Search Results for author: David Piorkowski

Found 8 papers, 0 papers with code

Quantitative AI Risk Assessments: Opportunities and Challenges

no code implementations13 Sep 2022 David Piorkowski, Michael Hind, John Richards

Although AI-based systems are increasingly being leveraged to provide value to organizations, individuals, and society, significant attendant risks have been identified.

Evaluating a Methodology for Increasing AI Transparency: A Case Study

no code implementations24 Jan 2022 David Piorkowski, John Richards, Michael Hind

The methodology was found to be usable by developers not trained in user-centered techniques, guiding them to creating a documentation template that addressed the specific needs of their consumers while still being reusable across different models and use cases.

Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models

no code implementations29 Jan 2021 Soya Park, April Wang, Ban Kawas, Q. Vera Liao, David Piorkowski, Marina Danilevsky

Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models.

How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study

no code implementations13 Jan 2021 David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, Felix Portnoy

The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team.

Towards evaluating and eliciting high-quality documentation for intelligent systems

no code implementations17 Nov 2020 David Piorkowski, Daniel González, John Richards, Stephanie Houde

In this paper, we propose and evaluate a set of quality dimensions to identify in what ways this type of documentation falls short.

Vocal Bursts Intensity Prediction

A Methodology for Creating AI FactSheets

no code implementations24 Jun 2020 John Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilović

This is the first work to describe a methodology for creating the form of AI documentation we call FactSheets.

Detecting Egregious Conversations between Customers and Virtual Agents

no code implementations NAACL 2018 Tommy Sandbank, Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, John Richards, David Piorkowski

In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction.

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