Social Responsibility of Algorithms
- URL: http://arxiv.org/abs/2012.03319v1
- Date: Sun, 6 Dec 2020 16:46:14 GMT
- Title: Social Responsibility of Algorithms
- Authors: Alexis Tsouki\`as
- Abstract summary: The paper makes a short overview of the scientific investigation around this topic, showing that the development, existence and use of such autonomous artifacts is much older than the recent interest in machine learning monopolised artificial intelligence.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Should we be concerned by the massive use of devices and algorithms which
automatically handle an increasing number of everyday activities within our
societies? The paper makes a short overview of the scientific investigation
around this topic, showing that the development, existence and use of such
autonomous artifacts is much older than the recent interest in machine learning
monopolised artificial intelligence. We then categorise the impact of using
such artifacts to the whole process of data collection, structuring,
manipulation as well as in recommendation and decision making. The suggested
framework allows to identify a number of challenges for the whole community of
decision analysts, both researchers and practitioners.
Related papers
- Artificial intelligence to automate the systematic review of scientific
literature [0.0]
We present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature.
We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies.
arXiv Detail & Related papers (2024-01-13T19:12:49Z) - On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
There have been severe concerns over the trustworthiness of AI technologies.
Machine and deep learning algorithms depend heavily on the data used during their development.
We propose a framework to evaluate the datasets through a responsible rubric.
arXiv Detail & Related papers (2023-10-24T14:01:53Z) - Designing Explainable Predictive Machine Learning Artifacts: Methodology
and Practical Demonstration [0.0]
Decision-makers from companies across various industries are still largely reluctant to employ applications based on modern machine learning algorithms.
We ascribe this issue to the widely held view on advanced machine learning algorithms as "black boxes"
We develop a methodology which unifies methodological knowledge from design science research and predictive analytics with state-of-the-art approaches to explainable artificial intelligence.
arXiv Detail & Related papers (2023-06-20T15:11:26Z) - Human-Centric Multimodal Machine Learning: Recent Advances and Testbed
on AI-based Recruitment [66.91538273487379]
There is a certain consensus about the need to develop AI applications with a Human-Centric approach.
Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes.
We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
arXiv Detail & Related papers (2023-02-13T16:44:44Z) - Proposing an Interactive Audit Pipeline for Visual Privacy Research [0.0]
We argue for the use of fairness to discover bias and fairness issues in systems, assert the need for a responsible human-over-the-loop, and reflect on the need to explore research agendas that have harmful societal impacts.
Our goal is to provide a systematic analysis of the machine learning pipeline for visual privacy and bias issues.
arXiv Detail & Related papers (2021-11-07T01:51:43Z) - Human-Robot Collaboration and Machine Learning: A Systematic Review of
Recent Research [69.48907856390834]
Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot.
This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
arXiv Detail & Related papers (2021-10-14T15:14:33Z) - AI Explainability 360: Impact and Design [120.95633114160688]
In 2019, we created AI Explainability 360 (Arya et al. 2020), an open source software toolkit featuring ten diverse and state-of-the-art explainability methods.
This paper examines the impact of the toolkit with several case studies, statistics, and community feedback.
The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.
arXiv Detail & Related papers (2021-09-24T19:17:09Z) - Individual Explanations in Machine Learning Models: A Survey for
Practitioners [69.02688684221265]
The use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise.
Many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways.
Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models.
arXiv Detail & Related papers (2021-04-09T01:46:34Z) - Knowledge as Invariance -- History and Perspectives of
Knowledge-augmented Machine Learning [69.99522650448213]
Research in machine learning is at a turning point.
Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks.
This white paper provides an introduction and discussion of this emerging field in machine learning research.
arXiv Detail & Related papers (2020-12-21T15:07:19Z) - Sensor Artificial Intelligence and its Application to Space Systems -- A
White Paper [35.78525324168878]
The goal of this white paper is to establish "Sensor AI" as a dedicated research topic.
A closer look at the sensors and their physical properties within AI approaches will lead to more robust and widely applicable algorithms.
Sensor AI will play a decisive role in autonomous driving as well as in areas of automated production, predictive maintenance or space research.
arXiv Detail & Related papers (2020-06-09T14:10:35Z)
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.