Doubting AI Predictions: Influence-Driven Second Opinion Recommendation
Effective human-AI collaboration requires a system design that provides humans with meaningful ways to make sense of and critically evaluate algorithmic recommendations. In this paper, we propose a way to augment human-AI collaboration by building on a common organizational practice: identifying experts who are likely to provide complementary opinions. When machine learning algorithms are trained to predict human-generated assessments, experts' rich multitude of perspectives is frequently lost in monolithic algorithmic recommendations. The proposed approach aims to leverage productive disagreement by (1) identifying whether some experts are likely to disagree with an algorithmic assessment and, if so, (2) recommend an expert to request a second opinion from.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Detecting Stance in Media on Global Warming
Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, "Leading scientists agree that global warming is a serious concern," framing a clause which affi…
ArticlesEvidential positive opinion influence measures for viral marketing
The Viral Marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large casca…
MarketingSocial Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data
The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from vario…
Counterfactual Inference of Second Opinions
Automated decision support systems that are able to infer second opinions from experts can potentially facilitate a more efficient allocation of resources; they can help decide when and from whom to seek a second opinion…
counterfactualCounterfactual InferenceA Continuous Opinion Dynamic Model in Co-evolving Networks--A Novel Group Decision Approach
Opinion polarization is a ubiquitous phenomenon in opinion dynamics. In contrast to the traditional consensus oriented group decision making (GDM) framework, this paper proposes a framework with the co-evolution of both …
Decision Making