LLMs increasingly serve as general-purpose AI assistants in daily life, and their subtly unethical suggestions become a serious and real concern. It is demanding to test and mitigate such unethical suggestions from LLMs. Despite existing efforts to detect violations of “testable” facets of ethics (e.g., fairness testing), it is challenging to encode the full scope of ethics (e.g., justice, deontology) into a test oracle without human annotations or intervention. In this article, we take inspiration from reflective equilibrium, a modern moral reasoning method in moral and political philosophy, to guide our approach. Instead of seeking unethical suggestions in LLMs, we aim to identify behavioral inconsistency in LLMs’ ethics-related suggestions. These inconsistencies are anticipated to serve as a useful proxy and hint at unethical suggestions. We formulate reflective equilibrium in the form of fixed-point iteration, instantiate it as a novel test oracle, and also employ it to form a mitigation scheme for LLMs’ behavioral inconsistency on ethics-related inputs. To facilitate testing, we also create a comprehensive test suite, EthicsSuite , with 20K moral situations. In our study, we evaluate eight widely used LLMs. Our experiments reveal that LLMs are prone to ethical inconsistencies, with 81.22% of our test cases prompting ethically inconsistent suggestions on average. Our human evaluation suggests that the majority of these inconsistencies indeed manifest unethical biases. Our mitigation scheme effectively refines a significant number (80.1%) of these suggestions for commercial LLMs such as GPT-4 and Claude.

Data quality issues have been a long-standing challenge in the database community. Erroneous data can lead to incorrect query results, which in turn affect the credibility of the data-driven decisions. To circumvent this issue, a common practice is to discovery integrity constraints and enforce them on the data to ensure its quality. For instance, one can use constraints entailed by functional dependencies (FDs) to detect violations in the data. However, existing approaches fail to effectively discover them from noisy data. In this paper, we present a novel form of integrity constraints as a program under a domain-specific language (DSL) that can be used to detect and rectify errors in the data. On top of DSL, we propose an efficient synthesis algorithm that leverages the statistical structural properties of the data to generate the sketch of the program that significantly reduces the search space and speedup the synthesis process. To demonstrate the usefulness of our approach, we evaluate it on 12 real-world datasets for error detection. Then, we show that the synthesized integrity constraints can be used to solidify ML-integrated SQL queries over 48 queries, leading to an average reduction of 87% in the error rates. Our open-source artifact, including the G uardrail framework and the datasets, is available for the community to use [2].

Prescriptive analytics seeks to identify optimal interventions for achieving desired outcomes, with causal inference playing a pivotal role in assessing intervention impacts on complex systems. However, existing approaches frequently neglect critical data privacy considerations and provide no means to verify the integrity of their recommendations. These limitations hinder its adoption in high-stakes domains such as healthcare and finance. In this paper, we introduce, zkCLEAR, a zero-knowledge proof (ZKP)-based C ausal Inference ( LEA rning and R easoning) framework for privacy-preserving and verifiable prescriptive analytics. Our solution allows data owners or service providers to cryptographically prove the validity of prescriptive conclusions derived from causal analysis without disclosing sensitive source data or proprietary causal models. We develop a suite of ZKP-friendly causal operators to build efficient causal modules, including structure learning, parameter learning, probabilistic inference, and counterfactual reasoning. To optimize performance, we also introduce a workflow decomposition strategy to facilitate efficient proof generation for complex workloads. We demonstrate the utility of zkCLEAR through three real-world applications. The framework faithfully follows the behavior of non-ZKP counterparts, with moderate overheads for privacy and verifiability. Additionally, we evaluate its efficiency and scalability using real-world datasets. It shows up to a 35.1× speedup in proof generation time and a 214.5× reduction in proof size compared to current general-purpose ZKP systems.

Software call stack is a sequence of function calls that are executed during the runtime of a software program. Software call stack analysis (CSA) is widely used in software engineering to analyze the runtime behavior of software, which can be used to optimize the software performance, identify bugs, and profile the software. Despite the benefits of CSA, it has recently come under scrutiny due to concerns about privacy. To date, software is often deployed at user-side devices like mobile phones and smart watches. The collected call stacks may thus contain privacy-sensitive information, such as healthy information or locations, depending on the software functionality. Leaking such information to third parties may cause serious privacy concerns such as discrimination and targeted advertisement. This paper presents PP-CSA, a practical and privacy-preserving CSA framework that can be deployed in real-world scenarios. Our framework leverages local differential privacy (LDP) as a principled privacy guarantee, to mutate the collected call stacks and protect the privacy of individual users. Furthermore, we propose several key design principles and optimizations in the technical pipeline of PP-CSA, including an encoder-decoder scheme to properly enforce LDP over software call stacks, and several client/server-side optimizations to largely improve the efficiency of PP-CSA. Our evaluation over real-world Java and Android programs shows that our privacy-preserving CSA pipeline can achieve high utility and privacy guarantees while maintaining high efficiency. We have released our implementation of PP-CSA as an open-source project at https://github.com/wangzhaoyu07/PP-CSA for results reproducibility. We will provide more detailed documents to support and the usage and extension of the community.