Salesforce Announces Public Sector Compliance Certifications, AI and Automation Tools to Accelerate Mission Impact

We’ve already addressed situations that require CJIS, Fedramp, SOC2 and PII compliance. In addition, current methods to contain these risks are difficult and expensive to implement and can actually make the problems less predictable – or worse. The subject of mitigating privacy and surveillance issues is complex and politically-sensitive, so I don’t want to spend a lot of time addressing it here. To make things worse, the most useful AI models like GPT4 and Bard are built on extremely large and complex neural network architectures that are practically impossible to decipher. As a result, AI can perpetuate and amplify existing biases and discrimination, especially in areas such as criminal justice, housing, and employment.

Once theory of mind AI becomes a reality, data scientists believe that self-aware AI is the next step. This type of AI would be aware of not only the mental states of others, like theory of mind, but it would also be aware of itself. Domino Cloud–our private and dedicated SaaS–takes the manual work out of compliance by continuously monitoring all of your models and data, so all code, datasets, models, environments and results (and versions) are centrally discoverable and traceable for audits. The role of citizens in protecting their data is important in a government driven by AI. With the increasing reliance on technology and the vast amount of data being collected, individuals must remain proactive in protecting their privacy and security. This includes being cautious about providing sensitive details such as bank verification numbers, or social security numbers, or financial information unless necessary.

How are SAIF and Responsible AI related?

(g)  Within 30 days of the date of this order, to increase agency investment in AI, the Technology Modernization Board shall consider, as it deems appropriate and consistent with applicable law, prioritizing funding for AI projects for the Technology Modernization Fund for a period of at least 1 year. Agencies are encouraged to submit to the Technology Modernization Fund project funding proposals that include AI — and particularly generative AI — in service of mission delivery. (a)  Within 365 days of the date of this order, to prevent unlawful discrimination from AI used for hiring, the Secretary of Labor shall publish guidance for Federal contractors regarding nondiscrimination in hiring involving AI and other technology-based hiring systems. (C)  implications for workers of employers’ AI-related collection and use of data about them, including transparency, engagement, management, and activity protected under worker-protection laws. (ii)  any computing cluster that has a set of machines physically co-located in a single datacenter, transitively connected by data center networking of over 100 Gbit/s, and having a theoretical maximum computing capacity of 1020 integer or floating-point operations per second for training AI.

For example, Georgia Government Transparency and Campaign Finance Commission successfully digitized campaign finance disclosure forms via OCR. We will also discuss some challenges and setbacks critical to deploying AI in government. Unlike the more high-level guidance detailed in the AI Bill of Rights, the Artificial Intelligence Act more carefully defines what kinds of AI activities should be allowed, which ones will be highly regulated, and what will be fully restricted based on having an unacceptable amount of risk. For example, activities that would be illegal under the AI Act include having AI negatively manipulate children, such as an AI-powered toy that encourages bad behavior.

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Meeting this goal requires robust, reliable, repeatable, and standardized evaluations of AI systems, as well as policies, institutions, and, as appropriate, other mechanisms to test, understand, and mitigate risks from these systems before they are put to use. It also requires addressing AI systems’ most pressing security risks — including with respect to biotechnology, cybersecurity, critical infrastructure, Secure and Compliant AI for Governments and other national security dangers — while navigating AI’s opacity and complexity. Testing and evaluations, including post-deployment performance monitoring, will help ensure that AI systems function as intended, are resilient against misuse or dangerous modifications, are ethically developed and operated in a secure manner, and are compliant with applicable Federal laws and policies.

For example, because many AI systems have web-based APIs, apps could easily be developed to interface directly with the APIs to generate attacks on demand. To attack an image content filter with a web-based API, attackers would simply supply an image to the app, which would then generate a version of the image able to trick the content filter but remain indistinguishable from the original to the human eye. Pushback to serious consideration of this attack threat will center around the technological prowess of Secure and Compliant AI for Governments attackers. As this attack method relies on sophisticated AI techniques, many may take false comfort in the fact that the attack method’s technical barriers will provide a natural barrier against attack. As a result, some may say that AI attacks do not deserve equal consideration with their traditional cybersecurity attack counterparts. Second, the military’s unique domain necessitates the creation of similarly unique datasets and tools, both of which are likely to be shared within the military at-large.