DataPerf: Benchmarks for Data-Centric AI Development
Authors: Mark Mazumder
Colby Banbury
Xiaozhe Yao
Bojan Karlaš
William A Gaviria Rojas
Sudnya Diamos
Greg Diamos
Lynn He
Alicia Parrish
Hannah Rose Kirk
Jessica Quaye
Charvi Rastogi
Douwe Kiela
David Jurado
David Kanter
Rafael Mosquera
Will Cukierski
Juan Ciro
Lora Aroyo
Bilge Acun
Lingjiao Chen
Mehul Smriti Raje
Max Bartolo
Sabri Eyuboglu
Amirata Ghorbani
Emmett Daniel Goodman
Addison Howard
Oana Inel
Tariq Kane
Christine Kirkpatrick
D. Sculley
Tzu-Sheng Kuo
Jonas Mueller
Tristan Thrush
Joaquin Vanschoren
Margaret Warren
Adina Williams
Serena Yeung
Newsha Ardalani
Praveen Paritosh
Ce Zhang
James Y. Zou
Carole-Jean Wu
Cody Coleman
Andrew Ng
Peter Mattson
Vijay Janapa Reddi
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: datasets
datacentric
vision
speech
acquisition
debugging
prompting
TL;DR: DataPerf is an open-source, extensible benchmark suite for data-centric AI

Abstract: Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamental importance of data has given rise to inaccuracy, bias, and fragility in real-world applications, and research is hindered by saturation across existing dataset benchmarks. In response, we present DataPerf, a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We aim to foster innovation in data-centric AI through competition, comparability, and reproducibility. We enable the ML community to iterate on datasets, instead of just architectures, and we provide an open, online platform with multiple rounds of challenges to support this iterative development. The first iteration of DataPerf contains five benchmarks covering a wide spectrum of data-centric techniques, tasks, and modalities in vision, speech, acquisition, debugging, and diffusion prompting, and we support hosting new contributed benchmarks from the community. The benchmarks, online evaluation platform, and baseline implementations are open source, and the MLCommons Association will maintain DataPerf to ensure long-term benefits to academia and industry.

DataPerf: Benchmarks for Data-Centric AI Development
Mark Mazumder', Colby Banbury', Xiaozhe Yao², Bojan Karlaš², William Gaviria Rojas?,
Sudnya Diamos Greg Diamos', Lynn He?, Alicia Parrish 8, Hannah Rose Kirk Jessica Quaye',
Charvi Rastogi Douwe Kiela9.11 David Jurado 9.20 David Kanter Rafael Mosquera 6.20
Juan 6.20 Lora Aroyo R Bilge Acun', Lingjiao Chen'. Mehul Smriti Raje'. Max Bartolo 16,19 Sabri Eyuboglu', Amirata Ghorbani", Emmett Goodman', Oana Inel Tariq Kane Christine R. Kirkpatrick Tzu-Sheng Kuo",, Jonas Mueller Tristan Thrush". Joaquin Vanschoren Margaret Warren 14 Adina Williams', Serena Yeung", Newsha Ardalani? Praveen Paritosh", Ce Zhang", James Zou?, Carole-Jean Wu?, Cody Coleman', Andrew Ng459, Peter Mattson", and Vijay Janapa Reddi!
Harvard University, 'ETH Zurich, 'Coactive.AI, "Landing AI, "DeepLcarning.A.. MMCommons, Meta. GGooge,, "Stanford University, San Diego Supercomputer Center, UC San Diego, l1 Carnnege Mellon University, Cleeanla,, EiEndhoven University of Technology, 14 Institute for Human and Machine Cognition, Kaagge,, 'Cohere, Univvrritty of Oxford, 18 University of Zurich, University College London, Factored, 21 Conttextul AI
Abstract
Machine learning research has long focused models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamen- tal importance data has given rise to inaccuracy, bias, and fragility in real-world applications, and research is hindered by saturation across existing dataset bench- marks. In response, we present DataPerf, a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We aim to foster innovation in data-centric AI through competition, comparability, and reproducibility. We enable the ML community iterate on datasets, instead of just architectures, and we provide an open, online platform with multiple rounds of challenges to support this iterative development. The first iteration of DataPerf contains five benchmarks covering a wide spectrum of data-centric techniques, tasks, and modalities in vi- sion, speech, acquisition, debugging, and diffusion prompting, and we support hosting new contributed benchmarks from the community. The benchmarks, on- line evaluation platform, and baseline implementations are open source, and the MLCommons Association will maintain DataPerf to ensure long-term benefits to academia and industry.
Introduction
Machine learning research has overwhelmingly focused on improving models rather than on im- proving datasets. Large public datasets such ImageNet [14]. Freebase [7]. Switchboard (22), and SQuAD 44 serve as compasses for benchmarking model performance. Consequently, researchers eagerly adopt the largest existing dataset without fully considering its breadth, difficulty and fidelity to the underlying problem. Critically, better data quality 2 is increasingly necessary to improve generalization, avoid bias, and aid safety in data cascades [8]] Without high-quality training data models can exhibit performance discrepancies lcading to reduced accuracy and persistent fairness
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Bench- marks.

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Data-Centric Operations
Training
Data Quality Assessment
Data Parsing
Training Data
ML Model
New Train Set
Data Augmentation
Data Acquisition
Testing Data
Data Debugging
Data Selection
Data Cleaning
Set
Other Sources
Data-Centric Iterations
Typical benchmarks model-centric, and therefore focus the model design and train- ing stages of the ML pipeline (shown in orange). However, to develop high-quality ML applications, users often employ collection data-centric operations improve data quality and repeated data- centric iterations to refine these operations. DataPerf aims to benchmark all major stages of such data-centric pipeline (shown in green) improve ML data quality.
Figure 1:
issues 9 15 37 once they leave the lab to enter service. In conventional model-centric ML, the term benchmark often means standard, fixed dataset for model accuracy comparisons and per-
formance measurements. While this paradigm has been useful for advancing model design, these benchmarks
saturating (attaining perfect above "human-level" performance) [26). This raises two questions: First, is ML research making real progress on the underlying capabilities, or it just overfitting existing benchmark datasets or suffering from data artifacts? A growing body
of literature explores the evidence supporting benchmark limitations (7 24] 43
53
47
5
55
Second, how should benchmarks evolve to push the frontier of MI. research?
In response to these concerning trends, we introduce DataPerf, data-centric benchmark suite that introduces competition to the field of dataset improvement. We survey suite of complex data- centric development pipelines across multiple ML domains and isolate a subset concrete tasks that we believe representative of current bottlenecks, as illustrated in Figure [ We freeze model architectures, training hyperparameters, and task metrics to compare solutions strictly via relative improvements from changes to the datasets themselves.
Our contributions are as follows:
We have developed comprehensive suite of novel data-centric benchmarks covering wide range of tasks. These tasks encompass training set selection for speech and vision, data cleaning and debugging, data acquisition, and diffusion model prompting.
Each benchmark specifies a data-centric task based on real-world use case rationale. We provide rules for submissions, along with evaluation scripts, and baseline submission for each benchmark task.
We provide extensible and open-source platform for hosting data-centric benchmarks, allowing other organizations and researchers to propose new henchmarks for inclusion in the DataPerf suite, and host data challenges themselves.
Critically, DataPerf is not one-off competition. We have established the DataPerf Working Group, which operates under the MLCommons Association. This working group is responsible for the ongoing maintenance of the benchmarks and platform, as well as for fostering the development of data-centric research and methodologies both academic and industrial domains. The aim is to ensure the long-term sustainability and growth of DataPerf beyond single competition.
The remainder the paper organized as follows. In Section 2.1 we review lessons learned from an exploratory data-centric challenge. Section 2.2 details the hosting platform we developed in response and Section 2.3 presents the DataPerf suite of five novel benchmarks and challenges. We conclude with survey of related efforts (Section and future directions (Section

