Machine learning models are increasingly used in critical decision-making applications. However, these models are susceptible to replicating or even amplifying bias present in real-world data. While there are various bias mitigation methods and base estimators in the literature, selecting the optimal model for a specific application remains challenging. This paper focuses on binary classification and proposes FairGridSearch, a novel framework for comparing fairness-enhancing models. FairGridSearch enables experimentation with different model parameter combinations and recommends the best one. The study applies FairGridSearch to three popular datasets (Adult, COMPAS, and German Credit) and analyzes the impacts of metric selection, base estimator choice, and classification threshold on model fairness. The results highlight the significance of selecting appropriate accuracy and fairness metrics for model evaluation. Additionally, different base estimators and classification threshold values affect the effectiveness of bias mitigation methods and fairness stability respectively, but the effects are not consistent across all datasets. Based on these findings, future research on fairness in machine learning should consider a broader range of factors when building fair models, going beyond bias mitigation methods alone.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. ROBERTS' RULES: Supreme Court chief justice report urges caution on use of AI ahead of contentious election year. PROTECT CHILDREN: Congress must stop a new AI tool used to exploit children. Rep. Jay Obernolte, R-Calif., attends a House Natural Resources Subcommittee on National Parks, Forests, and Public Lands hearing on the 9/11 Memorial and Museum Act and other legislation in Longworth Building on Tuesday, December 7, 2021. PRIVACY FEARS: Top lawmaker warns Congress needs to regulate AI appropriately.

The world of gaming is being rocked by an AI controversy that could upend the multi-billion dollar industry. Sexual predators are using a powerful new tool to exploit children -- AI image generators. Users on a single dark-web forum shared nearly 3,000 AI-generated images of child sexual abuse in just one month, according to a recent report from the UK-based Internet Watch Foundation. Unfortunately, current child sexual abuse laws are outdated. They don't adequately account for the unique dangers AI and other emerging technologies pose.

Dr. Sheila Nazarian, the star of Netflix's "Skin Decision: Before and After," said celebrities who want to speak out on the Israel-Hamas war should educate themselves first. A woman in Arizona has sued her plastic surgeon, accusing him of botching her procedure while operating under the influence of alcohol, leaving her in distress, according to local reports. Dr. Bradley Becker is a "Double Board Certified-Plastic Reconstructive surgeon who has been practicing in AZ for 21 years. "It's hard to feel like you can go out when you feel like Frankenstein," his former patient, Wendy Ellsworth, said in an interview Friday with Phoenix New Times. Ellsworth said she got a tummy tuck and breast reduction with Dr. Becker. Ellsworth sued Becker in Maricopa County Superior Court in September, accusing the Glendale plastic and reconstructive surgeon of "medical negligence," "battery" and "intentional infliction of emotional distress," the report said. Woman said she felt like Frankenstein after plastic surgery allegedly went wrong. Ellsworth said she thought she smelled alcohol when Dr. Becker came to see her before the operation began. "I had put my money down.

Out-of-distribution (OOD) detection plays a crucial role in ensuring the security of neural networks. Existing works have leveraged the fact that In-distribution (ID) samples form a subspace in the feature space, achieving state-of-the-art (SOTA) performance. However, the comprehensive characteristics of the ID subspace still leave under-explored. Recently, the discovery of Neural Collapse ($\mathcal{NC}$) sheds light on novel properties of the ID subspace. Leveraging insight from $\mathcal{NC}$, we observe that the Principal Angle between the features and the ID feature subspace forms a superior representation for measuring the likelihood of OOD. Building upon this observation, we propose a novel $\mathcal{NC}$-inspired OOD scoring function, named Entropy-enhanced Principal Angle (EPA), which integrates both the global characteristic of the ID subspace and its inner property. We experimentally compare EPA with various SOTA approaches, validating its superior performance and robustness across different network architectures and OOD datasets.

