Exploring the Nuances of Designing (with/for) Artificial Intelligence
- URL: http://arxiv.org/abs/2010.15578v1
- Date: Thu, 22 Oct 2020 20:34:35 GMT
- Title: Exploring the Nuances of Designing (with/for) Artificial Intelligence
- Authors: Niya Stoimenova, Rebecca Price
- Abstract summary: We explore the construct of infrastructure as a means to simultaneously address algorithmic and societal issues when designing AI.
Neither algorithmic solutions, nor purely humanistic ones will be enough to fully undesirable outcomes in the narrow state of AI.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Solutions relying on artificial intelligence are devised to predict data
patterns and answer questions that are clearly defined, involve an enumerable
set of solutions, clear rules, and inherently binary decision mechanisms. Yet,
as they become exponentially implemented in our daily activities, they begin to
transcend these initial boundaries and to affect the larger sociotechnical
system in which they are situated. In this arrangement, a solution is under
pressure to surpass true or false criteria and move to an ethical evaluation of
right and wrong. Neither algorithmic solutions, nor purely humanistic ones will
be enough to fully mitigate undesirable outcomes in the narrow state of AI or
its future incarnations. We must take a holistic view. In this paper we explore
the construct of infrastructure as a means to simultaneously address
algorithmic and societal issues when designing AI.
Related papers
- Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act)
Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence.
As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
arXiv Detail & Related papers (2024-10-10T17:38:38Z) - XXAI: Towards eXplicitly eXplainable Artificial Intelligence [0.0]
There are concerns about the reliability and safety of artificial intelligence based on sub-symbolic neural networks.
symbolic AI has the nature of a white box and is able to ensure the reliability and safety of its decisions.
We propose eXplicitly eXplainable AI (XXAI) - a fully transparent white-box AI based on deterministic logical cellular automata.
arXiv Detail & Related papers (2024-01-05T23:50:10Z) - Trust, Accountability, and Autonomy in Knowledge Graph-based AI for
Self-determination [1.4305544869388402]
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making.
The integration of KGs with neuronal learning is currently a topic of active research.
This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination.
arXiv Detail & Related papers (2023-10-30T12:51:52Z) - 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) - Inherent Limitations of AI Fairness [16.588468396705366]
The study of AI fairness has rapidly developed into a rich field of research with links to computer science, social science, law, and philosophy.
Many technical solutions for measuring and achieving AI fairness have been proposed, yet their approach has been criticized in recent years for being misleading, unrealistic and harmful.
arXiv Detail & Related papers (2022-12-13T11:23:24Z) - Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps.
We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches.
We explore this process with 43 AI practitioners and real end-users.
arXiv Detail & Related papers (2022-11-12T21:54:05Z) - 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) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Socially Responsible AI Algorithms: Issues, Purposes, and Challenges [31.382000425295885]
Technologists and AI researchers have a responsibility to develop trustworthy AI systems.
To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness.
arXiv Detail & Related papers (2021-01-01T17:34:42Z) - Bias in Multimodal AI: Testbed for Fair Automatic Recruitment [73.85525896663371]
We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases.
Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.
arXiv Detail & Related papers (2020-04-15T15:58:05Z) - Bias in Data-driven AI Systems -- An Introductory Survey [37.34717604783343]
This survey focuses on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful Machine Learning (ML) algorithms.
If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features like race, sex, etc.
arXiv Detail & Related papers (2020-01-14T09:39:09Z)
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.