New strategy uses consumer and government actions for climate change.
problem Insufficient low-carbon investments despite ethical savings potential.
method Voluntary low-carbon fund and ethical investments for sustainable development.
result Potential for global climate change response through individual actions and investments.
This paper evaluates AI ethics guidelines and their implementation.
problem Lack of comprehensive and effective AI ethics guidelines.
method Comprehensive evaluation and comparison of released ethics guidelines.
result Identifies overlaps and omissions in AI ethics guidelines.
Investors prioritize ESG in crypto-assets, showing higher exposure than traditional assets.
problem Understanding ESG preferences in crypto-assets and their investment behavior.
method A representative household finance survey in Austria to examine ESG preferences and crypto-investment exposure.
result ESG-conscious investors have higher exposure to crypto-assets compared to traditional asset classes.
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.
We show how to incorporate ethical principles into machine learning models.
problem Machine-learned systems often violate deontological ethical principles.
method Add shape constraints to models to ensure positive responses to relevant inputs.
result Shape constraints help produce more ethical AI by avoiding penalties on good attributes.
A new financial system with ethics risk modeled using fractional calculus.
problem Modeling financial systems with ethical considerations and market confidence.
method Introduced a five-dimensional conformable derivative financial system and a discretization scheme.
result Numerical solutions of the conformable derivative system were tested for hyperchaos.
BERT improves machines' ethical and moral decision-making.
problem Teaching machines ethical and moral choices.
method Applying BERT to human texts to extract moral bias scores.
result BERT improves the moral compass of machines.
Article calls for data scientists to join ethics debate.
problem Impact of data science on society and ethics.
method Systematic approach from CNIL, established ethical guidelines.
result Data science requires professional ethics to build trust.
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.
20 questions to improve AI research transparency, replicability, ethics, and effectiveness.
problem Lack of transparency, replicability, ethical concerns, and effectiveness in AI research.
method Presenting 20 questions to guide project planning and post-hoc evaluation.
result Facilitating a discussion to develop an international consensus framework.
This paper critiques machine learning fairness from a consequentialist perspective.
problem The ethical foundations of fairness in machine learning.
method Consequentialist analysis of existing fairness definitions and machine learning perspectives.
result Consequentialism highlights ethical tradeoffs in fairness definitions and decision making.
Model shows how social norms and individual ethics affect tax evasion.
problem Effects of social norms and individual ethics on tax evasion.
method Agent-based model with simulations of different tax compliance behaviors.
result Threshold levels in society composition explain tax evasion extent.
The paper argues for applying fairness in machine learning, even partial, as an improvement.
problem The lack of applied fairness in machine learning products.
method Elaborates on the importance of applying fairness, even partial, in machine learning systems.
result The paper supports the idea that even partial fairness is better than no fairness.
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.
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.
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…
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.
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…
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.
Paper discusses ethical norms for machine learning to prevent misuse.
problem Harmful misuse of machine learning applications.
method Proposes review parameters for ethical framework.
result Ethical guidelines for sharing sensitive machine learning information.
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.
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.
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.
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.
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.
Financial services need explainable AI for fair lending.
problem Challenges in credit decisioning due to compliance and ethical considerations.
method Use of explainable AI models to address fairness and ethics in lending.
result Explainable AI models improve fairness and ethics in lending decisions.
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.
This paper tackles interpretability of LLMs in finance.
problem Complexity and lack of transparency of LLMs in finance.
method Mechanistic interpretability to understand LLM behavior.
result Demonstrates practical relevance of mechanistic interpretability in financial use cases.
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.
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.
Paper analyzes minimal investment risk with budget and concentration constraints.
problem Minimal investment risk in portfolio optimization with budget and concentration constraints.
method Replica analysis to consider the minimal investment risk.
result Minimal investment risk with concentration constraint is larger than without.
The paper clarifies long-horizon investment and DCA, showing no risk reduction but different exposure profiles.
problem Misleading claims about reducing risk with longer investment horizons and DCA.
method Unified probabilistic framework, defining risk and uncertainty, and introducing effective investment exposure.
result Different investment timing strategies can lead to distinct exposure profiles over time, affecting risk and uncertainty.
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.
Investors with CPT make optimal investment decisions in a market with transaction costs.
problem Optimal investment strategy under CPT in a market with transaction costs.
method Explicit solution for optimal investment strategy in two examples.
result Investment strategy is affected by transaction costs and risk aversion.
Investment timing problem solved with threshold strategies in real options theory.
problem Investment timing problem in real options theory.
method Modeling investment project present values as a diffusion process, proving conditions for optimal investment time and optimality of threshold strategies.
result Necessary and sufficient conditions for an optimal investment time induced by a threshold strategy.
ChatGPT scores corporate investment plans, predicting future spending and returns.
problem Measuring and predicting corporate investment plans.
method Created a firm-level ChatGPT investment score based on conference calls.
result The investment score predicts future capital expenditures and returns.
Investment herding can reduce household consumption, a phenomenon called crowding-out effect.
problem Investment herding's impact on household consumption.
method Optimal control theory to model and solve for household investment and consumption decisions.
result Existence of crowding-out effect due to investment herding.
Qlib aims to integrate AI into quantitative investment.
problem Challenges in applying AI to quantitative investment.
method Design and develop Qlib to accommodate AI-driven workflow.
result Qlib realizes the potential of AI technologies in quantitative investment.
AI enhances quantitative investment for better returns and risk control.
problem Achieving stable returns through AI in quantitative investment.
method Application of AI technology in quantitative investment strategies.
result AI improves investment performance and risk management.
The investment economy is a main characteristic of prosperous society. The investment portfolio management is a main financial problem, which has to be solved by the investment, commercial and central banks with the application of modern portfolio theory in the investment economy. We use the learning analytics together…
Investing in declining tech boosts profits, study finds.
problem Optimal decision-making in declining profit streams.
method Modeling profit stream as Brownian motion with negative drift, analyzing thresholds for investment and exit.
result Investment threshold decreases in volatility when profit boost is large.
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.
This paper proposes an embedding-based neural network for more accurate investment return prediction.
problem Accurately predicting investment returns requires understanding industry knowledge and news, as well as leveraging relevant theories.
method The approach uses embedding to encode investment IDs into low-dimensional vectors, leveraging dual branches to separate different information, and employs the swish activation function.
result The proposed embedding-based dual branch model outperforms traditional machine learning models like Xgboost, Lightgbm, and Catboost on the Ubiquant Market Prediction dataset.
Investment patterns in Eurozone linked to economic indicators, revealing network interconnectedness.
problem Understanding the relationship between foreign portfolio investment and economic indicators in the Eurozone network.
method Analysis of strongly connected investment network of Eurozone and its major trading partners, using centrality measures and network visualization techniques.
result Strong correlation between investment patterns and economic indicators within the Eurozone network, with varying centrality measures among network members.
New approach to goal-based investing using hedging and reinforcement learning.
problem Maximizing probability of reaching investment goals with varying risk aversion.
method Lower partial moments, quantile hedging, efficient hedging, reinforcement learning.
result Optimal investment policies for goal-based investing are equivalent.