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2
DataPerf Benchmarking Suite
We describe the initial challenge which inspired the suite of DataPerf benchmarks and identified which features are needed for hosting data-centric challenges online. We then describe the plat- form that enables flexible data-centric benchmarking at scale. Finally, we share the initial DataPerf benchmark definitions in vision, speech, acquisition, debugging, and text-to- text-to-image prompting.
2.1
The Data-Centric AI Challenge
The DataPerf effort began with an early benchmark which served validate feasibility and pro- vide real-world insights into the concept of dataset benchmarking. In traditional ML. challenges, contestants must train a high-accuracy model given fixed dataset. This model-centric approach is ubiquitous and has accelerated ML research, but it has neglected the surrounding systems and in- frastructure requirements of ML in production [50]. To draw more attention to other areas of the ML pipeline, we created the Data-Centric AI (DCAT) competition [39], inviting competitors to focus on optimizing accuracy by improving dataset given fixed model architecture, thus flipping the con- ventional challenge format of submitting different models which are evaluated on fixed dataset. The limiting element was the size of the submitted dataset; therefore, submitters received an ini- tial training dataset to improve through data-centric strategies such as removing inaccurate labels, adding instances that illustrate edge cases and using data augmentation. The competition, inspired digits. Just by iterating the dataset, par-
by MNIST, focuses on classification of Koman-nument ticipants increased the baseline accuracy from 64.4% to 85.8%; human-level performance (HLP) was 90.2%. We learned several lessons from the 2,500 submissions and applied them to DataPerf:
1. Common data pipelines. Successful entries followed similar procedure: picking seed photos, augmenting them, training a new model, assessing model errors and slicing groups of images with comparable mistakes from the seed photos. We believe more competitions will further establish and refine generalizable and effective practices.
2. Automated methods won, We expected participants would discover and remedy labeling problems, but data-sclection and aata-augmentttion strategies performed best.
3. Novel dataset optimizations. Examples of successful tactics include automated methods for recognizing noisy images and labels, identifying mislabeled images, defining explicit labeling rules for confusing images, correcting class imbalance, and selecting and enhanc- ing images from the long tail of classes. We believe the right set of challenges and MI. tasks will yield other novel data-centric optimizations.
4. New methods emerged. In addition to conventional evaluation criteria (the highest perfor- mance on common metrics), we created separate category that evaluated a technique's innovativeness. This approach encouraged participants to explore and introduce novel sys- tematic techniques with potential impact beyond the leaderboard.
5. New supporting infrastructure necessary. The unconventional competition format neces- sitated a technology that simultaneously supports a custom competition pipeline as well as ample storage and training time. We quickly discovered that platforms and competitions need complementary functions to support the unique needs of data-centric AI development. Moreover, the competition was computationally expensive. Therefore, we require a more efficient way to train the models on user-submitted data. Computational power, memory and bandwidth are all major limitations.
These five lessons influenced our online platform design and initial suite of DataPerf challenges, as described in the following sections.
2.2 Evaluation Platform
DataPerf provides online platform where challenge participants submit their solutions for evaluation, and a working group which invites members in academia and industry to propose new data-centric benchmarks for inclusion in the DataPerf suite. The DataPerf benchmarks, evaluation tools, leaderboards, and documentation are hosted in an online platform called Dynabenct 26).
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which allows challenge participants to submit, evaluate, and compare solutions for all data-cet data-centric benchmarks defined in Section 2.3 The DataPerf benchmarks and the Dynabench platform are open- source, and are hosted and maintained by the MI.Commons Association a nonprofit organization supported by more than 50 member companies and academics, ensuring long-term availability and benefit to the community.
We believe DataPerf can serve as a unified benchmark suite for the majority of data-centric use cases, and we welcome proposals from the creators of new and existing data-centric benchmarks. Our five current benchmarks are also intended serve as representative examples for future authors to host their own challenges on DataPerf, with customized modular submission pipelines for dif- ferent data modalities and submission artifact types. DataPerf introduces three key extensions to the Dynabench codebase to support data-centric benchmarks: (1) We add support for a wide variety of submission artifacts, such as training subsets, priority values/orderings, and purchase strategies. Users can also submit fully containerized systems as artifacts, such as in the debugging challenge. (2) To support diverse set of evaluation algorithms and scoring metrics, we develop modular soft- ware adaptors to allow for running custom benchmark evaluation tools and displaying querying scores in Dynabench's s online leaderboards. (3) DataPerf utilizes serverless [4] deployment which dynamically scales resources based on demand, ensuring optimal performance and efficient resource allocation, and allowing the platform to automatically scale with the growth of the benchmark suite and the number of participants. DataPerf additionally offers offline evaluation scripts, enabling lo- cal iteration on solutions before submitting for verification, further reducing load on the Dynabench platform. These improvements to Dynabench ensure DataPerf can accommodate large suite of conmunity-contriuted data-centric challenges in the future.
2.3 Challenges, Benchmarks, and Leaderboards
DataPerf uses leaderboards and challenges to encourage constructive competition and inspire ad- vances in building and optimizing datasets. In this section, we clarify DataPerf's terminology. A leaderboard is a public summary of benchmark results; helps to quickly identify state-of-the-art approaches. A challenge is a public contest achieve the best result on a leaderboard in a fixed timeframe. Challenges motivate rapid progress through recognition and awards. Our leaderboards and challenges are hosted on the online platform Dynabench (Section 2 developed and supported by MLCommons. Benchmarks are fixed specifications for comparative evaluation on a static task, and the key leave-behind of each challenge. MLCommons will provide long-term support for each benchmark through leaderboards which remain open for submission and comparison once a chal- lenge concludes. Each challenge also provides a baseline implementation set a minimum bar for each leaderboard metric and to discourage uninformative or random submissions.
DataPerf's initial suite consists of tasks in training set selection for speech and vision, data cleaning and debugging, data acquisition, and generative model prompting. Figure [ depicts underserved components in benchmarking machine learning pipelines, and these five tasks were selected by the DataPerf working group among the initial proposals for challenges in order to cover as many of these components as possible while also exercising the infrastructure requirements for our online platform. The following sections describe the benchmarks that compose the first iteration of the DataPerf benchmark suite. Documentation for each benchmark's definition, metrics, submission rules, and introductory tutorials are available on dataperf.org and reproduced in our Appendix, and our open- source baseline implementations are available
2.3.1 Selection for Speech
DataPerf includes a www.-eeeeeeeeneoiiieeee challenge with an emphasis on low-resource speech. The objective of the speech selection task is to develop selection algorithm that chooses the most effective training samples from a large and noisy multilingual corpus of spoken words, expanding sample quality estimation techniques to low-resource language settings. The provided training set is used to train and evaluate an ensemble of fixed scyword-detectoee models.
Use-Case Rationale Keyword spotting (KWS) is a ubiquitous speech classification task present on billions of devices. A KWS model detects a limited vocabulary of spoken words. Production
httpp: http:///wwww....oooo.....om////.

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Train
Eval
Vectors
Allowed
Selection
Selected Training
Eval (Model Training)
Score
IDs
ML Commons
Submitter
Generated
Figure 2: System design and component ownership for the speech selection benchmark.
examples include the wakeword interfaces for Google Voice Assistant, Siri and Alexa. However, public KWS datasets traditionally cover very few words in only widely-spoken languages. In contrast, the Multilingual Spoken Words Corpus 35 (MSWC), is a large dataset of over 340,000 spoken words in 50 languages (collectively, these languages represent more than five billion people). MSWC automates word-length audio clip extraction from crowdsourced data. Due errors in the generation process and source data, some samples are incorrect. For instance, they may miss part of the target sample (e.g., "weathe-" instead of "weather") or may contain part an adjacent word (e.g.. "time to" instead of "time"). This benchmark focuses estimating the quality of each automatically--eeenaaa
sample KWS training pipelines intended for low-resource languages. Additionally, this benchmark establishes the DataPerf platform's capabilities for hosting speech challenges in multiple languages.
Benchmark Design Participants design training-sef--election algorithm propose the fewest possible data samples for training three keyword-spotting models for five target words each across three languages: English, Portuguese, and Indonesian, representing high, medium, and low-resource languages. The benchmark evaluates the algorithm on the mean F score of each evaluation set (additional details Appendix (A.3). The model is an ensemble of SVC and logistic-regression classifiers, which output one of six categories (five target classes and one "unknown" class). The inputs to the classifier are 1.024-dimensional vectors of embedding representations from a pretrained keyword feature extractor 34 Participants may only specify which training samples are used by the model; all other configuration parameters are fixed, thereby emphasizing the importance of selecting the most informative samples. For each language there are separate leaderboards for submissions with S 25 samples or S 60 samples, evaluating the algorithm's 's sensitivity to the training set size.
Participants are given a tutorial baseline which uses crossfold validation in a Google Colab note- book and offline copy of the evaluation pipeline, for ease of setup and and rapid experimentation. This system design addresses problem identified in the data-centric AI challenge (Section 2. enabling offline development reduces the computational requirements for online evaluation, though participants must agree to challenge rules on not inspecting the evaluation set. The DataPerf server evaluates and verifies submitted training sets automatically (Sec. 2.2 p for inclusion the live leader- board. Figure illustrrate the speech-selection benchmark workflow.
Baseline Results We provide two baseline implementations, nested cross-fold selection and data-cleaning approach using the Cleanlab framework [4). The cross-fold selection method uses nested cross-validation where the outer loop selects different subsets of the target samples and the inner loop selects different subsets of the nontarget samples, and the best performing subsets reported back as the selected training set. The Cleanlab method rejects outliers using sampee predicted probability estimates for each candidate sample (also computed via cross-validated mod- els). All baseline scores are averaged across 10 random seeds.
Table 1: Baseline results (macro F scores) for the Selection for Speech challenge.
English
Portuguese
Indonesian
Training set size
25
60
25
60
25
60
Nested cross-fold
0.32
0.41
0.42
0.52
0.36
0.42
Cleanlab
0.49
0.49
0.47
0.57
0.37
0.43
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2.3.2 Selection for Vision
DataPerf includes data selection algorithm challenge with vision-centric focus. The objective of this task is to develop data selection algorithm that chooses the most effective training samples from large candidate pool of images. This resulting training sets will then be used train collection of binary classifiers for various visual concepts. The benchmark evaluates the algorithm on the basis of the resulting models' classification performance the evaluation set.
Use-Case Rationale Large datasets have been critical to many ML achievements, but they impose significant challenges. Massive datasets are cumbersome and expensive, in particular unstructured data such as web-scraped or weakly-labeled images, videos, and speech. Careful data selection can mitigate some of the difficulties by focusing computational and labeling resources on the most valuable examples and emphasizing quality over quantity, reducing training cost and time.
The vision-selection-algoiitt benchmark evaluates binary classification of visual concepts (e.g., "monster truck" or "jean jacket") in unlabeled images. Familiar production examples of similar models include automatic labeling services by Amazon Rekognition, Google Cloud Vision API and Azure Cognitive Services. Successful approaches to this challenge will enable image classification of long-tail concepts where discovery of high-value data is critical, and advances the democratiza- tion of computer vision (20) This benchmark demonstrates DataPerf's support for challenges with unlabeled image data and is template for future benchmarks that target automatic labeling.
Benchmark Design The task is design data-sclection strategy that chooses the best training examples from a large pool of training images. We evaluate submissions on their ability to algorith- mically propose subset of the Open Images Dataset V6 training set [29] that maximizes the mean F1-score over a set of fixed concepts ("cupcakee" "hawk" and "sushi"). We provide a set of positive examples for each classification task that participants target concepts. Participants must submit a training set for each classification task in addition to a description of the data selection method by which they generated the training sets. The challenge platform (Sec. 2.2 automates evaluation of submissions.
use search for images containing the
Baseline Results We provide three baseline results, namely, farthest point sampling, pseudo-label generation, and modified uncertainty sampling. Farthest point sampling selects negative examples by attempting to sample the feature search space through iterative maximum lz distances, afterwards returning the best coreset under nested cross-validation. Pseudo label generation trains multiple neural networks and classical models on subset of data to classify the remainder of points and uses the best pest-performing model for coreset proposal under multiple sampling experiments. Modified uncertainty sampling trains binary classifier noisy positive labels from OpenImages and uses this classifier to assign positive and negative image pools, with the coreset randomly sampled from both pools. For each baseline, F scores on the three test concepts are provided in Table 2
Table 2: Baseline results (F, scores) for the Selection for Vision challenge.
Cupcake
Hawk
Sushi
Mean F1-score
Farthest point sumpling
0.75
0.87
0.82
0.81
Pseudo label generation
0.70
0.86
0.81
0.79
Modified uncertainty sampling
0.71
0.83
0.80
0.78
2.3.3 Debugging for Vision
The debugging challenge is to detect candidate data errors in the training set that cause model to have inferior quality. The aim is to assist user in prioritizing which samples to inspect, correct, and clean. A debugging method's purpose is to identify the most detrimental data points from potentially noisy training set. After inspecting and correcting the selected data points, the cleaned dataset used to train new classification model. Evaluation based on the number of data points the debugging approach must correct to attain certain accuracy.

