A Framework for Assessing Proportionate Intervention with Face
Recognition Systems in Real-Life Scenarios
- URL: http://arxiv.org/abs/2402.05731v1
- Date: Thu, 8 Feb 2024 15:07:21 GMT
- Title: A Framework for Assessing Proportionate Intervention with Face
Recognition Systems in Real-Life Scenarios
- Authors: Pablo Negri and Isabelle Hupont and Emilia Gomez
- Abstract summary: Face recognition (FR) has reached a high technical maturity but its use needs to be carefully assessed from an ethical perspective.
Recent AI policies propose that such FR interventions should be proportionate and deployed only when strictly necessary.
This paper proposes a framework to contribute to assessing whether an FR intervention is proportionate or not for a given context of use.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Face recognition (FR) has reached a high technical maturity. However, its use
needs to be carefully assessed from an ethical perspective, especially in
sensitive scenarios. This is precisely the focus of this paper: the use of FR
for the identification of specific subjects in moderately to densely crowded
spaces (e.g. public spaces, sports stadiums, train stations) and law
enforcement scenarios. In particular, there is a need to consider the trade-off
between the need to protect privacy and fundamental rights of citizens as well
as their safety. Recent Artificial Intelligence (AI) policies, notably the
European AI Act, propose that such FR interventions should be proportionate and
deployed only when strictly necessary. Nevertheless, concrete guidelines on how
to address the concept of proportional FR intervention are lacking to date.
This paper proposes a framework to contribute to assessing whether an FR
intervention is proportionate or not for a given context of use in the above
mentioned scenarios. It also identifies the main quantitative and qualitative
variables relevant to the FR intervention decision (e.g. number of people in
the scene, level of harm that the person(s) in search could perpetrate,
consequences to individual rights and freedoms) and propose a 2D graphical
model making it possible to balance these variables in terms of ethical cost vs
security gain. Finally, different FR scenarios inspired by real-world
deployments validate the proposed model. The framework is conceived as a simple
support tool for decision makers when confronted with the deployment of an FR
system.
Related papers
- AI and ethics in insurance: a new solution to mitigate proxy
discrimination in risk modeling [0.0]
Driven by the growing attention of regulators on the ethical use of data in insurance, the actuarial community must rethink pricing and risk selection practices.
Equity is a philosophy concept that has many different definitions in every jurisdiction that influence each other without currently reaching consensus.
We propose an innovative method, not yet met in the literature, to reduce the risks of indirect discrimination thanks to mathematical concepts of linear algebra.
arXiv Detail & Related papers (2023-07-25T16:20:56Z) - Measuring Equality in Machine Learning Security Defenses: A Case Study
in Speech Recognition [56.69875958980474]
This work considers approaches to defending learned systems and how security defenses result in performance inequities across different sub-populations.
We find that many methods that have been proposed can cause direct harm, like false rejection and unequal benefits from robustness training.
We present a comparison of equality between two rejection-based defenses: randomized smoothing and neural rejection, finding randomized smoothing more equitable due to the sampling mechanism for minority groups.
arXiv Detail & Related papers (2023-02-17T16:19:26Z) - User-Centered Security in Natural Language Processing [0.7106986689736825]
dissertation proposes a framework of user-centered security in Natural Language Processing (NLP)
It focuses on two security domains within NLP with great public interest.
arXiv Detail & Related papers (2023-01-10T22:34:19Z) - Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive
Privacy Analysis and Beyond [57.10914865054868]
We consider vertical logistic regression (VLR) trained with mini-batch descent gradient.
We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
arXiv Detail & Related papers (2022-07-19T05:47:30Z) - Towards a multi-stakeholder value-based assessment framework for
algorithmic systems [76.79703106646967]
We develop a value-based assessment framework that visualizes closeness and tensions between values.
We give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
arXiv Detail & Related papers (2022-05-09T19:28:32Z) - 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) - Measuring Fairness of Text Classifiers via Prediction Sensitivity [63.56554964580627]
ACCUMULATED PREDICTION SENSITIVITY measures fairness in machine learning models based on the model's prediction sensitivity to perturbations in input features.
We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness.
arXiv Detail & Related papers (2022-03-16T15:00:33Z) - Integrating Testing and Operation-related Quantitative Evidences in
Assurance Cases to Argue Safety of Data-Driven AI/ML Components [2.064612766965483]
In the future, AI will increasingly find its way into systems that can potentially cause physical harm to humans.
For such safety-critical systems, it must be demonstrated that their residual risk does not exceed what is acceptable.
This paper proposes a more holistic argumentation structure for having achieved the target.
arXiv Detail & Related papers (2022-02-10T20:35:25Z) - RobFR: Benchmarking Adversarial Robustness on Face Recognition [41.296221656624716]
Face recognition (FR) has recently made substantial progress and achieved high accuracy on standard benchmarks.
To facilitate a better understanding of the adversarial vulnerability on FR, we develop an adversarial robustness evaluation library on FR named textbfRobFR.
RobFR involves 15 popular naturally trained FR models, 9 models with representative defense mechanisms and 2 commercial FR API services.
arXiv Detail & Related papers (2020-07-08T13:39:22Z) - A Risk Assessment of a Pretrial Risk Assessment Tool: Tussles,
Mitigation Strategies, and Inherent Limits [0.0]
We perform a risk assessment of the Public Safety Assessment (PSA), a software used in San Francisco and other jurisdictions to assist judges in deciding whether defendants need to be detained before their trial.
We articulate benefits and limitations of the PSA solution, as well as suggest mitigation strategies.
We then draft the Handoff Tree, a novel algorithmic approach to pretrial justice that accommodates some of the inherent limitations of risk assessment tools by design.
arXiv Detail & Related papers (2020-05-14T23:56:57Z) - The Visual Social Distancing Problem [99.69094590087408]
We introduce the Visual Social Distancing problem, defined as the automatic estimation of the inter-personal distance from an image.
We discuss how VSD relates with previous literature in Social Signal Processing and indicate which existing Computer Vision methods can be used to manage such problem.
arXiv Detail & Related papers (2020-05-11T00:04:34Z)
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.