Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the …
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48 results for “ethical practices”
The goal of this article is to inspire data scientists to participate in the debate on the impact that their professional work has on society, and to become active in public debates on the digital world as data science professionals. How do ethical principles (e.g., fairness, justice, beneficence, and non-maleficence) …
Introduces data ethics for mathematicians, covering background, open data, and privacy.
problem Ensuring ethical use of data in mathematical research and education.
method Overview of data ethics concepts, societal context, and resources.
result Highlights the importance of ethical considerations in data use.
This paper uses decolonial theory to improve AI's ethical development.
problem AI's risks to vulnerable peoples and negative impacts of innovation.
method Embedding decolonial critical approach in AI technical practice.
result Developing tactics to align AI with ethical principles.
Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit. Whilst there is a lot of promising research currently being undertaken, the literature as a whole lacks:…
New approach to optimal income tax theory tackles inequity issues.
problem Optimal tax schedules often lead to minimal tax rates for higher earners, contradicting ethical practices.
method Developed a theorem for piecewise-linear environment and introduced a new utility function parameter.
result New approach leads to more equitable tax schedules, interpreting optimality criteria easily.
The paper investigates ethical issues in large image datasets, focusing on pornographic content.
problem Ethical issues in large-scale computer vision datasets, particularly concerning pornographic content.
method Cross-sectional model-based quantitative census covering various factors in the ImageNet-ILSVRC-2012 dataset.
result The dataset contains verifiably pornographic images, including non-consensual and voyeuristic content.
With a point of departure in the concept "uncomfortable knowledge," this article presents a case study of how the American Planning Association (APA) deals with such knowledge. APA was found to actively suppress publicity of malpractice concerns and bad planning in order to sustain a boosterish image of planning. In th…
FairTrade uses variational inference to create fair predictions in causal models.
problem Creating fair predictions in machine learning models with causal reasoning.
method FairTrade uses variational inference to account for unobserved confounders and integrates fairness constraints on causal paths.
result Demonstrates the effectiveness of FairTrade in creating fair predictions in both simulated and real-world datasets.
This paper presents the contemporary Fundamental Theorem of Asset Pricing as being equivalent to approaches to pricing that emerged before 1700 in the context of Virtue Ethics. This is done by considering the history of science and mathematics in the thirteenth and seventeenth century. An explanation as to why these ap…
Research analyzes ethical concerns around MEV on blockchain and social media.
problem Fairness issues in transaction ordering on blockchain.
method Applied NLP methods to analyze topics in tweets on MEV.
result Tweets discussed ethical concerns like security, equity, and solutions to MEV.
Allowing machines to choose whether to kill humans would be devastating for world peace and security. But how do we equip machines with the ability to learn ethical or even moral choices? Jentzsch et al.(2019) showed that applying machine learning to human texts can extract deontological ethical reasoning about "right"…
We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes." We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding…
A new chaotic financial system is proposed by considering ethics involvement in a four-dimensional financial system with market confidence. A five-dimensional conformable derivative financial system is presented by introducing conformable fractional calculus to the integer-order system. A discretization scheme is propo…
Study aims to measure and mitigate biases in motor insurance pricing.
problem Ethical biases in motor insurance pricing that affect fairness and regulatory compliance.
method Statistical methodologies and data analysis to measure and mitigate biases.
result Developed tools to measure and mitigate ethical biases in motor insurance pricing.
The paper evaluates the importance of monotonicity in AI fairness across various fields.
problem Ensuring fairness in AI applications across criminology, education, health care, and finance.
method Theoretical reasoning, simulation, and extensive empirical analysis of monotonic neural additive models (MNAMs).
result Monotonicity is essential for fairness in AI ethics and society, especially in criminology, education, health care, and finance.
Machine learning conferences face ethical issues in review process.
problem Ethical issues in the review process of machine learning conferences.
method Study of recruitment issues, double-blind process infringements, fraudulent behaviors, biases, and appendix phenomenon.
result Highlighting the need for awareness in the machine learning community.
Recent work on fairness in machine learning has primarily emphasized how to define, quantify, and encourage "fair" outcomes. Less attention has been paid, however, to the ethical foundations which underlie such efforts. Among the ethical perspectives that should be taken into consideration is consequentialism, the posi…
LLM sandbox and persona dynamics create unethical reality gaps that shift risk to users.
problem Ethical issues arise from LLMs generating reality gaps that shift risk to uninformed users.
method Analyzes the ethical implications of LLM sandbox and persona dynamics, comparing them to financial regulation and compliance.
result Active generation of reality gaps is unethical as it shifts epistemic risk to users.
Framework enhances AI explainability by aligning with human cognitive models.
problem Lack of explainability in AI models hinders trust and accountability.
method Integrates explainability techniques with Malle's five category model of behavior explanation.
result Demonstrates practical relevance in credit risk assessment and regulatory analysis.
This paper argues that the fundamental principle of contemporary financial economics is balanced reciprocity, not the principle of utility maximisation that is important in economics more generally. The argument is developed by analysing the mathematical Fundamental Theory of Asset Pricing with reference to the emergen…
New method reduces indirect discrimination in insurance risk models.
problem Indirect discrimination in insurance risk models using machine learning.
method Mathematical concepts of linear algebra to reduce indirect discrimination.
result Demonstrated promising performance in a concrete case of risk selection in life insurance.
A global consumer-led strategy to tackle climate changephysics.soc-ph
A successful response to climate change needs vast investments in low-carbon research, energy, and sustainable development. Governments can drive research, provide environmental regulation, and accelerate global development, but the necessary low-carbon investments of 2-3% GDP have yet to materialise. A new strategy to…
This paper tests LLMs in finance to assess ethical behavior.
problem Aligning AI with ethical and legal standards in finance.
method Prompted LLMs to simulate CEO behavior, analyzed with logistic regression.
result Significant heterogeneity in LLMs' unethical behavior propensity.
