AI Regulation: Are Governments Up to the Task?

Secure and Compliant AI for Governments

Continuing the stop sign example, if the dataset contains images of stop signs in the sun and shade, from straight ahead and from different angles, during the day and at night, it will learn all the possible ways a stop sign can appear in nature. Another—known as a poisoning attack—can stop an AI system from operating correctly in situations, or even insert a backdoor that can later be exploited by an adversary. Continuing the analogy, poisoning attacks would be the equivalent of hypnotizing the German analysts to close their eyes anytime they were about to see any valuable information that could be used to hurt the Allies. First, it begins by giving an accessible yet comprehensive description of how current AI systems can be attacked, the forms of these attacks, and a taxonomy for categorizing them. Renewables are widely perceived as an opportunity to shatter the hegemony of fossil fuel-rich states and democratize the energy landscape. Virtually all countries have access to some renewable energy resources (especially solar and wind power) and could thus substitute foreign supply with local resources.

Secure and Compliant AI for Governments

Autonomous weapon systems, even those that do not utilize AI, already carry great stigma due to a fear that attack or algorithmic mistakes will cause unacceptable collateral damage, and therefore present unacceptable levels of risk. More specifically, different segments of the public sector can implement versions of compliance that meet their needs on a segment-by-segment basis. For the military, the JAIC is a natural candidate for administrating this compliance program. As it is specifically designed as a centralized control mechanism over all significant military AI applications, it can use this centralized position to effectively administer the program. For law enforcement, the DOJ can use its relationship with law enforcement organizations, including the FBI and local law enforcement offices, as a basis for administrating a compliance program. Where necessary, DOJ can tie compliance as a pre-condition for receiving funding through grants.

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Without AI systems, human beings are in charge of these transactions, which means the process takes a long time and is susceptible to human error. It increases security by decreasing the chance of humans leaking confidential information thereby increasing compliance by ensuring high standards of privacy and quality. Across industries, we are seeing organizations move further on their digital transformation journeys. For example, in financial services, the payments ecosystem is an inflection point for transformation. We believe now is the time for change and IBM continues to work with its partner community to drive transformation. Temenos Payments Hub recently became the first dedicated payments solution to deliver innovative payments capabilities on the IBM Cloud for Financial Services, now the latest initiative in our long history together helping clients transform.

Secure and Compliant AI for Governments

As a result, traditional cybersecurity policies and defense can be applied to protect against some AI attacks. While AI attacks can certainly be crafted without accompanying cyberattacks, strong traditional cyber defenses will increase the difficulty of crafting certain attacks. The US government generates and collects a massive amount of data each year – everything from census information to intelligence gathering.

Manage risk, improve compliance, build trust and deliver better services.

EMMA guides around one million applicants per month regarding the various services offered by the department and directs them to relevant pages and resources. AI-based cognitive automation, such as rule-based systems, speech recognition, machine translation, and computer vision, can potentially automate government tasks at unprecedented speed, scale, and volume. A Governing magazine report found that 53% of local government officials cannot complete their work on time due to low operational efficiencies like manual paperwork, data collection, and reporting. As a result, their task backlogs keep piling up, causing further delays in government workflows. In the UK, National Health Service (NHS) formed an initiative to collect data related to COVID patients to develop a better understanding of the virus.

How can AI be secure?

Sophisticated AI cybersecurity tools have the capability to compute and analyze large sets of data allowing them to develop activity patterns that indicate potential malicious behavior. In this sense, AI emulates the threat-detection aptitude of its human counterparts.

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. A final recommended action plan should be ready no later than 12 months from its first convening. Because AI systems have already been deployed in critical areas, stakeholders and appropriate regulatory agencies should also retroactively apply these suitability tests to already deployed systems.

Our research shows, however, that the role countries are likely to assume in decarbonized energy systems will be based not only on their resource endowment but also on their policy choices. Government to identify, assess, test and implement technologies against the problems of foreign propaganda and disinformation, in cooperation with foreign partners, private industry and academia. Additionally, conversational AI offers to revolutionize the operations and missions of all public sector agencies. Conversational AI is a type of artificial intelligence intended to facilitate smooth voice or text communication between people and computers.