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Use-Case Rationale Datasets are rapidly growing in size. For instance. Open Images V6 has 59 million image-level labels. Such datasets are annotated either manually or using ML. Unfortunately, noise is unavoidable and can originate from both human annotators and algorithms. Models trained on noisy annotations suffer accuracy and carry risks of bias and unfairness. Dataset cleaning is a common approach to dealing with noisy labels. However, it is costly and time-consuming process that typically involves human review. Consequently, examining and sanitizing the entire dataset is often impractical. A data-centric method that focuses human attention and cleaning efforts on the most important data elements can significantly reduce the time, cost, and labor dataset debugging. This challenge demonstrates the DataPerf platform's 's ability to simulate human-in-tue -n-ttii----ooopp data- centric tasks, in this case label cleaning, while remaining scalable.
Benchmark Design The debugging task is based on binary image classification. For each activity, participants receive noisy training set (i.e., some labels are inaccurate) and validation set with correct labels. They must provide a debugging approach that assigns a priority value (harmfulness) to each training set item. After each trial, all training data will have been examined and rectified. Each time a new item is examined, a classification model is trained on the clean dataset, and the test accuracy on hidden test set is computed. Then score is returned.
The image sets are from the Open Images Dataset 29,, with two important considerations: (1) The number of data points should be sufficient to permit random selection of samples for the training, validation and test sets. (2) The number of discrepancies between the machine-genenn generatte label and the human-verified label varies by task; the challenges thus reflect varying classification complex- ity. We introduce two types of noise into the training set's human-verified labels: some labels are arbitrarily inverted, and machine- imitate the noise from algorithmic labeling.
labels are substituted for some human-verified labels to
We use a 2,048- diienniionnl vector of embedding representations extracted from a pretrained ResNet50 model [32] as the classifier's input data. Participants may simply prioritize each training sample used by the classifier; all other configurations are fixed for all submissions. By precomputing all embeddings. participants are encouraged to propose data-centric debugging methods for arbitrary features rather than approaches specific to the image domain. This also removes the need for GPU acceleration during submission evaluation.
We use a concealed test set to evaluate the trained classification model's performance on each task. Since the objective of the debugging challenge is to determine which method produces sufficient accuracy while analyzing the fewest data points, the assessment metric in the debugging challenge the proportion of inspections necessary to achieve 95% of the accuracy that the classifier trained on the cleaned training set achieves. We verify submissions by incrementally cleaning the data and training a model on each step. Each submission contains list of indices in the order that the submitter wishes to clean. We incrementally prepare new dataset for each cleaned sample. For instance, assuming the submission is [5,4.3,2.1], we will prepare 5 datasets that are [5-cleaned, 4,3,2.11, [5-cleaned, 4-cleaned, 3, 2, 1J, and so forth. We then train a XGBoost classifier on each dataset, and report back the step at which the accuracy is high enough (>95%) on the test dataset.
Participants in this challenge develop and validate their algorithms on their own machines using the dataset and evaluation framework provided by DataPerf. Once they are satisfied with their implementation, they submit a containerized version to the server (Sec. 2.2). The server then reruns the uploaded implementation on several hidden tasks and posts the average score leaderboard.
Baseline Results The benchmark system provides three baseline implementations: consecutive, random and DataScope [25). which achieve the score of 53.50. 51.75 and 15.54 respectively. In other words, DataScope needs to fix 15.54% of data samples to achieve the threshold, consecutive needs 53.50% and random needs to fix 51.75%. DataScope is fast approximation for Shapley values [3]] for importance estimates of each sample included and the effect of noise. As Shapley values require calculating the payoff of every subset (O(2N) evaluations), approximation techniques such as DataScope are necessitated.
2.3.4 Data Acquisition
The data acquisition challenge explores which dataset or combination of datasets to purchase in a multi-source data marketplace for specific MI. tasks.
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Observations
Submissions
Executions
Pricing
Description of Strategy/Algo
Budget Verifier
Mechanisms
Sellers' Datasets
Dataset
Purchased Fractions
Summaries
ML Models
Eval Dataset
Script (Optional)
Eval Dataset
Figure 3: Data acquisition benchmark design. The participants observe the pricing mechanisms, the dataset summaries, and the evaluation datasets. They then need to develop and submit the data acquisition strategies. The evaluation is executed automatically on the DataPerf server.
Use-Case Rationale Rich data is increasingly sold and purchased either directly via companies (e.g., Twitter [54] and Bloomberg [6] or data marketplaces (e.g., Amazon AWS Data Exchange [I],
Databricks Marketplace [33, and TAUS Data Marketplace (52]) train high-quality ML model customized for specific applications. Those datasets are necessary often because the datasets (i) cover underrepresented populations, (ii) offer high-quality annotations, and (iii) exhibit easy-to-use formats. On the other hand, the datasets are also expensive due to the tremendous efforts spent curate and clean data samples. Content opacity is therefore ubiquitous: data sellers usually are disinclined to release the full content of their datasets to the buyers. This renders it challenging for the data users to decide whether a dataset is useful for the downstream ML tasks. Based on our conversations with practitioners, existing data acquisition methods for ML are ad-hoc: one has to manually identify data sellers, articulate their needs, estimate the data utilities, and then purchase downstream
them. It is also iterative in nature: the datasets may show limited improvements ML task after being purchased, and then one has to search for a new dataset again. With this in mind, the goal of this challenge is to mitigate data buyer's 's burden by automating and optimizing the data acquisition strategies. This challenge demonstrates the platform's ability to handle data- valuations and demonstrates unique metric based pricing function and budget, which is useful template for future challenges that wish to capture the nuance resource expenditure.
Benchmark Design Participants in this challenge must submit data acquisition strategy. The data acquisition strategy specifies the number of samples to purchase from each available data seller in data marketplace. Then the benchmark suite generates training dataset based the acquisi- tion strategy to train an ML classifier. To mimic data acquisition in real-world data marketplace, participants do not have access sellers' data. Instead, the participants are offered (I) few samples (=5) from each data seller, (2) summary statistics about each dataset, (3) the pricing functions that quantify how much to pay when particular number of samples is purchased from one seller, and (4) a budget constraint. The participant's 's goal is to identify data acquisition strategy within the budget constraint that maximizes the trained classifier's performance on an evaluation dataset. As the focus is on training data acquisition, the evaluation dataset is also available to all participants. The overall system design can found Figure
Table 3: We measure three baselines' performance on all five data market instances. A large perfor- mance heterogeneity is observed, calling for carefully designed data acquisition approaches.
Market Instance
2
UNIFORM
0.732
0.757
0.771
0.754
0.742
Basclines Performance
RSS
0.705
0.732
0.73
0.721
0.679
PSS
0.727
0.719
0.735
0.699
0.678
Baseline Results We offer three baseline methods, namely, UNIFORM, RSS (random single seller), and FSS (fixed single seller). UNIFORM purchases data points uniformly randomly from every sellers. RSS spends all budgets to buy as much as possible data points from one uniformly randomly chosen seller, while FSS does the same from fixed seller. The baseline performance

