organizations are pairing humans with AI systems to improve decision-making and reducing costs. Proponents of human-centered AI argue that team performance can even further improve when the AI model explains its recommendations. However, a careful analysis of existing literature reveals that prior studies observed improvements due to explanations only when the AI, alone, outperformed both the human and the best human-AI team. This raises an important question: can explanations lead to complementary performance, i.e., with accuracy higher than both the human and the AI working alone?We address this question by devising comprehensive studies on human-AI teaming, where participants solve a task with help from an AI system without explanations and from one with varying types of AI explanation support. We carefully controlled to ensure comparable human and AI accuracy across experiments on three NLP datasets (two for sentiment analysis and one for question answering). While we found complementary improvements from AI augmentation, they were not increased by state-of-the-art explanations compared to simpler strategies, such as displaying the AI's confidence. We show that explanations increase the chance that humans will accept the AI's recommendation regardless of whether the AI is correct. While this clarifies the gains in team performance from explanations in prior work, it poses new challenges for human-centered AI: how can we best design systems to produce complementary performance? Can we develop explanatory approaches that help humans decide whether and when to trust AI input? CCS Concepts: • Human-centered computing → Empirical studies in HCI; Interactive systems and tools;• Computing methodologies → Machine learning.

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Excellent compatibility characteristics of CY-230 epoxy resin with a large variety of fibers and elemental particulates has lead us to develop the cost effective materials with desired characteristics. In the current investigation the livestock waste i.e. Chicken Feather Fiber (CFF) and extracted Fish residue is used as reinforcing materials. Here, the physical appearance, weight density, thickness swelling and water absorption characteristics of the chicken feather fiber filled epoxy based composite is scrutinized. The best CFF-epoxy composition is diagnosed which was 5 wt% CFF in epoxy resin and hardener filled composite. Results obtained correlates that the maximum water absorption at 4 wt% of CFF (i.e. 1.49%) was due to the maximum volume fraction (3.62%) at the 4 wt% CFF based composition. The improvement was characterized at 5 wt% CFF composition. Later the hybrid composition was fabricated with varying percentage of Extracted residue powder (ERP) from Rohu fish waste. Similarly all other results were characterizes and interpreted. The optimum hybrid composition having most compatible CFF and ERP weight percentage in Epoxy based hybrid composite was thus concluded.

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Large-scale generative models enabled the development of AI-powered code completion tools to assist programmers in writing code. However, much like other AI-powered tools, AI-powered code completions are not always accurate, potentially introducing bugs or even security vulnerabilities into code if not properly detected and corrected by a human programmer. One technique that has been proposed and implemented to help programmers identify potential errors is to highlight uncertain tokens. However, there have been no empirical studies exploring the effectiveness of this technique-nor investigating the different and not-yet-agreed-upon notions of uncertainty in the context of generative models. We explore the question of whether conveying information about uncertainty enables programmers to more quickly and accurately produce code when collaborating with an AI-powered code completion tool, and if so, what measure of uncertainty best fits programmers' needs. Through a mixed-methods study with 30 programmers, we compare three conditions: providing the AI system's code completion alone, highlighting tokens with the lowest likelihood of being generated by the underlying generative model, and highlighting tokens with the highest predicted likelihood of being edited by a programmer. We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits, and is subjectively preferred by study participants. In contrast, highlighting tokens according to their probability of being generated does not provide any benefit over the baseline with no highlighting. We further explore the design space of how to convey uncertainty in AI-powered code completion tools, and find that programmers prefer highlights that are granular, informative, interpretable, and not overwhelming. This work contributes to building an understanding of what uncertainty means for generative models and how to convey it effectively.

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