TOWARDS OPERATIONALIZING WHITE MALE ACCOUNTABILITY IN ARTIFICIAL INTELLIGENCE DEVELOPMENT INTERROGATING IMPACTS OF AND SOLUTIONS TO OVERREPRESENTATION UTILIZING RELATIONAL ETHICS FRAMEWORKS

Numerical metrics demonstrate that white men are demographically overrepresented in Artificial Intelligence (AI) technology development, research, and media coverage. This overrepresentation creates immediate and downstream harms that corporations and technologists in industry and the academy alike must contend with to ensure the creation of AI technologies, AI development organizations, and AI research institutions that are ethical, fair, accountable, transparent, and beneficial to all people. After defining the problem of overrepresentation and exploring why this problem is vital to address, this paper will posit a two-pronged theoretical solution to be implemented: (1) increasing white male accountability in AI technology spaces and (2) moving away from an underlying utilitarian or deontological ethical foundation and towards a relational ethical foundation. Using that theoretical analysis the paper will then present a model for taking this two-pronged theoretical solution from theory into practice by providing specific recommendations for operationalizing the proposed framework at the levels of AI technology development.

ON FUZZY PARTIAL FRACTIONAL ORDER EQUATIONS UNDER FUZZIFIED CONDITIONS

This paper is devoted to investigating or computing the solution to one-dimensional partial fuzzy fractional order heat equation. In particular, one-dimensional fuzzy partial heat equation is hybridized into two equations by hybrid techniques along with the concept of parametric fuzzy number. For this investigation, a hybrid method of decomposition due to Adomian and Laplace transform is used. The considered techniques are presented for the computation of series of solutions of partial fractional order heat equation. The applied techniques have also provided the accuracy, simplicity and efficiency as compared to other existing methods. Finally, some illustrations are solved for the justification of our theoretical solution.

Type-WA*: Using Exploration in Bounded Suboptimal Planning

Previous work on satisficing planning using greedy best-first search (GBFS) has shown that non-greedy, randomized exploration can help escape uninformative heuristic regions and solve hard problems faster. Despite their success when used with GBFS, such exploration techniques cannot be directly applied to bounded suboptimal algorithms like Weighted A* (WA*) without losing the solution-quality guarantees. In this work, we present Type-WA*, a novel bounded suboptimal planning algorithm that augments WA* with type-based exploration while still satisfying WA*'s theoretical solution-quality guarantee. Our empirical analysis shows that Type-WA* significantly increases the number of solved problems, when used in conjunction with each of three popular heuristics. Our analysis also provides insight into the runtime vs. solution cost trade-off.

THEORETICAL SOLUTION OF PILING COMPACTION AND THE INFLUENCE OF PILE-SOIL-BOUNDARY CURVE HYPOTHESIS

Research is ongoing to find theoretical solution to three-dimensional piling compaction. Considering the spacial-axis-symmetric characteristics, the boundary surface of pile-soil interaction is expressed by polynomials of different orders. First, the curve family parameter is introduced to construct the displacement and integral function. Then, the solution of pile-soil interaction is derived by combining the constitutive relation model of Duncan-Chang and the variational theory. Results of engineering computing show that the theoretical solution converges to the classical CEM and the limit equilibrium theory well at the corresponding computing area. Moreover, the effects of polynomial of different orders on the calculation results are not obvious. The conclusion in this paper can be used for reference in the derivation and application for other interaction of structure and soil problems.