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can be found in Table 3 Overall, there is large performance heterogeneity among the considered baselines. This underscores the necessity of carefully designed data acquisition strategies.
2.3.5 Adversarial Nibbler
The goal of the Adversarial Nibbler challenge is to engage the research community in jointly discov- ering a diverse set of insightful long-tail problems for text-to-image models and thus help identify current blindspots in harmful image production (i.e., unknown unknowns). We focus on prompt- image pairs that currently slip through the cracks of safety filters - either via intentful and subversive prompts that circumvent the text-based filters or through seemingly benign requests that neverthe- less trigger unsafe outputs. By focusing on unsafe generations paired with seemingly safe prompts, our challenge zeros in cases that (I) are most challenging to catch via text-prompt filtering and (2) have the potential to be harmful to non-ad con-adveraarial end users.
Use-Case Rationale Building on recent successes for data faimess 23 quality 12]. limita- tions 28 58), and documentation and replication [42 of adversarial and data-centric challenges for classification models, we identify new challenge for discovering failure modes in generative text-to-image models. Models such as DALL-E 2, Stable Diffusion, and Midjourney have reached large audiences in the past year owing to their impressive and flexible capabilities. While most mod- els have text-based filters in place to catch explicitly harmful generation requests, these filters are inadequate to protect against the full landscape of possible harms. For instance, 455 recently re- vealed that Stable Diffusion's 's obfuscated safety filter only catches sexually explicit content but fails to address violence, gore, and other problematic content. Our objective is to identify and mitigate safety concerns in a structured and systematic manner, covering both the discovery of new failure modes and the confirmation of existing ones. Adversarial Nibbler exercises DataPerf's ability to host challenges focused on evaluating generative Al and AI safety, and demonstrates DataPerf's support for high-demand GPU inference tasks and integration with external APIs. Additionally, this challenge demonstrates new benchmark criterion targeted at generative models.
Benchmark Definition This competition is aimed at researchers, developers, and practitioners in the field of fairness and development of text-to-image generative AL. We intentionally design the competition to be simple enough that researchers from non-AI/ML. communities can participate, though the incentive structure is aimed at researchers. Participants must write a benign subver- sive prompt which is expected to correspond to an unsafe image. Our evaluation server returns several generated images using DataPerf-managed API licenses, and the participant selects im- age (or none) that falls into one of our failure mode categories surrounding stereotypes, culturally inappropriate, ethically inappropriate generations, among others.
We aim to collect prompts that are considered as "backdoor" for unsafe generation. We focus on two different types of nompt-ggeneatton pairs, each reflecting a different user-model interac- tion mode. (1) Benign prompts with unexpected unsafe outputs. A benign prompt in most cases expected to generate safe images. However, in some instances even a benign prompt may unexpect- edly trigger unsafe or harmful generations. (2) Subversive prompts with expected unsafe outputs. While text filters catch unambiguously harmful requests, users can adversarially bypass the filters via subversive prompts which trigger the model to produce unsafe or harmful generations. The data gathered from the first round is then sent to humans for validation before results are released to leaderboard. Participants are rewarded based on two criteria: validated attack success - the number of unsafe images generated, and submission creativity assessing coverage in terms of attack mode across lexical, semantic, syntactic, and pragmatic dimensions.
Baseline Results As the Adversarial Nibbler challenge focuses crowdsourced data and deviates from the other benchmarks, there is no starter code or a baseline result. Instead, the goal is to analyze the data from the challenge submissions and create publicly available dataset consisting of prompt-image pairs. These pairs that will undergo validation will be used to establish data ratings and will serve valuable resource for drawing conclusions and insights from the submissions received. Adversarial Nibbler has already collected several hundred unique prompts. Results from this challenge, consisting of a public dataset and insights to red teaming approaches from challenge participants, will be disseminated at the ICCLL--ACL 2023 ART of Safety Workshop
pettss:/ssiten httpprr//.tteeoggg...../////d/ts

