Deontological Ethics By Monotonicity Shape Constraints

• URL: http://arxiv.org/abs/2001.11990v2
• Date: Fri, 13 Mar 2020 00:20:00 GMT
• Title: Deontological Ethics By Monotonicity Shape Constraints
• Authors: Serena Wang and Maya Gupta
• Abstract summary: We show how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms. We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding shape constraints that constrain the model to respond only positively to relevant inputs.
• Score: 6.117084972237769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 shape constraints that constrain the model to respond only positively to relevant inputs. We analyze the relationship between these deontological constraints that act on individuals and the consequentialist group-based fairness goals of one-sided statistical parity and equal opportunity. This strategy works with sensitive attributes that are Boolean or real-valued such as income and age, and can help produce more responsible and trustworthy AI.

Related papers

• Ethical AI: Towards Defining a Collective Evaluation Framework [0.3413711585591077]
  Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems.<n>Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias.<n>This article proposes a modular ethical assessment framework built on ontological blocks of meaning-discrete, interpretable units.
  arXiv  Detail & Related papers  (2025-05-30T21:10:47Z)
• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage. Models may behave unreliably due to poorly explored failure modes. causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Deontic Temporal Logic for Formal Verification of AI Ethics [4.028503203417233]
  This paper proposes a formalization based on deontic logic to define and evaluate the ethical behavior of AI systems. It introduces axioms and theorems to capture ethical requirements related to fairness and explainability. The authors evaluate the effectiveness of this formalization by assessing the ethics of the real-world COMPAS and loan prediction AI systems.
  arXiv  Detail & Related papers  (2025-01-10T07:48:40Z)
• Ethical and Scalable Automation: A Governance and Compliance Framework for Business Applications [0.0]
  This paper introduces a framework ensuring that AI must be ethical, controllable, viable, and desirable. Different case studies validate this framework by integrating AI in both academic and practical environments.
  arXiv  Detail & Related papers  (2024-09-25T12:39:28Z)
• The Impossibility of Fair LLMs [59.424918263776284]
  The need for fair AI is increasingly clear in the era of large language models (LLMs) We review the technical frameworks that machine learning researchers have used to evaluate fairness. We develop guidelines for the more realistic goal of achieving fairness in particular use cases.
  arXiv  Detail & Related papers  (2024-05-28T04:36:15Z)
• A Novel Neural-symbolic System under Statistical Relational Learning [50.747658038910565]
  We propose a general bi-level probabilistic graphical reasoning framework called GBPGR. In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models. Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
  arXiv  Detail & Related papers  (2023-09-16T09:15:37Z)
• Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance [0.8566457170664925]
  monotonicity axioms are essential in all areas of fairness, including criminology, education, health care, and finance. Our research contributes to the interdisciplinary research at the interface of AI ethics, explainable AI (XAI), and human-computer interactions (HCIs)
  arXiv  Detail & Related papers  (2023-01-17T18:21:31Z)
• Bias-inducing geometries: an exactly solvable data model with fairness implications [13.690313475721094]
  We introduce an exactly solvable high-dimensional model of data imbalance. We analytically unpack the typical properties of learning models trained in this synthetic framework. We obtain exact predictions for the observables that are commonly employed for fairness assessment.
  arXiv  Detail & Related papers  (2022-05-31T16:27:57Z)
• Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach [131.20444904674494]
  We tackle the problem of measuring group fairness under unawareness of sensitive attributes. We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.
  arXiv  Detail & Related papers  (2021-09-17T13:45:46Z)
• On the Opportunities and Risks of Foundation Models [256.61956234436553]
  We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration.
  arXiv  Detail & Related papers  (2021-08-16T17:50:08Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• AI-Ethics by Design. Evaluating Public Perception on the Importance of Ethical Design Principles of AI [0.0]
  We investigate how ethical principles are weighted in comparison to each other. We show that different preference models for ethically designed systems exist among the German population.
  arXiv  Detail & Related papers  (2021-06-01T09:01:14Z)
• Constrained Learning with Non-Convex Losses [119.8736858597118]
  Though learning has become a core technology of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced solutions.
  arXiv  Detail & Related papers  (2021-03-08T23:10:33Z)
• Fairness in Machine Learning [15.934879442202785]
  We show how causal Bayesian networks can play an important role to reason about and deal with fairness. We present a unified framework that encompasses methods that can deal with different settings and fairness criteria.
  arXiv  Detail & Related papers  (2020-12-31T18:38:58Z)
• Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes [72.64975113835018]
  Motivated by descriptive ethics, we investigate a novel, data-driven approach to machine ethics. We introduce Scruples, the first large-scale dataset with 625,000 ethical judgments over 32,000 real-life anecdotes. Our dataset presents a major challenge to state-of-the-art neural language models, leaving significant room for improvement.
  arXiv  Detail & Related papers  (2020-08-20T17:34:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.