62 research outputs found
Does Explainable AI Have Moral Value?
Explainable AI (XAI) aims to bridge the gap between complex algorithmic
systems and human stakeholders. Current discourse often examines XAI in
isolation as either a technological tool, user interface, or policy mechanism.
This paper proposes a unifying ethical framework grounded in moral duties and
the concept of reciprocity. We argue that XAI should be appreciated not merely
as a right, but as part of our moral duties that helps sustain a reciprocal
relationship between humans affected by AI systems. This is because, we argue,
explanations help sustain constitutive symmetry and agency in AI-led
decision-making processes. We then assess leading XAI communities and reveal
gaps between the ideal of reciprocity and practical feasibility. Machine
learning offers useful techniques but overlooks evaluation and adoption
challenges. Human-computer interaction provides preliminary insights but
oversimplifies organizational contexts. Policies espouse accountability but
lack technical nuance. Synthesizing these views exposes barriers to
implementable, ethical XAI. Still, positioning XAI as a moral duty transcends
rights-based discourse to capture a more robust and complete moral picture.
This paper provides an accessible, detailed analysis elucidating the moral
value of explainability.Comment: Preprint. Work in progress. Accepted at the workshop MP2 at NeurIPS
2023, 15 December 2023, New Orleans, U
PathologyGAN: Learning deep representations of cancer tissue
We apply Generative Adversarial Networks (GANs) to the domain of digital
pathology. Current machine learning research for digital pathology focuses on
diagnosis, but we suggest a different approach and advocate that generative
models could drive forward the understanding of morphological characteristics
of cancer tissue. In this paper, we develop a framework which allows GANs to
capture key tissue features and uses these characteristics to give structure to
its latent space. To this end, we trained our model on 249K H&E breast cancer
tissue images, extracted from 576 TMA images of patients from the Netherlands
Cancer Institute (NKI) and Vancouver General Hospital (VGH) cohorts. We show
that our model generates high quality images, with a Frechet Inception Distance
(FID) of 16.65. We further assess the quality of the images with cancer tissue
characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using
quantitative information to calculate the FID and showing consistent
performance of 9.86. Additionally, the latent space of our model shows an
interpretable structure and allows semantic vector operations that translate
into tissue feature transformations. Furthermore, ratings from two expert
pathologists found no significant difference between our generated tissue
images from real ones. The code, generated images, and pretrained model are
available at https://github.com/AdalbertoCq/Pathology-GANComment: MIDL 2020 final versio
An Algorithmic Framework for Fairness Elicitation
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders. We introduce a framework in which pairs of individuals can be identified as requiring (approximately) equal treatment under a learned model, or requiring ordered treatment such as "applicant Alice should be at least as likely to receive a loan as applicant Bob". We provide a provably convergent and oracle efficient algorithm for learning the most accurate model subject to the elicited fairness constraints, and prove generalization bounds for both accuracy and fairness. This algorithm can also combine the elicited constraints with traditional statistical fairness notions, thus "correcting" or modifying the latter by the former. We report preliminary findings of a behavioral study of our framework using human-subject fairness constraints elicited on the COMPAS criminal recidivism dataset
Concurrent Speech Synthesis to Improve Document First Glance for the Blind
International audienceSkimming and scanning are two well-known reading processes, which are combined to access the document content as quickly and efficiently as possible. While both are available in visual reading mode, it is rather difficult to use them in non visual environments because they mainly rely on typographical and layout properties. In this article, we introduce the concept of tag thunder as a way (1) to achieve the oral transposition of the web 2.0 concept of tag cloud and (2) to produce an innovative interactive stimulus to observe the emergence of self-adapted strategies for non-visual skimming of written texts. We first present our general and theoretical approach to the problem of both fast, global and non-visual access to web browsing; then we detail the progress of development and evaluation of the various components that make up our software architecture. We start from the hypothesis that the semantics of the visual architecture of web pages can be transposed into new sensory modalities thanks to three main steps (web page segmentation, keywords extraction and sound spatialization). We note the difficulty of simultaneously (1) evaluating a modular system as a whole at the end of the processing chain and (2) identifying at the level of each software brick the exact origin of its limits; despite this issue, the results of the first evaluation campaign seem promising
LakeBench: Benchmarks for Data Discovery over Data Lakes
Within enterprises, there is a growing need to intelligently navigate data
lakes, specifically focusing on data discovery. Of particular importance to
enterprises is the ability to find related tables in data repositories. These
tables can be unionable, joinable, or subsets of each other. There is a dearth
