Algorithms as Social-Ecological-Technological Systems: an Environmental
Justice Lens on Algorithmic Audits
- URL: http://arxiv.org/abs/2305.05733v2
- Date: Tue, 23 May 2023 16:20:26 GMT
- Title: Algorithms as Social-Ecological-Technological Systems: an Environmental
Justice Lens on Algorithmic Audits
- Authors: Bogdana Rakova, Roel Dobbe
- Abstract summary: This paper reframes algorithmic systems as intimately connected to and part of social and ecological systems.
We propose a first-of-its-kind methodology for environmental justice-oriented algorithmic audits.
- Score: 0.5076419064097732
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper reframes algorithmic systems as intimately connected to and part
of social and ecological systems, and proposes a first-of-its-kind methodology
for environmental justice-oriented algorithmic audits. How do we consider
environmental and climate justice dimensions of the way algorithmic systems are
designed, developed, and deployed? These impacts are inherently emergent and
can only be understood and addressed at the level of relations between an
algorithmic system and the social (including institutional) and ecological
components of the broader ecosystem it operates in. As a result, we claim that
in absence of an integral ontology for algorithmic systems, we cannot do
justice to the emergent nature of broader environmental impacts of algorithmic
systems and their underlying computational infrastructure. We propose to define
algorithmic systems as ontologically indistinct from
Social-Ecological-Technological Systems (SETS), framing emergent implications
as couplings between social, ecological, and technical components of the
broader fabric in which algorithms are integrated and operate. We draw upon
prior work on SETS analysis as well as emerging themes in the literature and
practices of Environmental Justice (EJ) to conceptualize and assess algorithmic
impact. We then offer three policy recommendations to help establish a
SETS-based EJ approach to algorithmic audits: (1) broaden the inputs and
open-up the outputs of an audit, (2) enable meaningful access to redress, and
(3) guarantee a place-based and relational approach to the process of
evaluating impact. We operationalize these as a qualitative framework of
questions for a spectrum of stakeholders. Doing so, this article aims to
inspire stronger and more frequent interactions across policymakers,
researchers, practitioners, civil society, and grassroots communities.
Related papers
- Levels of AGI for Operationalizing Progress on the Path to AGI [64.59151650272477]
We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence (AGI) models and their precursors.
This framework introduces levels of AGI performance, generality, and autonomy, providing a common language to compare models, assess risks, and measure progress along the path to AGI.
arXiv Detail & Related papers (2023-11-04T17:44:58Z) - Predictable Artificial Intelligence [77.1127726638209]
This paper introduces the ideas and challenges of Predictable AI.
It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems.
We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
arXiv Detail & Related papers (2023-10-09T21:36:21Z) - Discovering General Reinforcement Learning Algorithms with Adversarial
Environment Design [54.39859618450935]
We show that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks.
Despite impressive initial results from algorithms such as Learned Policy Gradient (LPG), there remains a gap when these algorithms are applied to unseen environments.
In this work, we examine how characteristics of the meta-supervised-training distribution impact the performance of these algorithms.
arXiv Detail & Related papers (2023-10-04T12:52:56Z) - Inference and dynamic decision-making for deteriorating systems with
probabilistic dependencies through Bayesian networks and deep reinforcement
learning [0.0]
We propose an efficient algorithmic framework for inference and decision-making under uncertainty for engineering systems exposed to deteriorating environments.
In terms of policy optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC) reinforcement learning approach.
Results demonstrate that DDMAC policies offer substantial benefits when compared to state-of-the-art approaches.
arXiv Detail & Related papers (2022-09-02T14:45:40Z) - Outsider Oversight: Designing a Third Party Audit Ecosystem for AI
Governance [3.8997087223115634]
We discuss the challenges of third party oversight in the current AI landscape.
We show that the institutional design of such audits are far from monolithic.
We conclude that the turn toward audits alone is unlikely to achieve actual algorithmic accountability.
arXiv Detail & Related papers (2022-06-09T19:18:47Z) - 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) - A Sociotechnical View of Algorithmic Fairness [16.184328505946763]
Algorithmic fairness has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making.
We argue that fairness is an inherently social concept and that technologies for algorithmic fairness should therefore be approached through a sociotechnical lens.
arXiv Detail & Related papers (2021-09-27T21:17:16Z) - A Framework of High-Stakes Algorithmic Decision-Making for the Public
Sector Developed through a Case Study of Child-Welfare [3.739243122393041]
We develop a cohesive framework of algorithmic decision-making adapted for the public sector.
We conduct a case study of the algorithms in daily use within a child-welfare agency.
We propose guidelines for the design of high-stakes algorithmic decision-making tools in the public sector.
arXiv Detail & Related papers (2021-07-07T21:24:35Z) - Computability, Complexity, Consistency and Controllability: A Four C's
Framework for cross-disciplinary Ethical Algorithm Research [1.8275108630751844]
We set out a framework which we believe is useful for fostering cross-disciplinary understanding of pertinent issues in ethical algorithmic literature.
We provide examples of how insights from ethics, philosophy and population ethics are relevant to and translatable within sciences concerned with the study and design of algorithms.
arXiv Detail & Related papers (2021-01-30T17:03:22Z) - Investigating Bi-Level Optimization for Learning and Vision from a
Unified Perspective: A Survey and Beyond [114.39616146985001]
In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems contain a series of closely related subproblms.
In this paper, we first uniformly express these complex learning and vision problems from the perspective of Bi-Level Optimization (BLO)
Then we construct a value-function-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies.
arXiv Detail & Related papers (2021-01-27T16:20:23Z) - Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components.
There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed.
We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
arXiv Detail & Related papers (2020-07-13T20:30:26Z)
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.