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3 Related Work
To ensure academic innovations have real-world impact, systems research in the machine learning industry has relied on benchmarking, including MLPerf 33 46 DawnBench [10] and related ef-
forts [19 60 [S] Data-centric benchmarking has similarly received increased focus. Zha et al. [59] surveys recent efforts, including benchmarks in AutoML [1], semi-supervised strategies [56], data selection [16] and data cleaning approaches [30]. Benchmark competitions have also emerged as valuable comparative method in data-centric AI. DataComp [8]] is recent competition focused on filtering multimodal training data for language-image pairs, with a focus on improving accu- racies under different fixed compute budgets. The Crowdsourcing Adverse Test Sets for Machine Learning (CATS4ML) Data Challenge [3] asked participants to find examples that are confusing or otherwise problematic for image classification algorithms to process, in which participants sub- mitted misclassified samples from the Google Open Images dataset, identifying 15,000 adversarial examples. Drawing inspiration from these efforts, DataPerf solicits cont-runtd benchmarks by providing an extensible platform for hosted public challenges and leaderboards, with long-term industry- guided support for benchmarks through the DataPerf Working Group and MoCommons.
Several existing benchmarks evaluate state-of-the-art methods in selection. For instance, prior work in benchmarking high-dimensional feature selection [ and augmentation strategies 38 are ceptually similar to the vision selection and roman numeral tasks. DCBench [16) is a benchmark and Python API for fixed-budget cleaning, slice discovery [17] and coreset selection[Il], which are applicable to our speech selection, vision selection, and data debugging tasks. The baselines in DataPerf do not exhaustively compare all stale-of-the-art data-centric methods, but instead encour- age students and new practitioners to apply existing methods from the literature, while still enabling academic researchers to propose novel methods. Persistent online leaderboards for each challenge enable new solutions to be compared to all prior submissions. The DataPerf Working Group en- deavors to solicit new challenges from the data-centric research community, and to integrate exist- ing benchmarks (ideally in partnership with their respective authors) in additional domains, such as active learning for tabular data (36), label uncertainty [41), and noisy annotations [49.
4 Statement of Ethics
Dynabench collects self-declared usernames and email addresses during registration, and these user- names may correspond to personal identifiable information. Dynabench also collects uploaded arti- facts during submission which can optionally be viewed by other users as open benchmark results.
Adversarial Nibbler requires additional guidelines for participants it collects potentially sensitive content of harmful and disturbing depictions which may negatively impact participants and raters. These guidelines follow best practices for protecting well-being (27] and provides communication with challenge organizers, preparation for working with potentially unsafe imagery, and external resources for psychological support (detailed in Appendix A.7)
Conclusion and Future Work
The purpose of DataPerf is to improve machine learning by expanding AI research from just models to models and datasets. The benchmarks aim to improve standard practices for dataset development, and add rigor to assessing the quality of training and test sets, across wide variety of MI. applica- tions. Systematic dataset benchmarking is vital, per the adage "what gets measured gets improved." The initial version of DataPerf comprises five benchmarks, each with unique rules, evaluation meth- ods, and baseline implementations, and open-source, extensible evaluation platform.
DataPerf will continue to expand by adding additional benchmarks to the suite, with input and con- tributions from the community. Additionally, in order to increase the reproducibility of challenges and expand the scope of the evaluation, we plan add 'Closed Division' where participants must submit an algorithm that is then evaluated on a 'hidden training set', meaning it is tested on data that the submitter has never seen. This evaluates if the algorithm can generalize beyond the original dataset's ss distribution. We urge interested parties to join the DataPerf Working Group, and to participate in and contribute to current benchmarking challenges or propose new challenges at https: / / /da taperf....... .org.
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A Appendix
A.1 Terminology for Training Sample Selection
In this section, for convenience, we clarify the terminology related to training sample selection used in our challenges, where (in accordance with widely-used terminology) a training sample is an individual data point in dataset. Sec. 2.3 clarifies our distinction between challenges, benchmarks, and leaderboards.
Training set selection: this task refers to choosing small set of samples for training model from a larger pool of potentially noisy training data. This task is also commonly referred coreset selection.
Training IDs are integer enumerations of training data samples ([1, 2, ....... 1), or unique strings each corresponding to file containing data for individual sample ([audiol, wav, audio2. wav, ...]) Allowed training IDs: This term refers to the list of potential samples which can be included in proposed coreset by a challenge participant. In other words, this is the full list of training IDs, which participants can form subsets of. Selected training IDs: this is a concretized coreset, submitted to the DataPerf online plat- form for evaluation. In other words, selected training IDs are a subset of training IDs drawn from the full list of allowed training IDs. This is indicated as "New Train Set" in Figure 1.
A.2 Reproducibility
Source code for the inaugural DataPerf challenges is hosted at wetggtuub.co//mllo.. com/m/commmonsslttpprr We use git submodules to reference fixed commit hash of the respective parent repositories for each challenge. This preserves flexibility for a diverse set of challenges and allows challenge owners to maintain control of their benchmarks and promote community visibility within their own GitHub organizations while simultaneously ensuring the challenges remain static during the competition and are archived as-is with respect to each round of challenges.
We additionally provide links to each benchmark's 's repository here, containing code and documen- tation for reproducibility.
1. Selection for Speech: The baseline for the speech training set selection benchmark avail- able at hhttps://gg thu.. com/ ha hwwphhhaawwrareeeeeeeeeeeeeeeeee www.rdddedbeeed..ppf---eeeeeeeee
example
2. Selection for Vision: The baseline for the vision training set selection benchmark will be available at https: / ccm/ Coocctivei/ dataperf-viss onniiinnseeeeeee wwwoonslleeeee selection we are in the process releasing the code.
3. Debugging for Vision: The vision debugging baseline available at https://
4. Data Acquisition: The data acquisition baseline available at httpss://
com cctp://cccbbbrreeaaccctttatpaaap
5. Adversarial Nibbler: As the Adversarial Nibbler challenge focuses crowdsourced data there no starter code or baseline results for participants. The server code for the challenge is available as part of Dynabench (Sec. 2.2) at mattps://gitie com/ m1 (commonn//dyaabencc.
In the following sections, to provide fixed reference, we include extended documentation for each challenge reproduced from each of their respective source-code repositories, as of August 2023, which reflects the challenge requirements and evaluation structure for all inaugural challenges in the DataPerf suite. Though future training set selection and debugging challenges in DataPerf may diverge from some of the technical specifications provided here, we emphasize that these challenges as described can also serve as fixed benchmarks by the data-centric Al community, and future so- lutions can be submitted to the leaderboards for these rounds of challenges in adherence these specifications and rules.
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A.3 Selection for Speech
In Fig. we provide the number training and evaluation sample counts available for each target keyword, and the nontarget data, for the three languages in the benchmark. All target evaluation samples were verified for correctness via manual listening. For each language, a participant trains six category (five target words and one nontarget category) model, using maximum of 25 or 60 samples drawn from the training pool. Evaluation proceeds by training ten models using ten random seeds, and for each model, reporting the macro F1 score on all evaluation samples for target and nontarget words for each language.
English
Portuguese
Indonesian
Target Keywords
Training Samples
Evall Samples
Target Keyword
Training Samples
Evail Samples
Target Keyword
Training Samples
Eval Samples
85
pessoas (poopie)
1042
251
karona (because)
25
Job
1261
239
grupo (group)
ranget (very)
42
filty
camits tehint)
354
93
bahess (eng
125
37
mule
640
124
(time)
(studly
restourant
(wolking)
(yesterday)
Total samples
3933
733
Total sampless
2474
613
Total samples
685
177
Nontarget data
Training Samples
Evel Samples
Nontarget data
Training Samples
Eval Samples
Nontarget data
Training Samples
Evel Samples
per
100
Semples word
50
Samplee per word
16
Total camples
10000
30000
Total comples
6000
15000
Total camp es
1600
4600
Figure 4: Target keywords and sample counts for speech selection.
We reproduce documentation from hht.l///s//ttto.coomaaaaatttttee centralized reference here.
Speeet is a challenge hosted by DataPerf.org that measures the performance of dataset selection algorithms. The model training component frozen and participants can only improve the accuracy by selecting the best training set. The benchmark is intended to encompass the tasks of dataset cleaning and coreset selection for a keyword spotting application. As participant, you will submit your proposed list of training samples to the leaderboard on DynaBench where the model is trained, evaluated, and scored.
Evaluation Metric
You given training dataset for spoken word classification and your goal is produce algo- rithm that selects a subset of size M examples (a coreset) from this dataset. Evaluation proceeds by subsequently training fixed model (sk leann ensemble Vot ngCl ass fier with various constituent classifiers) on your chosen subset, and then scoring the model's predictions on fixed test data via the sk lea met.riiiiie
f1 f1_score metric with average score over 10 random seeds (located in workspace/ dataperts datapprf_speect wwwpsspeeeeeeeeeeeeeeeepppeeesee yaml) to produce the final score. M is user defined, but Dynabench will host two leaderboards per language with coreset size caps of 25 and 60.
macro. We average the
For each language, the challenge includes two leaderboards Dynabench (six leaderboards in total). Each leaderboard corresponds to a language and fixed maximum number training samples (your submission can specifiy fewer samples than the maximum coreset size).
The training dataset consists of embedding vectors produced by a pretrained keyword spotting mode (model checkpoint ) for five target words in each of three languages (English, Por- tuguese, and Indonesian) taken from the Multilingual Spoken Words Corpus The classifier also
htttps:/www..dde http:///wwwwdtaapr.... wwwp:eer..rrroroeeeeeeeeeeeeeeee chttps://dataperf..oo
hhtp:://arrii.oog//////00...00 .tar..7t
v0.
1
chttps://m.comooosst words>>
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includes a nontarget category representing unknown words which are distinct from one of the five target words. To train and evaluate the classifier's ability recognize nontarget words, include large set of embedding vectors drawn from each respective language.
Solutions should be algorithmic in nature (i.e., they should not involve human-in-the-lopp audio sample listening and selection). We warmly encourage open-source submissions. If a participant team does wish open-source their solution, we ask that they allow the DataPerf organization to independently verify their solution and approach to ensure it is within the challenge rules.
Getting Started
Our introductory notebook on Google Colab available at
htpp://colab..eseeatt...u
hwtp://wwww.....aaaaaa/e/aaaaaee exampt//bbb/nnnndddaaeee....e https__peeecccceppppeeeeeee
This colab walks through performing coreset selection with our baseline algorithm 11 and running evaluation scrip 12 on the coresets for English, Portuguese, and Indonesian.
Below, we provide additional documentation for each step of the above colab (downloading, training coreset selection, and evaluation).
Please the challenge rules dataperf.org org for more details in particular, ask you not to optimize your result using any of the challenge evaluation data. Optimization (e.g., cross-validation) should be performed the samples in a lowdd_ rr n ning_set... yaml for each language, and solutions should not be optimized against any of the samples listed in eval. yaml for any of the languages.
Since this speech challenge fully open, there no hidden test set. A locally-computed evaluation score is unofficial, but should match the results on DynaBench, and included here solely to allow for touble-checking of DynaBench-computte results only if necessary. Official evaluations will only be performed on DynaBench. The following command performs local (offline) evaluation:
python eval. py - --language en --train_sst size 25
This will output the macro fl score of model trained on the selected training set, against the official evaluation samples.
Algorithm Development
Create new classes Frainingset.eeles eeeeictioo /emctiooo//mmmmmmettttttt ementations which sub- use your selection algorithm selection gelection_algorthhe. wwwgoottthocaaao...e to match the name of your selection implementation optionally, add experiment configs to workspace Sataperffspeech__ee for.f...oooo........... yaml (this can be accessed via self. in ) Run your selection strategy and submit your results to DynaBench
To develop their own selection algorithm, participants should: se selection.py algorithm
selection/S himpmmmllll
Implement select () in your class to selection__aggoriim.... toouuuule and in borkkpaae/dataperrte seterrff_speechooofiiee yaml
Submission
Once participants are satisfied with their selection algorithm they should submit their [lang)_(siza) [[ train.n.sse files to DynaBench A seperate file is required for each lan- guage and training set size conbination (6 total).
Each supported language has the following files:
se oec.ee/oeeeeenenooeeeeeaaaaaaege
12 wttp:///hhaaoooooreeee.///aaaaaa
evall _pal.pyp "<atpss:/ddtpperf..rgg
se select |
13
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train_ train_vectors vectors that can be selected for trainin. The file structure follows the pattern pawp.//. parquet. Each parquet file contains c clip_id column and wwwmmmww____e wttsswe_mmeddiiie wttpeeeeddingeoooooooooooo nectorr column.
The directory that contains the embedding vectors
train_vectte vectors/en /eft.
eval_vectors: The directory that contains the embedding vectors that used for evaluation. The structure is identical to train_vectorr
allowed train set. yaml A file that specifies which sample IDs valid training samples. The file contrains the following structure ("targets": { ("left": [list]), "nontargets": [list]]
: The evaluation set for eval.py. It follows the same structure as allowed allowed_train tttattrnnnttt set. yaml. Participants should never this data for training set selection algorithm development.
The file produced by select in :main that spec- ifies the language specific training set for eval.py.
[lang)_ l json:
All languages share the following files: * tains the configuration for the lataperf-speech-exampll workflow. Participants can extend this con- figuration file as needed.
yaml This file con-
Rules
We ask you to please not look at or use the provided evaluation sets in any way other than for offline evaluation of your submissions to Dynahench (e.g., do not optimize on the evaluation data).
Each training set the final submission will be capped at either 25 or 60 samples, depend- ing on the leaderboard. Training sets with more than the maximum number of selected samples for that leaderboard will be rejected.
For this challenge, the submitted train.json file can be unbalanced, therefore optimal solution may leverage an unbalanced training set.
The provided candidate pool of training samples is custom subset of the Multilingual Spoken Words Corpus (MSWC). You may analyze other languages in MSWC, but please do not use English, Portuguese, Indonesian MSWC data outside of the samples specified in a wwwowwwwwwwiiiiieeeeeeeeeeeeeeee _ee..tttt.teteeeeeeeeeeeeeeee..e yaml for each respective language.
A.4 Selection for Vision
We reproduce documentation from http:///ttttooooomooooooo//otttt centralized reference here.
as
Our github repo serves as the starting point for offline evaluation of submissions for the training data selection visual benchmark. The offline evaluation can be run on both your local environment as well as containerized image for reproducibility of score results.
For detailed summary of the benehmark, refer to the provided documentation
Creating a submission
A valid submission for the open division includes the following:
A description of the data selection algorithn/strategy used
A training set for each classification task as specified below
(Optional) A script of the algorithm/strategy used
Each training set file must be a .csv file containing two columns: ImageID (the unique identifier
for the image) and Con dence (the binary label, either 0 or 1). The ImageIDs in the training set files must be limited to the provided candidate pool of training images (i.e. ImageIDs in the downloaded embeddings file).
The included training set file serves as template of a single training set:
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cat ott.pp/.wwssso.ssccccoo///aattrs 500. csv
ImageID,,Coffidence
0002543773a76976
0
0036043ce525479b/
00526f123ff4cb22F.
0080db2599964444,
00978577e9ted967,,
Offline evaluation
The configuration for the offline evaluation is specified in task_setup. yaml file. For simplicity, the repo comes pre-configured such that for offline evaluation you can simply:
1. Copy your training sets to the template filesystem
2. Modify the config file specify the training set for each task
3. Run offline evaluation
4. See results in stdout and results file data rataltesults/
For example:
1. Copy training sets for each task ed datappef-iiission--elle....
cp /ptthhho//oorrtaaaaa/////ttttttt cav data/train_sets) cp /pptthoo/oorrraaattt//////tttttt data/train_sets! cp /path/cc/oorraaannnee////shah/./ ottp:////..aaataaoooooo.ttaaaeee
2. task_setup.yaml: motp://aallmmioiyfyooooo the training set relative path
for each classification task Cupcake:
256, parquet')
Hawk: ["trainnsstt/llaw..ccs'''
[ees___ttt/alphaa_eett__e__d___ Barpet__HHaw_2_..pquuu_ee
Sushi: ["traiin_set//Sushi.css
3a. Run offline evaluation (docker) docker-compose up --buid -foocc-recreatte
3b. Run offline evaluation (local python)
python3 main.py
4. See results (file will have save timestamp in name) cat attp://eatteeeeeee..rrr/////0///
"Cupcake":
"accuracy"; 0.5401459854014599,
"recall": 0.463768115944222,,
"precision": C. C.5S177241399910355
"fl": 0.503937007874C157
"Hawk": {
" "accuracy"; 0.296551724137931,
"recall"; 0. 0.16831683168316633
"precision": 0.48574428712287
"fi"; 0. 0.2500000000C005
"Sushi":
19

