Fix link (#1087)

Kept getting requests for a WIP google doc, and could not figure out where they were coming from -- realized that the doc was still linked to here, and so needed to be fixed.

docs/hub/model-card-landscape-analysis.md

@@ -25,7 +25,7 @@ These groupings are not mutually exclusive; they do include overlapping aspects
 | DATA                             	| ***Dataset Development Lifecycle Documentation Framework*** [(Hutchinson et al., 2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445918)                        	| “We introduce a rigorous framework for dataset development transparency that supports decision-making and accountability. The framework uses the cyclical, infrastructural and engineering nature of dataset development to draw on best practices from the software development lifecycle.”                                                                                                         	| See [(Hutchinson et al., 2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445918), Appendix A for templates                                                                    	|
 | DATA                             	| ***Data Cards*** [(Pushkarna et al., 2021)](https://huggingface.co/papers/2204.01075)                                                                                  	| “Data Cards are structured summaries of essential facts about various aspects of ML datasets needed by stakeholders across a dataset’s lifecycle for responsible AI development. These summaries provide explanations of processes and rationales that shape the data and consequently the models.”                                                                                                  	| See the [Data Cards Playbook github](https://github.com/PAIR-code/datacardsplaybook/)                                                                                            	|
 | DATA                             	| ***CrowdWorkSheets***  [(Díaz et al., 2022)](https://huggingface.co/papers/2206.08931)                                                         	| “We introduce a novel framework, CrowdWorkSheets, for dataset developers to facilitate transparent documentation of key decisions points at various stages of the data annotation pipeline: task formulation, selection of annotators, plat- form and infrastructure choices, dataset analysis and evaluation, and dataset release and maintenance.”                                                 	| See [(Díaz et al., 2022)](hhttps://huggingface.co/papers/2206.08931)                                                                                          	|
-| MODELS AND METHODS               	| ***Model Cards*** [Mitchell et al. (2018)](https://huggingface.co/papers/1810.03993)                                                                                   	| “Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions…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.”                                	| See https://huggingface.co/models (and [Model Card Examples](https://docs.google.com/document/d/1qapo3LEfRatHmyZYwklopeitIZiaEC3p1WX30c6CeP4/edit#heading=h.xvsvrztjqdod) below) 	|
+| MODELS AND METHODS               	| ***Model Cards*** [Mitchell et al. (2018)](https://huggingface.co/papers/1810.03993)                                                                                   	| “Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions…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.”                                	| See https://huggingface.co/models, the [Model Card Guidebook](https://huggingface.co/docs/hub/model-card-guidebook), and [Model Card Examples](https://huggingface.co/docs/hub/model-card-appendix#model-card-examples) |
 | MODELS AND METHODS               	| ***Value Cards*** [Shen et al. (2021)](https://dl.acm.org/doi/abs/10.1145/3442188.3445971)                                                                         	| “We present Value Cards, a deliberation-driven toolkit for bringing computer science students and practitioners the awareness of the social impacts of machine learning-based decision making systems….Value Cards encourages the investigations and debates towards different ML performance metrics and their potential trade-offs.”                                                               	| See [Shen et al. (2021)](https://dl.acm.org/doi/abs/10.1145/3442188.3445971), Section 3.3                                                                                        	|
 | MODELS AND METHODS               	| ***Method Cards*** [Adkins et al. (2022)](https://dl.acm.org/doi/pdf/10.1145/3491101.3519724)                                                                      	| “We propose method cards to guide ML engineers through the process of model development…The information comprises both prescriptive and descriptive elements, putting the main focus on ensuring that ML engineers are able to use these methods properly.”                                                                                                                                          	| See [Adkins et al. (2022)](https://dl.acm.org/doi/pdf/10.1145/3491101.3519724), Appendix A                                                                                       	|
 | MODELS AND METHODS               	| ***Consumer Labels for ML Models*** [Seifert et al. (2019)](https://ris.utwente.nl/ws/portalfiles/portal/158031484/Seifert2019_cogmi_consumer_labels_preprint.pdf) 	| “We propose to issue consumer labels for trained and published ML models. These labels primarily target machine learning lay persons, such as the operators of an ML system, the executors of decisions, and the decision subjects themselves”                                                                                                                                                       	| See [Seifert et al. (2019)](https://ris.utwente.nl/ws/portalfiles/portal/158031484/Seifert2019_cogmi_consumer_labels_preprint.pdf)                                               	|