803 research outputs found
LIMEADE: A General Framework for Explanation-Based Human Tuning of Opaque Machine Learners
Research in human-centered AI has shown the benefits of systems that can
explain their predictions. Methods that allow humans to tune a model in
response to the explanations are similarly useful. While both capabilities are
well-developed for transparent learning models (e.g., linear models and GA2Ms),
and recent techniques (e.g., LIME and SHAP) can generate explanations for
opaque models, no method for tuning opaque models in response to explanations
has been user-tested to date. This paper introduces LIMEADE, a general
framework for tuning an arbitrary machine learning model based on an
explanation of the model's prediction. We demonstrate the generality of our
approach with two case studies. First, we successfully utilize LIMEADE for the
human tuning of opaque image classifiers. Second, we apply our framework to a
neural recommender system for scientific papers on a public website and report
on a user study showing that our framework leads to significantly higher
perceived user control, trust, and satisfaction. Analyzing 300 user logs from
our publicly-deployed website, we uncover a tradeoff between canonical greedy
explanations and diverse explanations that better facilitate human tuning.Comment: 16 pages, 7 figure
Editable User Profiles for Controllable Text Recommendation
Methods for making high-quality recommendations often rely on learning latent
representations from interaction data. These methods, while performant, do not
provide ready mechanisms for users to control the recommendation they receive.
Our work tackles this problem by proposing LACE, a novel concept value
bottleneck model for controllable text recommendations. LACE represents each
user with a succinct set of human-readable concepts through retrieval given
user-interacted documents and learns personalized representations of the
concepts based on user documents. This concept based user profile is then
leveraged to make recommendations. The design of our model affords control over
the recommendations through a number of intuitive interactions with a
transparent user profile. We first establish the quality of recommendations
obtained from LACE in an offline evaluation on three recommendation tasks
spanning six datasets in warm-start, cold-start, and zero-shot setups. Next, we
validate the controllability of LACE under simulated user interactions.
Finally, we implement LACE in an interactive controllable recommender system
and conduct a user study to demonstrate that users are able to improve the
quality of recommendations they receive through interactions with an editable
user profile.Comment: Accepted to SIGIR 2023; Pre-print, camera-ready to follo
From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences
We describe the state-of-the-art in performance modeling and prediction for Information Retrieval
(IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its
shortcomings and strengths. We present a framework for further research, identifying five major
problem areas: understanding measures, performance analysis, making underlying assumptions
explicit, identifying application features determining performance, and the development of prediction
models describing the relationship between assumptions, features and resulting performanc
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
The rise of powerful large language models (LLMs) brings about tremendous
opportunities for innovation but also looming risks for individuals and society
at large. We have reached a pivotal moment for ensuring that LLMs and
LLM-infused applications are developed and deployed responsibly. However, a
central pillar of responsible AI -- transparency -- is largely missing from the
current discourse around LLMs. It is paramount to pursue new approaches to
provide transparency for LLMs, and years of research at the intersection of AI
and human-computer interaction (HCI) highlight that we must do so with a
human-centered perspective: Transparency is fundamentally about supporting
appropriate human understanding, and this understanding is sought by different
stakeholders with different goals in different contexts. In this new era of
LLMs, we must develop and design approaches to transparency by considering the
needs of stakeholders in the emerging LLM ecosystem, the novel types of
LLM-infused applications being built, and the new usage patterns and challenges
around LLMs, all while building on lessons learned about how people process,
interact with, and make use of information. We reflect on the unique challenges
that arise in providing transparency for LLMs, along with lessons learned from
HCI and responsible AI research that has taken a human-centered perspective on
AI transparency. We then lay out four common approaches that the community has
taken to achieve transparency -- model reporting, publishing evaluation
results, providing explanations, and communicating uncertainty -- and call out
open questions around how these approaches may or may not be applied to LLMs.
We hope this provides a starting point for discussion and a useful roadmap for
future research
An introduction to explainable artificial intelligence with LIME and SHAP
Treballs Finals de Grau de Matemà tiques, Facultat de Matemà tiques, Universitat de Barcelona, Any: 2022, Director: Albert Clapés i Sintes i Sergio Escalera Guerrero[en] Artificial intelligence (AI) and more specifically machine learning (ML) have shown their potential by approaching or even exceeding human levels of accuracy for a variety of real-world problems. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, creating a tradeoff between accuracy and interpretability. These models are known for being "black box"
and opaque, which is especially problematic in industries like healthcare. Therefore, understanding the reasons behind predictions is crucial in establishing trust, which is fundamental if one plans to take action based on a prediction, or when deciding whether or not to implement a new model. Here is where explainable artificial intelligence (XAI) comes in by helping humans to comprehend and trust the results and output created by a machine learning model. This project is organised in 3 chapters with the aim of introducing the reader to the field of explainable artificial intelligence. Machine learning and some related concepts are introduced in the first chapter. The second chapter focuses on the theory of the random forest model in detail. Finally, in the third
chapter, the theory behind two contemporary and influential XAI methods, LIME and SHAP, is formalised. Additionally, a public diabetes tabular dataset is used to illustrate an application of these two methods in the medical sector. The project concludes with a discussion of its possible future works
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed
appropriately, may deliver the best of expectations over many application sectors across the field. For this
to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability,
an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural
Networks) that were not present in the last hype of AI (namely, expert systems and rule based models).
Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely
acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in
this article examines the existing literature and contributions already done in the field of XAI, including a
prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define
explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that
covers such prior conceptual propositions with a major focus on the audience for which the explainability
is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions
related to the explainability of different Machine Learning models, including those aimed at explaining
Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This
critical literature analysis serves as the motivating background for a series of challenges faced by XAI,
such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept
of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI
methods in real organizations with fairness, model explainability and accountability at its core. Our
ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve
as reference material in order to stimulate future research advances, but also to encourage experts and
professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any
prior bias for its lack of interpretability.Basque GovernmentConsolidated Research Group MATHMODE - Department of Education of the Basque Government IT1294-19Spanish GovernmentEuropean Commission TIN2017-89517-PBBVA Foundation through its Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2018 call (DeepSCOP project)European Commission 82561
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