Assistive AI for Augmenting Human Decision-making

TL;DR

The proposed framework not only extends regulatory landscapes but also highlights the synergy between AI technology and human judgement, underscoring the potential of AI to serve as a vital instrument in discerning reality from fiction and thus enhancing the decision-making process.

Abstract

Regulatory frameworks for the use of AI are emerging. However, they trail behind the fast-evolving malicious AI technologies that can quickly cause lasting societal damage. In response, we introduce a pioneering Assistive AI framework designed to enhance human decision-making capabilities. This framework aims to establish a trust network across various fields, especially within legal contexts, serving as a proactive complement to ongoing regulatory efforts. Central to our framework are the principles of privacy, accountability, and credibility. In our methodology, the foundation of reliability of information and information sources is built upon the ability to uphold accountability, enhance security, and protect privacy. This approach supports, filters, and potentially guides communication, thereby empowering individuals and communities to make well-informed decisions based on cutting-edge advancements in AI. Our framework uses the concept of Boards as proxies to collectively ensure that AI-assisted decisions are reliable, accountable, and in alignment with societal values and legal standards. Through a detailed exploration of our framework, including its main components, operations, and sample use cases, the paper shows how AI can assist in the complex process of decision-making while maintaining human oversight. The proposed framework not only extends regulatory landscapes but also highlights the synergy between AI technology and human judgement, underscoring the potential of AI to serve as a vital instrument in discerning reality from fiction and thus enhancing the decision-making process. Furthermore, we provide domain-specific use cases to highlight the applicability of our framework.

Figures (6)

• Figure 1: Certified AI systems (left) are controlled/validated by a central control unit. Domain and Community-dependent Assistive AI when used with human decisions only (middle) controls for the AI output. The overseeing unit community is controlled here. Community control units can certify AI systems as well (right) this way automated decision-making is possible, while the system still facilitates human overrides if needed.
• Figure 2: The Board can be connected to consultants and auditors. Each unit, e.g., a consulting company may have a Board and may have associated external consultants and auditors, too.
• Figure 3: Boards typically assume dual functions. First (left), they decide on policy changes, especially if the environment changes due to AI developments. These changes affect everyone in the community, including other boards. Second (right), they perform process control, i.e., review and control policy changes of the boards in their community, ensuring these changes follow the community's values and legal standards. The Board can change, add to, or clarify the policy changes of the Boards of its community. In all cases, Assistive Artificial Intelligence can support the decision-making procedure.
• Figure 4: Flowchart for handling communications. The Assistive Artificial Intelligence system can suggest actions related to communications to the Board, to moderators designated by the Board and to end users. The final decisions can be made by human actors or, in certain cases, automatically. The choice of behaviour depends on the Board's policy.
• Figure 5: Flowchart for updating the Board knowledge base and policy based on Assistive Artificial Intelligence recommendations. Box colours in this figure indicate component levels, legal (grey), board (blue), and decision level (yellow).