Approaching Unanticipated Consequences
- URL: http://arxiv.org/abs/2306.09959v1
- Date: Fri, 16 Jun 2023 16:43:52 GMT
- Title: Approaching Unanticipated Consequences
- Authors: Andrew Darby and Pete Sawyer and Nelly Bencomo
- Abstract summary: We explored how software that fulfils its requirements may have un-envisioned aftereffects with significant impacts.
We considered three real-world case studies and engaged with literature from several disciplines to develop a conceptual framework.
We found participant groups navigated the model with either a convergent or divergent intent.
The study demonstrated potential for the conceptual framework to be used as a tool with implications for research and practice.
- Score: 3.253495920474109
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In an ever-changing world, even software that fulfils its requirements may
have un-envisioned aftereffects with significant impacts. We explored how such
impacts can be better understood at the pre-design phase in support of
organisational preparedness. We considered three real-world case studies and
engaged with literature from several disciplines to develop a conceptual
framework. Across three workshops with industry practitioners and academics
creative strategies from speculative design practices were used to prompt
engagement with the framework. We found participant groups navigated the model
with either a convergent or divergent intent. The academics, operating in an
exploratory mode, came to a broad understanding of a class of technologies
through its impacts. Operating in an anticipatory mode the industry
practitioners came to a specific understanding of a technology's potential in
their workplace. The study demonstrated potential for the conceptual framework
to be used as a tool with implications for research and practice.
Related papers
- Explainability in AI Based Applications: A Framework for Comparing Different Techniques [2.5874041837241304]
In business applications, the challenge lies in selecting an appropriate explainability method that balances comprehensibility with accuracy.
This paper proposes a novel method for the assessment of the agreement of different explainability techniques.
By providing a practical framework for understanding the agreement of diverse explainability techniques, our research aims to facilitate the broader integration of interpretable AI systems in business applications.
arXiv Detail & Related papers (2024-10-28T09:45:34Z) - Diffusion-Based Visual Art Creation: A Survey and New Perspectives [51.522935314070416]
This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives.
Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation.
We aim to shed light on the mechanisms through which AI systems emulate and possibly, enhance human capacities in artistic perception and creativity.
arXiv Detail & Related papers (2024-08-22T04:49:50Z) - Towards a General Framework for Continual Learning with Pre-training [55.88910947643436]
We present a general framework for continual learning of sequentially arrived tasks with the use of pre-training.
We decompose its objective into three hierarchical components, including within-task prediction, task-identity inference, and task-adaptive prediction.
We propose an innovative approach to explicitly optimize these components with parameter-efficient fine-tuning (PEFT) techniques and representation statistics.
arXiv Detail & Related papers (2023-10-21T02:03:38Z) - The Participatory Turn in AI Design: Theoretical Foundations and the
Current State of Practice [64.29355073494125]
This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation.
We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
arXiv Detail & Related papers (2023-10-02T05:30:42Z) - 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) - Active Inference in Robotics and Artificial Agents: Survey and
Challenges [51.29077770446286]
We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning.
We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
arXiv Detail & Related papers (2021-12-03T12:10:26Z) - Holistically Placing the ICT Artefact in Capability Approach [0.0]
This paper proposes a framework that holistically places the Information and Communication Technology (ICT) Artefact in Capability Approach (CA)
The framework harmonises the different conceptualisations of technology within CA-based frameworks in ICT4D.
arXiv Detail & Related papers (2021-08-22T16:49:20Z) - Mind the Gap: A Framework (BehaveFIT) Guiding The Use of Immersive
Technologies in Behavior Change Processes [0.0]
The Behavioral Framework for immersive Technologies (BehaveFIT) presents an intelligible categorization and condensation of psychological barriers and immersive features.
These three steps explain how BehaveFIT can be used, and include guiding questions and one example for each step.
arXiv Detail & Related papers (2020-12-20T12:48:01Z) - Forecasting: theory and practice [65.71277206849244]
This article provides a non-systematic review of the theory and the practice of forecasting.
We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches.
We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts.
arXiv Detail & Related papers (2020-12-04T16:56:44Z) - Behavior Priors for Efficient Reinforcement Learning [97.81587970962232]
We consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors.
We discuss how such latent variable formulations connect to related work on hierarchical reinforcement learning (HRL) and mutual information and curiosity based objectives.
We demonstrate the effectiveness of our framework by applying it to a range of simulated continuous control domains.
arXiv Detail & Related papers (2020-10-27T13:17:18Z)
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.