles are tested for safety, testing frameworks for the security of models can be established and used as a core component alongside the traditional testing methods used for vehicles, drones, weapon systems, and other systems that will adopt AI. In the simplest scenarios where a central repository holds the datasets and other important assets, the vanilla intrusion detection methods that are currently a mainstay of cybersecurity can be applied. In this simple case, if assets such as datasets or models are accessed by an unauthorized party, this should be noted immediately and the proper steps should be taken in response. To name a few common cases, data points may be mislabeled, corrupted, or inherently flawed. They may arise through completely natural processes such as human error and sensor failure.

As a result, it is imperative that policymakers recognize the problem, identify vulnerable systems, and take steps to mitigate risk before people get hurt. This report has identified five critical areas that are already vulnerable to these attacks, and growing more https://www.metadialog.com/governments/ so with each day. The content filters that will serve as the first line of defense against extremist recruiting, misinformation and disinformation campaigns, and the spread of hate and encouragement of genocide can be rendered ineffective with AI attacks.

Governments need to be sensitive to the risks of overregulation and stymieing innovation, on the one hand, and the risks of moving too slowly (relative to the pace of AI progress), on the other. This kind of multilayered approach (regulating the development, deployment, and use of AI technologies) is how we deal with most safety-critical technologies. In aviation, the Federal Aviation Administration gives its approval before a new airplane is put in the sky, while there are also rules for who can fly the planes, how they should be maintained, how the passengers should behave, and where planes can land. The council will develop recommendations for its utilization of artificial intelligence throughout state government, while honoring transparency, privacy and equity. Those recommendations should be ready by no later than six months from the date of its first convening.

(j)  The term “differential-privacy guarantee” means protections that allow information about a group to be shared while provably limiting the improper access, use, or disclosure of personal information about particular entities. In the end, AI reflects the principles of the people who build it, the people who use it, and the data upon which it is built. I firmly believe that the power of our ideals; the foundations of our society; and the creativity, diversity, and decency of our people are the reasons that America thrived in past eras of rapid change. We are more than capable of harnessing AI for justice, security, and opportunity for all.