Exosoul: ethical profiling in the digital world

• URL: http://arxiv.org/abs/2204.01588v1
• Date: Wed, 30 Mar 2022 10:54:00 GMT
• Title: Exosoul: ethical profiling in the digital world
• Authors: Costanza Alfieri, Paola Inverardi, Patrizio Migliarini and Massimiliano Palmiero
• Abstract summary: The project Exosoul aims at developing a personalized software exoskeleton which mediates actions in the digital world according to the moral preferences of the user. The approach is hybrid, first based on the identification of profiles in a top-down manner, and then on the refinement of profiles by a personalized data-driven approach. We consider the correlations between ethics positions (idealism and relativism) personality traits (honesty/humility, conscientiousness, Machiavellianism and narcissism) and worldview (normativism)
• Score: 3.6245424131171813
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The development and the spread of increasingly autonomous digital technologies in our society pose new ethical challenges beyond data protection and privacy violation. Users are unprotected in their interactions with digital technologies and at the same time autonomous systems are free to occupy the space of decisions that is prerogative of each human being. In this context the multidisciplinary project Exosoul aims at developing a personalized software exoskeleton which mediates actions in the digital world according to the moral preferences of the user. The exoskeleton relies on the ethical profiling of a user, similar in purpose to the privacy profiling proposed in the literature, but aiming at reflecting and predicting general moral preferences. Our approach is hybrid, first based on the identification of profiles in a top-down manner, and then on the refinement of profiles by a personalized data-driven approach. In this work we report our initial experiment on building such top-down profiles. We consider the correlations between ethics positions (idealism and relativism) personality traits (honesty/humility, conscientiousness, Machiavellianism and narcissism) and worldview (normativism), and then we use a clustering approach to create ethical profiles predictive of user's digital behaviors concerning privacy violation, copy-right infringements, caution and protection. Data were collected by administering a questionnaire to 317 young individuals. In the paper we discuss two clustering solutions, one data-driven and one model-driven, in terms of validity and predictive power of digital behavior.

Related papers

• Smoke Screens and Scapegoats: The Reality of General Data Protection Regulation Compliance -- Privacy and Ethics in the Case of Replika AI [1.325665193924634]
  This paper takes a critical approach towards examining the intricacies of these issues within AI companion services. We analyze articles from public media about the company and its practices to gain insight into the trustworthiness of information provided in the policy. The results reveal despite privacy notices, data collection practices might harvest personal data without users' full awareness.
  arXiv  Detail & Related papers  (2024-11-07T07:36:19Z)
• Temporal fingerprints: Identity matching across fully encrypted domain [6.44378713940627]
  Cross domain identity matching is essential for practical applications and theoretical insights into the privacy implications of data disclosure. We demonstrate that individual temporal data, in the form of inter-event times distribution, constitutes an individual temporal fingerprint, allowing for matching profiles across different domains back to their associated real-world entity. Our findings indicate that simply knowing when an individual is active, even if information about who they talk to and what they discuss is lacking, poses risks to users' privacy.
  arXiv  Detail & Related papers  (2024-07-05T08:41:28Z)
• Eagle: Ethical Dataset Given from Real Interactions [74.7319697510621]
  We create datasets extracted from real interactions between ChatGPT and users that exhibit social biases, toxicity, and immoral problems. Our experiments show that Eagle captures complementary aspects, not covered by existing datasets proposed for evaluation and mitigation of such ethical challenges.
  arXiv  Detail & Related papers  (2024-02-22T03:46:02Z)
• Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems [103.416202777731]
  We study "persona biases", which we define to be the sensitivity of dialogue models' harmful behaviors contingent upon the personas they adopt. We categorize persona biases into biases in harmful expression and harmful agreement, and establish a comprehensive evaluation framework to measure persona biases in five aspects: Offensiveness, Toxic Continuation, Regard, Stereotype Agreement, and Toxic Agreement.
  arXiv  Detail & Related papers  (2023-10-08T21:03:18Z)
• Large Language Models Can Infer Psychological Dispositions of Social Media Users [1.0923877073891446]
  We test whether GPT-3.5 and GPT-4 can derive the Big Five personality traits from users' Facebook status updates in a zero-shot learning scenario. Our results show an average correlation of r =.29 (range = [.22,.33]) between LLM-inferred and self-reported trait scores. predictions were found to be more accurate for women and younger individuals on several traits, suggesting a potential bias stemming from the underlying training data or differences in online self-expression.
  arXiv  Detail & Related papers  (2023-09-13T01:27:48Z)
• SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles [50.90773979394264]
  This paper studies a model that protects the privacy of individuals' sensitive information while also allowing it to learn non-discriminatory predictors. A key characteristic of the proposed model is to enable the adoption of off-the-selves and non-private fair models to create a privacy-preserving and fair model.
  arXiv  Detail & Related papers  (2022-04-11T14:42:54Z)
• Leveraging Privacy Profiles to Empower Users in the Digital Society [7.350403786094707]
  Privacy and ethics of citizens are at the core of the concerns raised by our increasingly digital society. We focus on the privacy dimension and contribute a step in the above direction through an empirical study on an existing dataset collected from the fitness domain. The results reveal that a compact set of semantic-driven questions helps distinguish users better than a complex domain-dependent one.
  arXiv  Detail & Related papers  (2022-04-01T15:31:50Z)
• Representative & Fair Synthetic Data [68.8204255655161]
  We present a framework to incorporate fairness constraints into the self-supervised learning process. We generate a representative as well as fair version of the UCI Adult census data set. We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.
  arXiv  Detail & Related papers  (2021-04-07T09:19:46Z)
• Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach [54.32266555843765]
  This paper studies a model that protects the privacy of the individuals sensitive information while also allowing it to learn non-discriminatory predictors. The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints.
  arXiv  Detail & Related papers  (2020-09-26T10:50:33Z)
• Beyond Our Behavior: The GDPR and Humanistic Personalization [0.0]
  We propose a new paradigm of humanistic personalization. We re-frame distinction between implicit and explicit data collection as one of nonconscious ("organismic") behavior and conscious ("reflective") action. We discuss how an emphasis on narrative accuracy can reduce opportunities for data done done of injustice subjects.
  arXiv  Detail & Related papers  (2020-08-31T07:40:09Z)
• 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.