Deepfakes, visual content created or modified by AI algorithms are increasingly being used to spread misinformation. Consequently, social media platforms are now labelling AI-content. To implement deepfake detectors in a way that permits user acceptance and trust in the decision, it is important to provide explanations of the model decision that align with user preferences. In order to better understand these user preferences, we will compare user preferences along the dimensions of acceptance and trust between (C1) just the output label, (C2) model output + feature importance mask over regions of the image that are salient to model output, (C3) model output + natural language explanation of image decision, and (C4) model output + mask + natural language explanation. Preferences will be measured using Likert scale and statistical significance performed via Friedman test. We find that the presence of a natural language component (C3 and C4) produces clearer explanations, and increases user trust and acceptance over the baseline (C1) and mask (C2).
To deter SNS users from posting or reposting misinformation/disinformation, we develop a method to quantify each user’s degree of responsibility when misinformation/disinformation spreads. We utilize the Banzhaf index, a technique for quantitatively evaluating player contributions in participatory games, for this purpose. Although the computation of the Banzhaf index has exponential time complexity with respect to the number of users, we developed an algorithm computable in realistic time by limiting it to tree structures modeling information propagation in social networks. We also discuss challenges for practical implementation of the proposed method and possibilities for extending the current work.
Governance in data ecosystems relies on data policies to control how assets are accessed and used. However, automatically detecting conflicts and relationships between these policies is challenging, as they often rely on implicit knowledge that is not explicitly available. In this work, we propose a Neuro-Symbolic pipeline that discovers these relationships. Our approach uses Large Language Models (LLMs) to systematically generate logical rules that approximate these implicit semantic relationships into explicit logical rules executable by a deterministic reasoning engine. We include a proof-of-concept implementation demonstrating how this approach automatically discovers hidden inter-policy conflicts based on a set of real data policies, ensuring a systematic and auditable governance process.
Private set operations are fundamental building blocks for privacy-preserving data analytics and services. In outsourced computation settings, trusted execution environments (TEEs) enable encrypted data processing, but they remain vulnerable to side channel leakages through data-dependent memory access patterns and program control flow. Doubly oblivious algorithms mitigate against this threat by ensuring both data-independent control flow and memory access behavior. In this work, we investigate the application of a doubly oblivious radix-partitioning technique, originally proposed for oblivious join operations, to the problems of Private Set Intersection (PSI) and Private Set Union (PSU). Particularly, for cases where the operations are between a client’s set and a server’s set, where the server’s set is much bigger than the client’s, we explore the possibility of processing multiple client requests at once to amortize the cost of the expensive operations. We propose algorithms for PSI and PSU using the doubly oblivious radix-partitioning and extend them to handle multi-set operations, which will enable processing multiple requests at once.
Schema matching is a core task in data integration that aims to identify semantic correspondences between elements of different schemas. Recent work shows that Large Language Models (LLMs) achieve promising results even when only the names and descriptions of schema elements are available. An additional advantage of LLMs is that they can provide natural-language explanations for their predictions. However, the informal nature of these explanations prevents automatic verification, requiring their correctness to be assessed by domain experts. In this paper, we conduct an initial investigation into grounding explanations for LLM-generated schema matchings as formal explanation expressions in a logical framework. These explanatory expressions consist of similarity predicates that capture both syntactic and semantic relationships among metadata elements. Predicted correspondences are hence compiled into machine-checkable explanation expressions that serve both as a basis for automatic verification and as concise, standardized artefacts that enable humans to inspect and understand matching decisions. We empirically explore the effectiveness of our approach on two healthcare benchmarks, providing initial evidence of its potential.