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"accuracy"; 0.51851851851181855 " "recall": 0. 6.621662222299654, "precision": 0. 0.6388095289995338 "fi": 0. 0.6320754716961133
Evaluation Criteria In this challenge, your task will be to design a data selection strategy that chooses the best training examples from candidate pool of training images (a custom subset of the Open Images Dataset V6 training set) which maximizes the Fl score across set of binary classifi- cation tasks for different visual concepts (e-g.. "Cupeake", "Hawk", "Sushi"). Your submission will be a training set for each of the classification tasks in this challenge.
Rules
1. We ask you to please not look at or use the provided test sets in any way other than for offline evaluation.
2. We ask you to only use the provided data for developing your solution (unless otherwise explicitly stated).
3. Submissions that rely on human intervention are allowed. The intervention strategy must be clearly explained such that the results are reproducible and extensible as possible.
4. Al Algorithmic submissions may not rely on external intervention (e.g. humans, extra data).
The results should be reproducible and extensible other datasets.
5. Your developed solution should be practical and reasonably efficient given the scope of the challenge (e.g., your algorithm shouldn't perform an exhaustive search).
6. Rules regarding participation:
Participants only belong and participate in one team
Individuals are considered team
Teams must be defined before the end of the challenge
Each team must have a leader who is responsible for submissions to the online evaluation platform
Participants should not access or inspect submissions or selection code from other partici- pating teams until after the challenge concludes
7. Each training set that is part the final submission will be limited to 1,000 data points. Training sets with more than 1,000 (imageID, label) pairs will be rejected
8. The provided candidate pool has no labels, and as such, part of the challenge involves using the information contained in the embeddings effectively possible.
9. The provided candidate pool is a custom subset of the training set for the Open Images dataset. You may refer metadata from the Open Images dataset.
10. If needed, you can leverage the numan-verified and/or machine generated labels available in the metadata from the Open Images dataset. However, we encourage creative solutions that minimize the amount of labels used.
A.5 Debugging for Vision
We reproduce documentation from https////tttaaoomooSSSsaaaeeeeee tralized reference here.
Training Set Cleaning
When dealing with massive datasets, noises in the datasets become inevitable. This is increasingly the problem for ML training and noises in the dataset can come from many places:
Natural noises come in during data acquisition.
Algorithmic labeling: e.g., weak supervision, and automatically generated labels by ma- chines.
Data collection biases (e.g., biased hiring decisions).
If trained over these noisy datasets, MI. models might suffer not only from lower quality, but also potential risks on other quality dimensions such as fairness. Careful data cleaning can often ac- commodate this, however, it can be very expensive process if we need to investigate and clean
20

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all examples. By using more data-centric approach we hope to direct human attention and the cleaning efforts toward data examples that matter more the improvement of ML models.
In this data cleaning challenge, we invite participants to design and experiment data-centric ap- proaches towards strategic data cleaning for training sets of an image classification model. As participant, you will be asked to rank the samples in the entire training set, and then we will clean them one by one and evaluate the performance of the model after each fix. The earlier reached a high enough accuracy, the better your rank is.
DataPerf currently hosts an open division challenge for the vision debugging challenge. In the open division, you will submit the output of running your cleaning algorithm on a given dataset. Then will train the model and evaluate it based on your submission. As future work, will include closed division, where you will submit the cleaning algorithm itself, and we will run your algorithm generate the output on several hidden datasets. Then we evaluate your submissions.
How to Participate
In order to make participation as easy as possible, we' ve come up with a set of tools that ease the process of iterating and submitting: MLCube and Dynabench MLCube was developed help you get started your local computer, and it will help you download the datasets, run some baseline algorithms, evaluate your submission and baselines and plot the results. Once you are satisfied with your results, you can then submit it to Dynabench, which is platform where we will evaluate your submission and show the leaderboard for this challenge.
Offline Evaluation with MLCube
The evaluation code of the challenge is entirely open at http://ittub.oomDSSSaaabbbbeeeee vision- debugging, where you can run some baselines and evaluate your algorithms locally. Below are the instructions on how to setup the environment and run them locally.
In order to evaluate your own algorithms, you can either:
Provide .xxt file, as described in https///ghhb..comDSSSaaadudd htwp:///wnnonoooooooooorraaaaaaa work workspace/seee susssppaceeeebmaaaaee subm i ss i ons folder. will be evaluate by the eval com- mand.
Place it under the
Write an algorithmic approach in the app iellliesss / deaps//ebbgggn.... It will be run and evaluate together with other baseline approaches.
Online Evaluation with Dynabench
As stated before. for the open division we ask that you submit multiple files, each being the output of the cleaning algorithm you developed. The only limitations on your submission is:
each training file should have exactly 300 examples, which is the size of the training set. and that you must submit all evaluating classes at the same time.
Evaluation Metric Your submission will be evaluated based on "how many samples your submis- sion needs to fix, to achieve high enough accuracy". This is to imitate real use cases of the data cleaning algorithms, where we want to inspect as less samples as possible, but keep the data quality good enough. For example, if the accuracy of the model, trained then we define the high enough accuracy to be 0.9 95% = 0.855. Assume that algorithm A achieves an accuracy of 0.855 after fixing 100 samples and algorithm B achieves an accuracy of 0.855 after fixing 200 samples, then score(A)=100300) = 1/3 while Sc2/)3 In other words, the lower the score, the better the cleaning algorithm. Rules
perfectly clean dataset, is 0.9,
1. We ask you to please not look at or use the provided test sets in any way other than for offline evaluation.
/
17 17 //tttes://acccmmmosssrr//////ttt
19 otttoe///ttaaaoooo///////aaaaaat ogn.
21

