Rules Of Engagement: Levelling Up To Combat Unethical CUI Design
- URL: http://arxiv.org/abs/2207.09282v1
- Date: Tue, 19 Jul 2022 14:02:24 GMT
- Title: Rules Of Engagement: Levelling Up To Combat Unethical CUI Design
- Authors: Thomas Mildner, Philip Doyle, Gian-Luca Savino, Rainer Malaka
- Abstract summary: We propose a simplified methodology to assess interfaces based on five dimensions taken from prior research on so-called dark patterns.
Our approach offers a numeric score to its users representing the manipulative nature of evaluated interfaces.
- Score: 23.01296770233131
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While a central goal of HCI has always been to create and develop interfaces
that are easy to use, a deeper focus has been set more recently on designing
interfaces more ethically. However, the exact meaning and measurement of
ethical design has yet to be established both within the CUI community and
among HCI researchers more broadly. In this provocation paper we propose a
simplified methodology to assess interfaces based on five dimensions taken from
prior research on so-called dark patterns. As a result, our approach offers a
numeric score to its users representing the manipulative nature of evaluated
interfaces. It is hoped that the approach - which draws a distinction between
persuasion and manipulative design, and focuses on how the latter functions
rather than how it manifests - will provide a viable way for quantifying
instances of unethical interface design that will prove useful to researchers,
regulators and potentially even users.
Related papers
- Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration [12.24579785420358]
Algorithmic Recourse aims to provide actionable explanations, or recourse plans, to overturn potentially unfavourable decisions taken by automated machine learning models.
We propose an interaction paradigm based on a guided interaction pattern aimed at both eliciting the users' preferences and heading them toward effective recourse interventions.
arXiv Detail & Related papers (2024-04-08T08:00:05Z) - Disentangled Interaction Representation for One-Stage Human-Object
Interaction Detection [70.96299509159981]
Human-Object Interaction (HOI) detection is a core task for human-centric image understanding.
Recent one-stage methods adopt a transformer decoder to collect image-wide cues that are useful for interaction prediction.
Traditional two-stage methods benefit significantly from their ability to compose interaction features in a disentangled and explainable manner.
arXiv Detail & Related papers (2023-12-04T08:02:59Z) - Exploring Predicate Visual Context in Detecting Human-Object
Interactions [44.937383506126274]
We study how best to re-introduce image features via cross-attention.
Our model with enhanced predicate visual context (PViC) outperforms state-of-the-art methods on the HICO-DET and V-COCO benchmarks.
arXiv Detail & Related papers (2023-08-11T15:57:45Z) - The HCI Aspects of Public Deployment of Research Chatbots: A User Study,
Design Recommendations, and Open Challenges [19.965388973809336]
We report on a mixed-methods user study conducted on a recent research chat.
We find that abstract anthropomorphic representation for the agent has a significant effect on user's perception, that offering AI explainability may have an impact on feedback rates, and that two (diegetic and extradiegetic) levels of the chat experience should be intentionally designed.
arXiv Detail & Related papers (2023-06-07T20:24:43Z) - A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented,
Temporal and Depth-aware design [77.34726150561087]
We conduct a survey on the most relevant and recent advances in Deep Semantic in the context of vision for autonomous vehicles.
Our main objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective.
arXiv Detail & Related papers (2023-03-08T01:29:55Z) - Interactive introduction to self-calibrating interfaces [4.111899441919164]
This paper aims to provide an intuitive understanding of the self-calibrating interface paradigm.
Under this paradigm, you can choose how to use an interface which can adapt to your preferences on the fly.
We introduce a PIN entering task and gradually release constraints, moving from a pre-calibrated interface to a self-calibrating interface.
arXiv Detail & Related papers (2022-12-12T08:39:30Z) - Knowledge Guided Bidirectional Attention Network for Human-Object
Interaction Detection [3.0915392100355192]
We argue that the independent use of the bottom-up parsing strategy in HOI is counter-intuitive and could lead to the diffusion of attention.
We introduce a novel knowledge-guided top-down attention into HOI, and propose to model the relation parsing as a "look and search" process.
We implement the process via unifying the bottom-up and top-down attention in a single encoder-decoder based model.
arXiv Detail & Related papers (2022-07-16T16:42:49Z) - ConsNet: Learning Consistency Graph for Zero-Shot Human-Object
Interaction Detection [101.56529337489417]
We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images.
We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs.
Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
arXiv Detail & Related papers (2020-08-14T09:11:18Z) - A Graph-based Interactive Reasoning for Human-Object Interaction
Detection [71.50535113279551]
We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs.
We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet.
Our framework is end-to-end trainable and free from costly annotations like human pose.
arXiv Detail & Related papers (2020-07-14T09:29:03Z) - Cascaded Human-Object Interaction Recognition [175.60439054047043]
We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding.
At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network.
With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
arXiv Detail & Related papers (2020-03-09T17:05:04Z)
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.