Natural Language Processing (NLP) plays an important role in our daily lives, particularly due to the enormous progress of Large Language Models (LLM). However, NLP has many fairness-critical use cases, e.g., as an expert system in recruitment or as an LLM-based tutor in education. Since NLP is based on human language, potentially harmful biases can diffuse into NLP systems and produce unfair results, discriminate against minorities or generate legal issues. Hence, it is important to develop a fairness certification for NLP approaches. We follow a qualitative research approach towards a fairness certification for NLP. In particular, we have reviewed a large body of literature on algorithmic fairness, and we have conducted semi-structured expert interviews with a wide range of experts from that area. We have systematically devised six fairness criteria for NLP, which can be further refined into 18 sub-categories. Our criteria offer a foundation for operationalizing and testing processes to certify fairness, both from the perspective of the auditor and the audited organization.

Alan Turing was a British mathematician born on June 23, 1912 In Maida Vale, London, to father Julius, a civil servant, and mother Ethel, the daughter of a railway engineer. His talents were recognised early on at school but he struggled with his teachers when he began boarding at Sherborne School aged 13 because he was too fixated on science. Turing continued to excel at maths but his time at Sherborne was also rocked by the death of his close friend Christopher Morcom from tuberculosis. Morcom was described as Turing's'first love' and he remained close with his mother following his death, writing to her on Morcom's birthday each year. He then moved on to Cambridge where he studied at King's College, graduating with a first class degree in mathematics.

Organizations face the challenge of ensuring compliance with an increasing amount of requirements from various regulatory documents. Which requirements are relevant depends on aspects such as the geographic location of the organization, its domain, size, and business processes. Considering these contextual factors, as a first step, relevant documents (e.g., laws, regulations, directives, policies) are identified, followed by a more detailed analysis of which parts of the identified documents are relevant for which step of a given business process. Nowadays the identification of regulatory requirements relevant to business processes is mostly done manually by domain and legal experts, posing a tremendous effort on them, especially for a large number of regulatory documents which might frequently change. Hence, this work examines how legal and domain experts can be assisted in the assessment of relevant requirements. For this, we compare an embedding-based NLP ranking method, a generative AI method using GPT-4, and a crowdsourced method with the purely manual method of creating relevancy labels by experts. The proposed methods are evaluated based on two case studies: an Australian insurance case created with domain experts and a global banking use case, adapted from SAP Signavio's workflow example of an international guideline. A gold standard is created for both BPMN2.0 processes and matched to real-world textual requirements from multiple regulatory documents. The evaluation and discussion provide insights into strengths and weaknesses of each method regarding applicability, automation, transparency, and reproducibility and provide guidelines on which method combinations will maximize benefits for given characteristics such as process usage, impact, and dynamics of an application scenario.

Land use / land cover (LULC) modeling is a challenging task due to long-range dependencies between geographic features and distinct spatial patterns related to topography, ecology, and human development. We identify a close connection between modeling of spatial patterns of land use and the task of image inpainting from computer vision and conduct a study of a modified PixelCNN architecture with approximately 19 million parameters for modeling LULC. In comparison with a benchmark spatial statistical model, we find that the former is capable of capturing much richer spatial correlation patterns such as roads and water bodies but does not produce a calibrated predictive distribution, suggesting the need for additional tuning. We find evidence of predictive underdispersion with regard to important ecologically-relevant land use statistics such as patch count and adjacency which can be ameliorated to some extent by manipulating sampling variability.

Real-world last-mile delivery datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile delivery dataset exists to support research in this field. In this paper, we introduce \texttt{LaDe}, the first publicly available last-mile delivery dataset with millions of packages from the industry. LaDe has three unique characteristics: (1) Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation. (2) Comprehensive information. It offers original package information, such as its location and time requirements, as well as task-event information, which records when and where the courier is while events such as task-accept and task-finish events happen. (3) Diversity. The dataset includes data from various scenarios, including package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations. We verify LaDe on three tasks by running several classical baseline models per task. We believe that the large-scale, comprehensive, diverse feature of LaDe can offer unparalleled opportunities to researchers in the supply chain community, data mining community, and beyond. The dataset homepage is publicly available at https://huggingface.co/datasets/Cainiao-AI/LaDe.