of benchmarks for these tasks in the public domain, with related work targeting
private datasets. In LakeBench, we develop multiple benchmarks for these tasks
by using the tables that are drawn from a diverse set of data sources such as
government data from CKAN, Socrata, and the European Central Bank. We compare
the performance of 4 publicly available tabular foundational models on these
tasks. None of the existing models had been trained on the data discovery tasks
that we developed for this benchmark; not surprisingly, their performance shows
significant room for improvement. The results suggest that the establishment of
such benchmarks may be useful to the community to build tabular models usable
for data discovery in data lakes
Grammar Filtering For Syntax-Guided Synthesis
Programming-by-example (PBE) is a synthesis paradigm that allows users to
generate functions by simply providing input-output examples. While a promising
interaction paradigm, synthesis is still too slow for realtime interaction and
more widespread adoption. Existing approaches to PBE synthesis have used
automated reasoning tools, such as SMT solvers, as well as works applying
machine learning techniques. At its core, the automated reasoning approach
relies on highly domain specific knowledge of programming languages. On the
other hand, the machine learning approaches utilize the fact that when working
with program code, it is possible to generate arbitrarily large training
datasets. In this work, we propose a system for using machine learning in
tandem with automated reasoning techniques to solve Syntax Guided Synthesis
(SyGuS) style PBE problems. By preprocessing SyGuS PBE problems with a neural
network, we can use a data driven approach to reduce the size of the search
space, then allow automated reasoning-based solvers to more quickly find a
solution analytically. Our system is able to run atop existing SyGuS PBE
synthesis tools, decreasing the runtime of the winner of the 2019 SyGuS
Competition for the PBE Strings track by 47.65% to outperform all of the
competing tools
A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
People increasingly use videos on the Web as a source for learning. To
support this way of learning, researchers and developers are continuously
developing tools, proposing guidelines, analyzing data, and conducting
experiments. However, it is still not clear what characteristics a video should
have to be an effective learning medium. In this paper, we present a
comprehensive review of 257 articles on video-based learning for the period
from 2016 to 2021. One of the aims of the review is to identify the video
characteristics that have been explored by previous work. Based on our
analysis, we suggest a taxonomy which organizes the video characteristics and
contextual aspects into eight categories: (1) audio features, (2) visual
features, (3) textual features, (4) instructor behavior, (5) learners
activities, (6) interactive features (quizzes, etc.), (7) production style, and
(8) instructional design. Also, we identify four representative research
directions: (1) proposals of tools to support video-based learning, (2) studies
with controlled experiments, (3) data analysis studies, and (4) proposals of
design guidelines for learning videos. We find that the most explored
characteristics are textual features followed by visual features, learner
activities, and interactive features. Text of transcripts, video frames, and
images (figures and illustrations) are most frequently used by tools that
support learning through videos. The learner activity is heavily explored
through log files in data analysis studies, and interactive features have been
frequently scrutinized in controlled experiments. We complement our review by
contrasting research findings that investigate the impact of video
characteristics on the learning effectiveness, report on tasks and technologies
used to develop tools that support learning, and summarize trends of design
guidelines to produce learning video
Improving accountability in recommender systems research through reproducibility
Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating reproducibility of recommender systems experimentation, we indirectly address the issues of accountability and transparency in recommender systems research from the perspectives of practitioners, designers, and engineers aiming to assess the capabilities of published research works. These issues have become increasingly prevalent in recent literature. Reasons for this include societal movements around intelligent systems and artificial intelligence striving toward fair and objective use of human behavioral data (as in Machine Learning, Information Retrieval, or Human–Computer Interaction). Society has grown to expect explanations and transparency standards regarding the underlying algorithms making automated decisions for and around us. This work surveys existing definitions of these concepts and proposes a coherent terminology for recommender systems research, with the goal to connect reproducibility to accountability. We achieve this by introducing several guidelines and steps that lead to reproducible and, hence, accountable experimental workflows and research. We additionally analyze several instantiations of recommender system implementations available in the literature and discuss the extent to which they fit in the introduced framework. With this work, we aim to shed light on this important problem and facilitate progress in the field by increasing the accountability of researchThis work has been funded by the Ministerio de Ciencia, Innovación y Universidades (reference: PID2019-108965GB-I00
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