AI enhances financial services but humans are irreplaceable for empathy, presence, and ethics.
problem AI's limitations in financial services, especially with small datasets and human judgment.
method EPOCH framework highlighting five irreplaceable human capabilities: Empathy, Presence, Opinion, Creativity, and Hope.
result Humans are essential for trust, innovation, and consumer experience in financial services.
The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.
Machine learning practitioners are often ambivalent about the ethical aspects of their products. We believe anything that gets us from that current state to one in which our systems are achieving some degree of fairness is an improvement that should be welcomed. This is true even when that progress does not get us 100%…
Counterfactual Fairnessstat.ML
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a …
RAMEN corrects observational data biases for multiple environments.
problem Bias in observational data for causal inference.
method RAMEN algorithm that leverages heterogeneity of multiple data sources.
result RAMEN produces unbiased treatment effect estimates.
Paper defines XAI concepts using category theory.
problem Lack of precise mathematical definitions for XAI.
method Uses Category theory to define XAI concepts rigorously.
result Establishes a theoretical foundation for XAI.
Machine learning uses crowdworkers; determining their status as human subjects is tricky.
problem Determining the appropriate status of ML crowdworkers as human subjects.
method Investigation of natural language processing studies to expose challenges and propose solutions.
result Potential loophole in the U.S. Common Rule for ML research oversight.
Paper protects privacy and fairness in deep learning models.
problem Ensuring fairness in deep learning models while protecting sensitive data.
method Uses differential privacy and Lagrangian duality to design fair predictors.
result Demonstrates improved model performance on prediction tasks.
New framework for contesting algorithmic decisions, not just explaining them.
problem Helping individuals review and correct erroneous algorithmic decisions.
method Operationalized contestability as a natural complement to explainable AI (XAI), identifying three types of evidence for reversal.
result Existing EU legislation already grants individuals legal rights to contest algorithmic decisions.
The study examines dataset usage patterns in machine learning research.
problem Lack of attention to dataset dynamics in machine learning research.
method Analysis of dataset usage patterns across machine learning subcommunities and time periods (2015-2020).
result Increasing concentration on fewer and fewer datasets, significant adoption from other tasks, and concentration across the field on datasets introduced by elite institutions.
Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns. However, it may be the case that derivative datasets or predictive models developed within individu…
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventionscs.LG
The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact comput…
Deep Neural Networks have achieved huge success at a wide spectrum of applications from language modeling, computer vision to speech recognition. However, nowadays, good performance alone is not sufficient to satisfy the needs of practical deployment where interpretability is demanded for cases involving ethics and mis…
AI enhances ESG practices in finance, but requires careful consideration.
problem Regulatory pressures and stakeholder awareness drive ESG adoption.
method Industrial survey categorizing AI applications in ESG.
result AI improves analytical capabilities, risk assessment, and customer engagement.
Paper reinterprets marginal productivity theory using vectorial products, challenging traditional ethical interpretations.
problem Challenges traditional ethical interpretations of marginal productivity theory.
method Formulates marginal productivity theory using vectorial marginal products, contrasting with traditional scalar approach.
result Vectorial marginal products conflict with traditional distributive shares picture of property.
Neural style transfer, first proposed by Gatys et al. (2015), can be used to create novel artistic work through rendering a content image in the form of a style image. We present a novel method of reconstructing lost artwork, by applying neural style transfer to x-radiographs of artwork with secondary interior artwork …
Optimal Sampling for Generalized Linear Model under Measurement Constraint with Surrogate Variablesstat.ME
Optimal sampling strategy improves prediction accuracy with surrogate variables under measurement constraints.
problem Measurement-constrained datasets and lack of labeled data.
method A-optimality criterion for optimal sampling, leveraging surrogate variables.
result Achieves lower asymptotic variance and reduced empirical mean squared error.
This thesis tackles bias in AI decision-making in banking.
problem Bias in AI-driven banking decisions.
method Understanding, mitigating, and accounting for bias in AI systems.
result Establishment of Responsible AI practices for fair decision-making.
AI models forget statistics' lesson: correlation doesn't imply causation.
problem AI models often produce flawed causal models due to ignoring correlation vs causation.
method Demonstrates examples of flawed AI models and proposes rethinking core models.
result Current efforts to make AI models ethical are insufficient.
In this report, an automated bartender system was developed for making orders in a bar using hand gestures. The gesture recognition of the system was developed using Machine Learning techniques, where the model was trained to classify gestures using collected data. The final model used in the system reached an average …
AI random forest model improves credit risk scoring for Azerbaijani SMEs.
problem Improving accuracy in identifying defaulters for SME loans.
method Used Python to compare a Delphi model with a random forest model, measuring accuracy, precision, recall, and F-1 scores.
result Significant improvements in model performance (e.g., from 0.69 to 0.83 in accuracy).
This paper surveys algorithmic advancements in Optimal Transport with applications in machine learning.
problem Quantifying differences between distributions in various fields.
method Examines classical and modern computational techniques, including Sinkhorn iterations and primal-dual strategies.
result Highlights the robustness and scalability of OT algorithms in high-dimensional problems.
Machine learning algorithms can unintentionally discriminate; tools detect and fix this.
problem Unintentional discrimination in machine learning algorithms.
method Statistical tools to detect and eliminate biases.
result Tools can identify and mitigate algorithmic discrimination.
Digital twins improve single-arm trials by providing robust treatment effect estimates.
problem Lack of control arms in single-arm trials limits their gold-standard evidence.
method Outcome-model-based synthetic controls using machine learning models trained on historical data.
result Digital twins offer more robust treatment effect estimates and principled corrections.