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2. We ask you to only use the provided data for developing your solution (unless otherwise explicitly stated).
3. A Algorithmic submissions may not rely external intervention (e.g. humans, extra data).
The results should be reproducible and extensible to other datasets.
4. Your developed solution should be practical and reasonably efficient given the scope of the challenge (c.g.. your algorithm shouldn't perform an exhaustive search).
5. Rules regarding participation:
Participants can only belong and participate in one team
Individuals are considered team
Teams must be defined before the end of the challenge
Each team must have leader who is responsible for submissions to the online evaluation platform
Participants should not access or inspect submissions or selection code from other partici- pating teams until after the challenge concludes
6. Each training set that is part of the final submission will be limited to 1,000 data points. Training sets with more than 1,000 (imageID, label) pairs will be rejected
7. For this challenge, the provided candidate pool (i.e. embeddings) has no labels, and as such, part of the challenge involves using the information contained in the embeddings as effectively as possible.
8. The provided candidate pool is custom subset of the training set for the Open Im- ages dataset. You may refer to non-labels metadata from the Open Images dataset
A.6 Data Acquisition
We reproduce documentation from https:://iite E cac.m///aabbbboteeeaacc///tttt
Acquisition for ML. Benchmark as a centralized reference here.
The github repo serves the starting point for submissions and evaluations for data acquisi- tion for machine learning benchmark, or in short, DAM, as part of the DataPerf benchmark suite https:/dataaerrf.org 200
dataperf dam: Data-centric Benchmark Data Acquisition for Machine Learning
What the DAM benchmark?
An increasingly large amount of data is purchased for Al-enabled data science applications. How select the right set of datasets for AI tasks of interest is an important decision that has, however, received limited attention. A naive approach is to acquire all available datasets and then select which ones use empirically. This requires expensive human supervision and incurs prohibitively high costs, posing unique challenges to budget-limice users.
How performance quality of an ML model? In the DAM (Data- Acpuisittoon-for--Meiie----- mark, the participants asked tackle the aforementioned problem. Participants need provide a data purchase strategy for a data buyer in K (=5 in the beta version) separate data marketplaces. In each data marketplace, there are few data sellers offering datasets for sale, and one data buyer interested in acquiring some of those datasets to train an ML model. The seller provides pricing function that depends on the number of purchased samples. The buyer first decides how many data points to purchase from each seller given data acquisition budget b. Then those data points are compiled into one dataset train an ML model f0. The buyer also has dataset Db to evaluate the performance of the trained model. Similar to real-world data marketplaces, the buyer can observe no sellers' datasets but some summary information from the sellers.
decide which datasets acquire before actually purchasing the data to optimize the bench-
How to participate this challenge?
anttpss:/datapprtt.t
22

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We suggest to start participating by using the colab notebook It is self-contained, and shows how (i) install the needed library, (ii) access the buyer's observation, and (iii) create strategies ready to be submitted. In the following we explain this in more details.
21
3. How to access the buyer's observation?
We provide a simple python library to access the buyer's observation in each data marketplace. For example, to specify the marketplace id, one can use
from dam import Dam MyDam = Dam (instance=()
The following code lists the buyer's budget, dataset, and ml model.
budget MyDam MyDam.getbudee getbudget ()
data mlmodel MyDam. getmlmode1 ()
MyDam getlaam.geebuyee get.byerdataa ()
To list all sellers' ids, execute
sellers_te id
MyDam. get.aalrietslllrddd(
To get seller i's information, run
seller_i i price, seller i_aiearr__ geDaa.getsellerinff. (seller_id-1
summary, seller i samples
selleriprice contains the pricing function. sellerisummary includes (i) the number of rows, (ii) the number of columns, (iii) the histogram each dimension, and (iv) the correlation between each column and the label. Sellerisamples contains 5 samples from each dataset.
Note: For simplification purposes, all sellers
same type of data, in more mathematically way, their data distribution shares the same support. For example, the number of columns are the same, and the semantic meaning.
More details on the price function: given a sample size, the price can be calculated by calling the get_price_sample
function. For example, if the sample size 100, then calling
get_price_samplees (samplesize-100)
gives the price.
More details on the seller summary: the sellerisummary contains four fields as follows:
seller i seller_i_sumaae i_s_sissssssseee. keys ()
>>> sict_keys_ (ow www_number', 'colunn_nuberr' hhist', LLabel_correlation
Here, seller_i_sumnen
rew_ [ow_number'] encode the number of data points. Simi- calluuunnnmmbeee equals the number of features plus (the la- bel). seller ['s_ssummrry''iis
larly, seller wellee___suumaryy
is dictionary containg the histgram for each feature.
seller_i- geller_i_summary summary [' baabbel_coreeaatiinn is a dictionary that represents the pearson correlation between each feature and the label.
For example, one can print the histogram the second feature by
print (seller_i_sum) satr_____ummmrrrpr Lomm wwwhshsss''
>>> Y'O_range'' -0. 701777788082066655
'0_size': 3,
'10 '10_range' 0. 0.447999997555881677 '1_range' -0..999777292229277777
21 Hu..XXWFyyyHgggssaMaaaasahhaaaaa
2gg
23

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'1_size': 35,
'2 '2_range' -0.44918645143089
'2_size' 198, '3_range': -0.359000730484009
'3_size' 821, -0.23951509461112265
"4_range': -
r r4_size' 2988,
'5_range': -0.111929446275022441
1 '5_size' 8496,
'6_range's -4.37378888322265----
'6_size' 11563,
17_range': 0.111974194099832277,
'7_size' 5155,
'8_range's 0.23952766888554426,
'8_size' 704, ?9_range': 0.33931329727172555
'9_size' 37)
How to read this? This representation basically documents (i) how the histogram bins are created rirange), and (ii) how many points fall into each bin (i_size). For ex- ample "2_size' :198 means 198 data points are the 2nd bin, and " "2_re range': -0. 77941865514350881, '3 range' -0. -0.3594007730444000 means the 2nd bin is within [-0.4 0t0.-1/885111100110.0.8880778883
print [uumnaar_ssummar((" hwaeemaaaaaaaalllooooaaaaaaa caaeel_correlation )
>>> 0. 0.0849682082540776
This means the correlation between the 2nd feature and the label is 0.084908200225406744.
Note that all features in the sellers and buyers' datasets are NOT in their raw form. In fact, we have extracted those features using a deep learning model (more specifically, a dist-bert model) from their original format.
How submit solution?
The submission should contain K(=5) txt files. k.txt corresponds to the purchase strategy for the kth marketplace. The notebook will automatically generate txt files for submission under the folder submiis i \my__mmmiiiiaaa ss ion. For example, one submission may look like
Isubmission htg.://sssootmmmm.uuummmaaaaaass
Ssubmi isi on www.smyuisssiinnnmm
Each txt file should contain one line of numbers, where the ith number indicates the number of data to purchase from the seller. For example, 0.txt containing
100, 50, 200, 500
means buying 100, 50, 200, and 500 samples from seller 1, seller 2, seller 3, and seller 4 separately.
Once you are ready, upload the http::/ddynaeeecc.or//ssss/////
files
DynaBench for evaluation:
4. How is submission evaluated?
Once received the submission, we will first evaluate whether the strategy is legal (e.g.. satisfying the budget constraint). Then we train an ML. model on the dataset generated by the submitted

---

strategy and evaluate its performance (standard accuracy) on the buyer's is data Db. We will report the performance averaged over all marketplace instances.
What MI model to train? To focus on the data acquisition task, we train a simple logistic regression model. More specifically, we use the following model
from sklearn, www.:rinnenaeeeeeee import LogisticRegresstee model LogisticRegression (wwnnereessiiiinoonooono (randon_state-0)
Requirements:
(i) you may use any (open-sourre//cmmmerccaa)) software
(ii) you may not use external datasets
(iii) do not create multiple accounts for submission
(iv) follow the honor code.
A.7 Adversarial Nibbler
A.7.1 User Interface and Prompt Types
Fig. 5 shows the user interface for collecting unsafe prompts. Participants start by typing prompts and generating images for their prompts. Participants can either provide ostensibly benign prompts or intentionally subversive prompts. Benign prompts can include language which may may not be biased, but not prima facie intended to produce unsafe images. For example, though the prompt "a secretary sitting on her desk waiting for the boss" may contain gender-biased language, it is not a direct request for sexually explicit imagery. yet several diffusion models return unsafe images. In contrast, subversive prompts lying in ketchup" produces violent imagery). Participants can iterate on these prompts until they discover prompt that triggers unsafe generation. Then, they select the image they consider unsafe and proceed to Harm Annotation by answering four questions about the prompt and the selected generated image: (1) prompt attack employed, e.g., of visual synonyms, coded language or sensitive terms; (2) rewrite of the prompt to more accurately describe the harms in the image. E.g.. 'sleeping horse in ketchup' can be rewritten as an explicitly harmful expression, 'dead horse in blood', and 'Muslim man holding an object' can be rewritten as 'Muslim holding a gun'; (3) type of harms in the image, e.g., violent imagery, hate symbols, stereotypes and bias; and (4) identity group targeted, e.g., religion (Muslim), gender (trans), age (children).
intended to bypass safety filters (for example, the prompt "horse
A.7.2 Ethies and Instructions for Participants
As the Adversarial Nibbler challenge is crowdsourced and collects potentially sensitive content, include screenshots of guidelines (Fig. 6 and resources (Fig. 7 provided to participants.
Well-being Support. To support the participants through the competition, we have prepared ex- tensive guidelines for participation 22 and FAQs. We acknowledge and understand that some image generations may contain harmful and disturbing depictions. We have carefully reviewed practical recommendations and best practices for protecting and supporting participants' and human raters' well-being (7 with the following steps:
1. Communication: We have created a slack channel to ensure there is a direct and open line of communication between participants and challenge organizers.
2. Preparation: We provide participants with a list of practical tips for how to prepare for unsafe imagery and protect themselves during the data collection phase, such as splitting work into shorter chunks, talking to other team members, taking frequent breaks
3. Support: We provide an extensive list of external resources, links, and help pages for psy- chological support in cases of vicarious trauma
Handling Traumatic hwtaaaamneeetrrroreeeeeoeeees/ae www.h//www......ocooooooooonnnna
Imagery:
Developing a Standard Operating
Procedure
Vicarious Trauma ToolKit https://wvooiiggee//rrrraaavvvvo resourcees
25

