About the ABOUT ML ProjectABOUT ML (Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles) is a multi-year, multi-stakeholder initiative led by PAI. This initiative aims to bring together a diverse range of perspectives to develop, test, and implement machine learning system documentation practices at scale.The initiative is an ongoing, iterative process designed to co-evolve with the rapidly advancing field of AI development and deployment. In recognition that documentation is both an artifact and a process, ABOUT ML is structured into an artifact workstream and a process workstream. In 2020, ABOUT ML will produce one resource for each workstream ... [The workstreams are documented as goals in this StratML rendition.]Partnership on AIPAI_b060bec2-8979-11e6-8860-8c5d3831b3c2ABOUT ML Steering CommitteeThe ABOUT ML Steering Committee is comprised of around 30 experts, researchers and practitioners recruited from a diverse set of PAI Partner organizations. The Steering Committee guides the process of updating ABOUT ML drafts based on the public comments submitted and new developments in research and practice. They vote to approve new releases by “rough consensus” commonly used by other multi-stakeholder working groups. They convene 1-3 times a year, depending on the volume of proposed changes and velocity of change of research and practice. To allow for as many diverse perspectives as possible, PAI limits participation in the Steering Committee to up to 2 people per organization, with 1 vote per organization. As needed, PAI will periodically reopen applications to the Steering Committee and recruit more members.Norberto AndradePRIVACY AND PUBLIC POLICY MANAGER (FACEBOOK)Amir BanifatemiGENERAL MANAGER, INNOVATION & GROWTH (XPRIZE)Rachel BellamyPRINCIPAL RESEARCHER & MANAGER, HUMAN-AI COLLABORATION (IBM)Umang BhattSTUDENT FELLOW (LEVERHULME CENTRE FOR THE FUTURE OF INTELLIGENCE)Rumman ChowdhuryMANAGING DIRECTOR (ACCENTURE AI)Jacomo CorboCHIEF SCIENTIST (QUANTUMBLACK)Daniel FirstASSOCIATE / DATA SCIENTIST (MCKINSEY / QUANTUMBLACK)Ben GarfinkelRESEARCH FELLOW (FUTURE OF HUMANITY INSTITUTE)Jeremy GillulaTECH PROJECTS DIRECTOR (EFF)Brenda LeongSENIOR COUNSEL AND DIRECTOR OF STRATEGY (FUTURE OF PRIVACY FORUM)Tyler LiechtyDATA ENGINEER (DEEPMIND)Momin M. MalikDATA SCIENTIST (BERKMAN KLEIN CENTER)Lassana MagassaGRADUATE RESEARCH ASSOCIATE (TECH POLICY LAB/UNIVERSITY OF WASHINGTON)Meg MitchellRESEARCHER, ML FAIRNESS, ETHICAL AI (GOOGLE)Amanda NavarroMANAGING DIRECTOR (POLICYLINK)Deborah RajiTECH FELLOW (AI NOW)Nicole RigilloANTHROPOLOGIST (BERGGRUEN INSTITUTE)Andrew SelbstPOSTDOCTORAL SCHOLAR (DATA & SOCIETY)Ramya SethuramanPRODUCT MANAGER (FACEBOOK)Moninder SinghRESEARCH STAFF MEMBER (IBM)Amber SinhaSENIOR PROGRAMME MANAGER (CENTRE FOR INTERNET AND SOCIETY)Michael SprangerSENIOR RESEARCH SCIENTIST, AI COLLABORATION OFFICE (SONY)Andrew StraitRESEARCHER, ETHICS AND SOCIETY TEAM (DEEPMIND)Gabriel StraubHEAD OF DATA SCIENCE AND ARCHITECTURE (BBC)Michael VealeASSISTANT PROFESSOR (UCL)Hanna WallachSENIOR PRINCIPAL RESEARCHER (MICROSOFT)Adrian WellerSENIOR RESEARCH FELLOW (LEVERHULME CENTRE FOR THE FUTURE OF INTELLIGENCE)Abigail WenMANAGING COUNSEL, OFFICE OF THE CTO (INTEL)Alexander WongCO-DIRECTOR, VISION AND IMAGE PROCESSING (VIP) RESEARCH GROUP (UNIVERSITY OF WATERLOO)Jennifer Wortman VaughanPRINCIPAL RESEARCHER (MICROSOFT)Andrew ZaldivarSENIOR DEVELOPER ADVOCATE (GOOGLE)AML ContributorsOur goal for 2020 is to design testable pilots in a multi stakeholder manner. To make this process more tractable, we’ve broken this into two different workstreams... we invite you to share your thoughts, comments, and feedback on any that you are interested in.Responsible AI_db9d33de-aebb-11ea-9851-0cfb0a83ea00To develop, test, and implement machine learning system documentation practices at scale_db9d3550-aebb-11ea-9851-0cfb0a83ea00ResponsibilityAn ongoing multistakeholder initiative to enable responsible AI by increasing transparency and accountability with machine learning system documentation.DocumentationDocumentation for machine learning systems can contribute to responsible AI development by bringing more transparency into “black box” models and by bridging the gap between increasingly pervasive AI ethics principles and day-to-day operations and practice. Documentation can shape practice because by asking the right question at the right time in the AI development process, teams will become more likely to identify potential issues and take appropriate mitigating actions.TransparencyAccountabilityDatabaseCreate a database of documentation questions._db9d3622-aebb-11ea-9851-0cfb0a83ea001Artifact Workstream: A database of documentation questions, adapted by the domain of the machine learning applicationExamplesGather feedback on deployed examples._db9d373a-aebb-11ea-9851-0cfb0a83ea001.1See https://www.partnershiponai.org/about-ml/#:~:text=About%20the%20Project,stakeholder%20initiative%20led%20by%20PAI.QuestionsGather questions in a database._3af1202c-af19-11ea-8a04-488d1e83ea001.2Provide input at https://docs.google.com/spreadsheets/d/1HYlWBos7AXxV4TRvWkK_EF6iFFWNjk8NEevlh1JC9Bk/edit#gid=845089852ProcessSolve the challenge of how documentation can be created at scale within an organization. _3af12194-af19-11ea-8a04-488d1e83ea002Question database for comments -- https://docs.google.com/spreadsheets/d/1HYlWBos7AXxV4TRvWkK_EF6iFFWNjk8NEevlh1JC9Bk/edit#gid=845089852GuidanceDevelop a guide to initiating and scaling a documentation pilot._3af12252-af19-11ea-8a04-488d1e83ea002.1Process Workstream: A research-based guide to initiating and scaling a documentation pilot.Future Work_3af12342-af19-11ea-8a04-488d1e83ea003PilotsDesign ABOUT ML pilots._3af123ec-af19-11ea-8a04-488d1e83ea003.1TestingRun and test pilots._3af12496-af19-11ea-8a04-488d1e83ea003.2Implementation & ScalingImplement and scale what works._3af1254a-af19-11ea-8a04-488d1e83ea003.32020-06-15https://www.partnershiponai.org/about-ml/#:~:text=About%20the%20Project,stakeholder%20initiative%20led%20by%20PAI.OwenAmburOwen.Ambur@verizon.net