Alexandra Olteanu

Emily Sheng

Dan Vann

Hanna Wallach

It is critical to measure and mitigate fairness-related harms caused by AI text generation systems, including stereotyping and demeaning har… (see more)ms. To that end, we introduce FairPrism, a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering a diverse set of harms relating to gender and sexuality. FairPrism aims to address several limitations of existing datasets for measuring and mitigating fairness-related harms, including improved transparency, clearer specification of dataset coverage, and accounting for annotator disagreement and harms that are context-dependent. FairPrism’s annotations include the extent of stereotyping and demeaning harms, the demographic groups targeted, and appropriateness for different applications. The annotations also include specific harms that occur in interactive contexts and harms that raise normative concerns when the “speaker” is an AI system. Due to its precision and granularity, FairPrism can be used to diagnose (1) the types of fairness-related harms that AI text generation systems cause, and (2) the potential limitations of mitigation methods, both of which we illustrate through case studies. Finally, the process we followed to develop FairPrism offers a recipe for building improved datasets for measuring and mitigating harms caused by AI systems.

2023-07-01

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)

AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms

Zana Buçinca

Chau Minh Pham

Maurice Jakesch

Marco Túlio Ribeiro

Saleema Amershi

While demands for change and accountability for harmful AI consequences mount, foreseeing the downstream effects of deploying AI systems rem… (see more)ains a challenging task. We developed AHA! (Anticipating Harms of AI), a generative framework to assist AI practitioners and decision-makers in anticipating potential harms and unintended consequences of AI systems prior to development or deployment. Given an AI deployment scenario, AHA! generates descriptions of possible harms for different stakeholders. To do so, AHA! systematically considers the interplay between common problematic AI behaviors as well as their potential impacts on different stakeholders, and narrates these conditions through vignettes. These vignettes are then filled in with descriptions of possible harms by prompting crowd workers and large language models. By examining 4113 harms surfaced by AHA! for five different AI deployment scenarios, we found that AHA! generates meaningful examples of harms, with different problematic AI behaviors resulting in different types of harms. Prompting both crowds and a large language model with the vignettes resulted in more diverse examples of harms than those generated by either the crowd or the model alone. To gauge AHA!'s potential practical utility, we also conducted semi-structured interviews with responsible AI professionals (N=9). Participants found AHA!'s systematic approach to surfacing harms important for ethical reflection and discovered meaningful stakeholders and harms they believed they would not have thought of otherwise. Participants, however, differed in their opinions about whether AHA! should be used upfront or as a secondary-check and noted that AHA! may shift harm anticipation from an ideation problem to a potentially demanding review problem. Drawing on our results, we discuss design implications of building tools to help practitioners envision possible harms.

2023-06-05

ArXiv (preprint)

Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?

Shreya Chowdhary

Anna Kawakami

Jina Suh

Mary L Gray

Koustuv Saha

2023-01-01

FAccT (published)

Human-Centered Responsible Artificial Intelligence: Current & Future Trends

Mohammad Tahaei

Marios Constantinides

Daniele Quercia

Sean Kennedy

Michael Muller

Simone Stumpf

Q. Vera Liao

Ricardo Baeza-Yates

Lora Aroyo

Jess Holbrook

Ewa Luger

Michael Madaio

Ilana Golbin Blumenfeld

Maria De-Arteaga

Jessica Vitak

2023-01-01

CHI Extended Abstracts (published)

Responsible AI Considerations in Text Summarization Research: A Review of Current Practices

Yu Lu Liu

Meng Cao

Su Lin Blodgett

Jackie Cheung

Adam Trischler

2023-01-01

EMNLP (Findings) (published)

openreview.net

The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources

Akshatha Arodi

Martin Pömsl

Kaheer Suleman

Adam Trischler

Jackie Cheung

Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make in… (see more)ferences using information from multiple sources. An important class of such inferences are those that require both background knowledge, presumably contained in a model’s pretrained parameters, and instance-specific information that is supplied at inference time. However, the integration and reasoning abilities of NLU models in the presence of multiple knowledge sources have been largely understudied. In this work, we propose a test suite of coreference resolution subtasks that require reasoning over multiple facts. These subtasks differ in terms of which knowledge sources contain the relevant facts. We also introduce subtasks where knowledge is present only at inference time using fictional knowledge. We evaluate state-of-the-art coreference resolution models on our dataset. Our results indicate that several models struggle to reason on-the-fly over knowledge observed both at pretrain time and at inference time. However, with task-specific training, a subset of models demonstrates the ability to integrate certain knowledge types from multiple sources. Still, even the best performing models seem to have difficulties with reliably integrating knowledge presented only at inference time.

2022-12-15

ArXiv (preprint)

ADEPT: An Adjective-Dependent Plausibility Task

Ali Emami

Ian Porada

Kaheer Suleman

Adam Trischler

Jackie Cheung

2021-01-01

Annual Meeting of the Association for Computational Linguistics (published)