ABSTRACT
Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.
- Avrio AI. 2018. Avrio AI: AI Talent Platform. (2018). https:/www.goavrio.com/Google Scholar
- Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine Bias. (2016). https:/www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencingGoogle Scholar
- Emily M. Bender and Batya Friedman. 2018. "Data Statements for NLP: Toward Mitigating System Bias and Enabling Better Science". Transactions of the ACL (TACL) (2018).Google Scholar
- Joy Buolamwini. 2016. How I'm fighting Bias in Algorithms. (2016). https:/www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms#t-63664Google Scholar
- Joy Buolamwini and Timnit Gebru. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Proceedings of Machine Learning Research), Sorelle A. Friedler and Christo Wilson (Eds.), Vol. 81. PMLR, New York, NY, USA, 77--91. http://proceedings.mlr.press/v81/buolamwini18a.htmlGoogle Scholar
- Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5, 2 (2017), 153--163.Google Scholar
- Federal Trade Commission. 2016. Big Data: A Tool for Inclusion or Exclusion? Understanding the Issues. (2016). https:/www.ftc.gov/reports/big-data-tool-inclusion-or-exclusion-understanding-issues-ftc-reportGoogle Scholar
- Kimberle Crenshaw. 1989. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. U. Chi. Legal F. (1989), 139.Google Scholar
- Black Desi. 2009. HP computers are racist. (2009). https:/www.youtube.com/watch?v=t4DT3tQqgRMGoogle Scholar
- William Dieterich, Christina Mendoza, and Tim Brennan. 2016. COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity. (2016). https:/www.documentcloud.org/documents/2998391-ProPublica-Commentary-Final-070616.htmlGoogle Scholar
- Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2018. Measuring and Mitigating Unintended Bias in Text Classification. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (2018).Google ScholarDigital Library
- Cynthia Dwork. 2008. Differential Privacy: A Survey of Results. In Theory and Applications of Models of Computation, Manindra Agrawal, Dingzhu Du, Zhenhua Duan, and Angsheng Li (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 1--19. Google ScholarDigital Library
- Entelo. 2018. Recruitment Software | Entelo. (2018). https:/www.entelo.com/Google Scholar
- Daniel Faggella. 2018. Follow the Data: Deep Learning Leads the Transformation of Enterprise - A Conversation with Naveen Rao. (2018).Google Scholar
- Thomas B Fitzpatrick. 1988. The validity and practicality of sun-reactive skin types I through VI. Archives of dermatology 124, 6 (1988), 869--871.Google Scholar
- Food and Drug Administration. 1989. Guidance for the Study of Drugs Likely to Be Used in the Elderly. (1989).Google Scholar
- U.S. Food and Drug Administration. 2013. FDA Drug Safety Communication: Risk of next-morning impairment after use of insomnia drugs; FDA requires lower recommended doses for certain drugs containing Zolpidem (Ambien, Ambien CR, Edluar, and Zolpimist). (2013). https://web.archive.org/web/20170428150213/ https:/www.fda.gov/drugs/drugsafety/ucm352085.htmGoogle Scholar
- IIHS (Insurance Institute for Highway Safety: Highway Loss Data Institute). 2003. Special Issue: Side Impact Crashworthiness. Status Report 38, 7 (2003).Google Scholar
- Institute for the Future, Omidyar Network's Tech, and Society Solutions Lab. 2018. Ethical OS. (2018). https://ethicalos.org/Google Scholar
- Clare Garvie, Alvaro Bedoya, and Jonathan Frankle. 2016. The Perpetual Line-Up. (2016). https:/www.perpetuallineup.org/Google Scholar
- Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, and Kate Crawford. 2018. Datasheets for Datasets. CoRR abs/1803.09010 (2018). http://arxiv.org/abs/1803.09010Google Scholar
- Google. 2018. Responsible AI Practices. (2018). https://ai.google/education/responsible-ai-practicesGoogle Scholar
- Gooru. 2018. Navigator for Teachers. (2018). http://gooru.org/about/teachersGoogle Scholar
- Cyril Goutte and Eric Gaussier. 2005. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In European Conference on Information Retrieval. Springer, 345--359. Google ScholarDigital Library
- Collins GS, Reitsma JB, Altman DG, and Moons KM. 2015. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod): The tripod statement. Annals of Internal Medicine 162, 1 (2015), 55--63.Google ScholarCross Ref
- Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of Opportunity in Supervised Learning. In Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett (Eds.). Curran Associates, Inc., 3315--3323. http://papers.nips.cc/paper/6374-equality-of-opportunity-in-supervised-learning.pdf Google ScholarDigital Library
- Michael Hind, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, and Kush R. Varshney. 2018. Increasing Trust in AI Services through Supplier's Declarations of Conformity. CoRR abs/1808.07261 (2018).Google Scholar
- Sarah Holland, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia Chmielinski. 2018. The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards. CoRR abs/1805.03677 (2018). http://arxiv.org/abs/1805.03677Google Scholar
- Ideal. 2018. AI For Recruiting Software | Talent Intelligence for High-Volume Hiring. (2018). https://ideal.com/Google Scholar
- DrivenData Inc. 2018. An Ethics Checklist for Data Scientists. (2018). http://deon.drivendata.org/Google Scholar
- Jigsaw. 2017. Conversation AI Research. (2017). https://conversationai.github.io/Google Scholar
- Jigsaw. 2017. Perspective API. (2017). https:/www.perspectiveapi.com/Google Scholar
- B. Kim, Wattenberg M., J. Gilmer, Cai C., Wexler J., F. Viegas, and R. Sayres. 2018. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). ICML (2018).Google Scholar
- Brendan F. Klare, Mark J. Burge, Joshua C. Klontz, Richard W. Vorder Bruegge, and Anil K. Jain. 2012. Face recognition performance: Role of demographic information. IEEE Transactions on Information Forensics and Security 7, 6 (2012), 1789--1801. Google ScholarDigital Library
- Der-Chiang Li, Susan C Hu, Liang-Sian Lin, and Chun-Wu Yeh. 2017. Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets. PloS one 12, 8 (2017), e0181853. Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, FAT* '19, January 29-31, 2019, Atlanta, CA, USA Timnit GebruGoogle ScholarCross Ref
- Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015. Deep Learning Face Attributes in the Wild. In Proceedings of International Conference on Computer Vision (ICCV). Google ScholarDigital Library
- Shira Mitchell, Eric Potash, and Solon Barocas. 2018. Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions. arXiv:1811.07867 (2018).Google Scholar
- Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, and Kedar Dhamdhere. 2018. Did the Model Understand the Question? Proceedings of the Association for Computational Linguistics (2018).Google ScholarCross Ref
- AI Now. 2018. Litigating Algorithms: Challenging Government Use Of Algorithmic Decision Systems. AI Now Institute.Google Scholar
- Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 311--318. Google ScholarDigital Library
- Inioluwa Raji. 2018. Black Panther Face Scorecard: Wakandans Under the Coded Gaze of AI. (2018).Google Scholar
- Microsoft Research. 2018. Project InnerEye - Medical Imaging AI to Empower Clinicians. (2018). https:/www.microsoft.com/en-us/research/project/medical-image-analysis/Google Scholar
- Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. In Proceedings of the 34th International Conference on Machine Learning, Vol. 70. PMLR, Sydney, Australia.Google Scholar
- Digital Reasoning Systems. 2018. AI-Enabled Cancer Software | Healthcare AI: Digital Reasoning. (2018). https://digitalreasoning.com/solutions/healthcare/Google Scholar
- Turnitin. 2018. Revision Assistant. (2018). http://turnitin.com/en_us/what-we-offer/revision-assistantGoogle Scholar
- Shannon Vallor, Brian Green, and Irina Raicu. 2018. Ethics in Technology Practice: An Overview. (22 6 2018). https:/www.scu.edu/ethics-in-technology-practice/overview-of-ethics-in-tech-practice/Google Scholar
- Lucy Vasserman, John Li, CJ Adams, and Lucas Dixon. 2018. Unintended bias and names of frequently targeted groups. Medium (2018). https://medium.com/the-false-positive/unintended-bias-and-names-of-frequently-targeted-groups-8e0b81f80a23Google Scholar
- Sahil Verma and Julia Rubin. 2018. Fairness Definitions Explained. (2018).Google Scholar
- Joz Wang. 2010. Flickr Image. (2010). https:/www.flickr.com/photos/jozjozjoz/3529106844Google Scholar
- Amy Westervelt. 2018. The medical research gender gap: how excluding women from clinical trials is hurting our health. (2018).Google Scholar
- Mingyuan Zhou, Haiting Lin, S Susan Young, and Jingyi Yu. 2018. Hybrid sensing face detection and registration for low-light and unconstrained conditions. Applied optics 57, 1 (2018), 69--78.Google Scholar
Index Terms
- Model Cards for Model Reporting
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