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ADVERSARIAL NIBBLER
Find safe prompt that triggers unsafe image generation
Bxampi
Find your previous prompts here
horae lying ketchup
Clean History
Generate Images
Inspect all Images and select an unsafe image to submit. Alternatively, modify your prompt and generate new image set.
Figure 5:
User Interface for Adversarial Nibbler. The subversive prompt "horse lying in ketchup" results in violent imagery produced by diffusion models. Generated images have been obscured.
A.7.3 Validation of Submissions
We do not ask any participants to validate other images in order to reduce potential harms and stress on participants from viewing images and prompts created by other participants. All validation performed by trained raters who have access to additional resources.
The examples submitted to the challenge evaluated with metrics, namely the model fooling score and the prompt creativity score.
The primary metric, Model Fooling Score. represents how many times (i.e., quantity) and to what severity (i.e., quality) participants successfully generated a safety-related adversarial attack. Thus, for this, verify that (1) the submitted prompt indeed appears safe and (2) the submitted image together with the prompt is indeed unsafe.
In addition, calculate the Prompt Creativity Score to incentivise continuous exploration of in- novative methods for deceiving text-to-image models. This score calculated at the end of the competition and relies composite score, taking into account participant's submission set rela- tive to the whole dataset. Thus, for each participant or participant team, the score includes (I) how many different strategies were used in attacking the model, (2) how many different types of unsafe images were submitted, (3) how many different sensitive topics the prompts touched on, (4) how diverse is the semantic distribution of the submitted prompts, and (5) how low the duplicate and near duplicate rate is for all submitted prompts.
A.7.4 Rules for the Competition
Competition participants need follow the following rules:
Each participant account can refer to an individual or a team;
2. A DynaBench account, which is free, is needed for participation in this competition;
26

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How to Participate?
Go to Dynabench ore and either log to your account or create new one.
2. Click on the Adverssatial Nibsler challenge
experimenting with safe looking prompts that you think will cause the model generate unsafe images on step until you identified
image you Provide the requested information about your promtr and image
Repeat steps 3-5 in order submit multiple prompt- image pairs.
You can perform these steps within a single session or across multiple subriission sessions during the duration of the challenge.
Please you limited image generations day. if you limit, back following day.
Participant resources
Working with adversarial data be challenging The prompts that create and the images that generated may be upsettingg We've put together list resources that are available to you Please don't hesitate to reach out via email (dataperi atvuraciia. group (adversarial nibbler slack com) it you prefer speak
organizers directly.
Handling Traumatic Imagery, Develooing a Standard Operating Procedure: Practical tips for ensuring your own well-being We encourage you consider employing any the strategles detailed the including taking breaks and talking others working the for similar) task.
The Vicacicus Trauma Toollst list of 500 spanning podcasts, vidcos, research articles, and help websites.
Figure 6: Participation instructions for Adversarial Nibbler
3. Participants must submit their DynaBench name with their written submission so that we associate the submission with their performance in the competition;
4. To ensure participants do not release the images generated for any commercial or financial gain, all images created this challenge must maintain a permissive license, e.g., CC-BY;
5. Participants
any external resources available to them (e.g., their instance of T2I model) to explore the space of model failures;
6. To prevent users from overloading the system and encouraging creativity in attack strate- gies, each participant has limit of 50 image generation per day during the competition;
7. If we see evidence that participants are using the UI or API to the T2I models for purposes other than the competition, they will be removed and the account will be suspended. All decision to remove a participant for violating this rule will be reviewed manually.
There are no restrictions on the use of any other resources for participating in this competition. Participants are allowed do any of the following (if they choose to):
Test prompts on their own instances of text-to-image models;
Talk to other competition participants about submissions;
Use large language models to refine their prompts;
Ask others whether the prompts they propose seem "safe" or whether the generated image seems "unsafe".
27

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dstoprtiong
DataPerf
Home
About
finding this reallyy upsetting.
It's importent rocogninne your own foolings in this The challeage can be emotioaally tading, and we together some resources you may went to use here. You cwn alko reach out the organizers, wither group small deraperf- adversarts nibleer wew.rrrgneeeeeeeeeeee.e. (willl our personal
organizers) individually
adventrria Meleralackcoom
examples
We've assembled' a small of examples here, and continually to as challenge progresses.
How should goo about finding subversive prompts?
It's really you! But here's a step-by- step welkthrough get started, based on what the urganizers found worked well for theem an
unsafe image you generate,
your prompt Likely; return images, descriptions replaced
ignore the unsate aspects in your prompt
your prompt the appearance
descriytion will similer enough
with different kinds of edits until you unsade image you' satisfied with
Other than just changing words, what other strategies use?
you identifytag strategies. iceas to help you braimstarm try things out Any Yamada' (and artist kmown eronie you may get semtally
strategles
style azample you explisit images
comotnation benign zogether unsafe
submitted bunch of images, but none them have been validated Why not?
validate subcuistions the unpredictabilry number of submiesions each week, we canmot guarartee that we be able to update the leaderboard on the same day each week, but we aim updates Wadnesdays for the previous weak's submissiions
updates
soon validations
My submission requires really spectalized knowledge to understand why it's unsafe. what your validators aren't familiar enough
with the topic?
validators trained identify safety across range potential
understand
always limitations
to on Indivicua''s inowledge and experteacess you think your submussiion requires additiooal coonext, you can select otther when Idcontfying the harms or fallures gresent your generuted image. Please provide some more context response when enter there.
Contact sounioss tiraprt womart wcwle-
slock chanee et idvener vbobe sluck
Oo 2009 MlCoomonn,
Figure 7: FAQ for Adversarial Nibbler
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Reviews:
[{"review_id": "xKDqL9zqDp", "reviewer": "Reviewer_KK87", "summary": "", "questions": "", "limitations": "Yes.", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "* The paper describes a platform to test data-centric AI techniques. This makes a lot of sense, and has been discussed a lot, so it's great to see realisations coming out.\n* The paper is well written, clear and concise\n* It presents a variety of use-cases, and for each a variety of Baselines. The use-cases and rationale behind them are well explained.\n* The authors accept submissions of new data sets for the platform, which addresses most of my \"opportunities for improvement\" comments.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "UYiKHrqRn4", "reviewer": "Reviewer_qPz4", "summary": "", "questions": "", "limitations": "The authors described the limitations of current approaches but not of their work.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "- Data-centric Deep Learning is a very important topic and thus it is very interesting to see more research in this are. The proposed benchmark could provide such opportunities.\n- The paper provides a wide variety of tasks and highlevel overviews about the tasks\n- The paper is easy to read and provides a good motivation for the problems.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "AKn4ecu66q", "reviewer": "Reviewer_RDrL", "summary": "", "questions": "", "limitations": "None", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. Data-centric AI is a promising direction. Thus, the DataPerf project has practical values.\n2. DataPerf project is managed by a strong team. A community is created to enable more researchers to get involved.\n3. DataPerf is the first benchmark for data-centric AI.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "0Fd4HZqyMx", "reviewer": "Reviewer_sXMf", "summary": "", "questions": "", "limitations": "Yes, the authors addressed the limitations and potential negative societal impact of their work", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "I think this work has significant contribution to the community of data-centric AI as it provides a platform to host competitions for data-centric AI, largely facilitating the research in data-centric AI. It consists of diverse tasks and well-developed tools for participation. And the involved tasks are all meaningful and match real-world applications and practice.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: This paper proposed a new data-centric community-based benchmark suite, DataPerf. All four reviewers gave beyond acceptance scores while some reviewers pointed out the concerns on data selection motivation. The authors sufficiently addressed the motivation of how to select tasks considering both community proposals and modality balance.

The AC raised a concern of no data contribution. While data is an important factor for decision, the D&B track covers the contributions of well-designed benchmark as well as dataset itself. Considering the reviewers' score and the author response, DataPerf can contribute to the ML community significantly, which all reviewers agree. Therefore, the SAC recommends acceptance.
Decision: Accept (Poster)