SAIF ensures that ML-powered applications are developed in a responsible manner, taking into account the evolving threat landscape and user expectations. We’re excited to share the first steps in our journey to build a SAIF ecosystem across governments, businesses and organizations to advance a framework for secure AI deployment that works for all. The guidelines shall, at a minimum, describe the significant factors that bear on differential-privacy safeguards and common risks to realizing differential privacy in practice.

Why AI governance is crucial

The report shall include a discussion of issues that may hinder the effective use of AI in research and practices needed to ensure that AI is used responsibly for research. The Assistant to the President for National Security Affairs and the Director of OSTP shall coordinate the process of reviewing such funding requirements to facilitate consistency in implementation of the framework across funding agencies. (ii)   Within 150 days of the date of this order, the Secretary of the Treasury shall issue a public report on best practices for financial institutions to manage AI-specific cybersecurity risks. (t)  The term “machine learning” means a set of techniques that can be used to train AI algorithms to improve performance at a task based on data. Additionally, the IBM Cloud Security and Compliance Center is designed to deliver enhanced cloud security posture management (CSPM), workload protection (CWPP), and infrastructure entitlement management (CIEM) to help protect hybrid, multicloud environments and workloads. The workload protection capabilities aim to prioritize vulnerability management to support quick identification and remediation of critical vulnerabilities.

  • The same goes for adoption of automated decision-making tools at the state and local levels.
  • At AWS, we’re excited about generative AI’s potential to transform public sector organizations of all sizes.
  • Second, the proliferation of powerful yet cheap computing hardware means almost everyone has the power to run these algorithms on their laptops or gaming computers.
  • However, Microsoft has designed a new architecture that enables government agencies to access these language models from Azure Government securely.
  • Different industries will likely play into one of these scenarios, if not a hybrid of both.

Because the users’ data never leaves their devices, their privacy is protected and their fears that companies may misuse their data once collected are allayed. Federated learning is being looked to as a potentially groundbreaking solution to complex public policy problems surrounding user privacy and data, as it allows companies to still analyze and utilize user data without ever needing to collect that data. Public policy creating “AI Security Compliance” programs will reduce the risk of attacks on AI systems and lower the impact of successful attacks. Compliance programs would accomplish this by encouraging stakeholders to adopt a set of best practices in securing systems against AI attacks, including considering attack risks and surfaces when deploying AI systems, adopting IT-reforms to make attacks difficult to execute, and creating attack response plans. This program is modeled on existing compliance programs in other industries, such as PCI compliance for securing payment transactions, and would be implemented by appropriate regulatory bodies for their relevant constituents. Biden’s executive order introduces new reporting requirements for organizations that develop (or demonstrate an intent to develop) foundational models.

That comes with the ability to create a storage infrastructure–or even create their own private cloud – that can be used going forward like a private cloud for each agency. The circuit itself can be created in less than eight hours, which allows for substantial changes to the system essentially by the end of a business day. Once established, the secure cloud fabric becomes the support infrastructure for cloud migration and cloud portability. “Agencies can have the ability to move workloads between clouds easily, as well as having the ability to manage their Docker or Kubernetes environment in a simple structured environment.

Secure and Compliant AI for Governments

If health research industries train a model on data that’s biased – for instance, does not include any data from Native American populations – then it’s not going to produce equitable results. Department of Energy has developed an AI tool called Transportation State Estimation Capability (TranSEC). It uses machine learning to analyze traffic flow, even from incomplete or sparse traffic data, to deliver real-time street-level estimations of vehicle movements. A highly regulated approach to AI development, like in the European model, could help to keep people safe, but it could also hinder innovation in countries that accept the new standard, something EU officials have said they want in place by the end of the year. That is why many industry leaders are urging Congress to adopt a lighter touch when it comes to AI regulations in the United States.

Read more about Secure and Compliant AI for Governments here.

How would you define the safe secure and reliable AI?

Safe and secure

To be trustworthy, AI must be protected from cybersecurity risks that might lead to physical and/or digital harm. Although safety and security are clearly important for all computer systems, they are especially crucial for AI due to AI's large and increasing role and impact on real-world activities.

What are the trustworthy AI regulations?

The new AI regulation emphasizes a relevant aspect for building trustworthy AI models with reliable outcomes: Data and Data Governance. This provision defines the elements and characteristics to be considered for achieving high-quality data when creating your training and testing sets.

What is AI in governance?

AI governance is the ability to direct, manage and monitor the AI activities of an organization. This practice includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.