sentence-transformers How to use rgtlai/ai-policy-ft with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("rgtlai/ai-policy-ft")
sentences = [
"What measures should be taken to ensure that automated systems are safe and effective before deployment?",
" AI BILL OF RIGHTS\nFFECTIVE SYSTEMS\nineffective systems. Automated systems should be \ncommunities, stakeholders, and domain experts to identify \nSystems should undergo pre-deployment testing, risk \nthat demonstrate they are safe and effective based on \nincluding those beyond the intended use, and adherence to \nprotective measures should include the possibility of not \nAutomated systems should not be designed with an intent \nreasonably foreseeable possibility of endangering your safety or the safety of your community. They should \nstemming from unintended, yet foreseeable, uses or \n \n \n \n \n \n \n \nSECTION TITLE\nBLUEPRINT FOR AN\nSAFE AND E \nYou should be protected from unsafe or \ndeveloped with consultation from diverse \nconcerns, risks, and potential impacts of the system. \nidentification and mitigation, and ongoing monitoring \ntheir intended use, mitigation of unsafe outcomes \ndomain-specific standards. Outcomes of these \ndeploying the system or removing a system from use. \nor \nbe designed to proactively protect you from harms \nimpacts of automated systems. You should be protected from inappropriate or irrelevant data use in the \ndesign, development, and deployment of automated systems, and from the compounded harm of its reuse. \nIndependent evaluation and reporting that confirms that the system is safe and effective, including reporting of \nsteps taken to mitigate potential harms, should be performed and the results made public whenever possible. \nALGORITHMIC DISCRIMINATION PROTECTIONS\nYou should not face discrimination by algorithms and systems should be used and designed in \nan equitable way. Algorithmic discrimination occurs when automated systems contribute to unjustified \ndifferent treatment or impacts disfavoring people based on their race, color, ethnicity, sex (including \npregnancy, childbirth, and related medical conditions, gender identity, intersex status, and sexual \norientation), religion, age, national origin, disability, veteran status, genetic information, or any other \nclassification protected by law. Depending on the specific circumstances, such algorithmic discrimination \nmay violate legal protections. Designers, developers, and deployers of automated systems should take \nproactive \nand \ncontinuous \nmeasures \nto \nprotect \nindividuals \nand \ncommunities \nfrom algorithmic \ndiscrimination and to use and design systems in an equitable way. This protection should include proactive \nequity assessments as part of the system design, use of representative data and protection against proxies \nfor demographic features, ensuring accessibility for people with disabilities in design and development, \npre-deployment and ongoing disparity testing and mitigation, and clear organizational oversight. Independent \nevaluation and plain language reporting in the form of an algorithmic impact assessment, including \ndisparity testing results and mitigation information, should be performed and made public whenever \npossible to confirm these protections. \n5\n",
"You should be protected from abusive data practices via built-in \nprotections and you should have agency over how data about \nyou is used. You should be protected from violations of privacy through \ndesign choices that ensure such protections are included by default, including \nensuring that data collection conforms to reasonable expectations and that \nonly data strictly necessary for the specific context is collected. Designers, de\nvelopers, and deployers of automated systems should seek your permission \nand respect your decisions regarding collection, use, access, transfer, and de\nletion of your data in appropriate ways and to the greatest extent possible; \nwhere not possible, alternative privacy by design safeguards should be used. \nSystems should not employ user experience and design decisions that obfus\ncate user choice or burden users with defaults that are privacy invasive. Con\nsent should only be used to justify collection of data in cases where it can be \nappropriately and meaningfully given. Any consent requests should be brief, \nbe understandable in plain language, and give you agency over data collection \nand the specific context of use; current hard-to-understand no\ntice-and-choice practices for broad uses of data should be changed. Enhanced \nprotections and restrictions for data and inferences related to sensitive do\nmains, including health, work, education, criminal justice, and finance, and \nfor data pertaining to youth should put you first. In sensitive domains, your \ndata and related inferences should only be used for necessary functions, and \nyou should be protected by ethical review and use prohibitions. You and your \ncommunities should be free from unchecked surveillance; surveillance tech\nnologies should be subject to heightened oversight that includes at least \npre-deployment assessment of their potential harms and scope limits to pro\ntect privacy and civil liberties. Continuous surveillance and monitoring \nshould not be used in education, work, housing, or in other contexts where the \nuse of such surveillance technologies is likely to limit rights, opportunities, or \naccess. Whenever possible, you should have access to reporting that confirms \nyour data decisions have been respected and provides an assessment of the \npotential impact of surveillance technologies on your rights, opportunities, or \naccess. \nDATA PRIVACY\n30\n",
"APPENDIX\nLisa Feldman Barrett \nMadeline Owens \nMarsha Tudor \nMicrosoft Corporation \nMITRE Corporation \nNational Association for the \nAdvancement of Colored People \nLegal Defense and Educational \nFund \nNational Association of Criminal \nDefense Lawyers \nNational Center for Missing & \nExploited Children \nNational Fair Housing Alliance \nNational Immigration Law Center \nNEC Corporation of America \nNew America’s Open Technology \nInstitute \nNew York Civil Liberties Union \nNo Name Provided \nNotre Dame Technology Ethics \nCenter \nOffice of the Ohio Public Defender \nOnfido \nOosto \nOrissa Rose \nPalantir \nPangiam \nParity Technologies \nPatrick A. Stewart, Jeffrey K. \nMullins, and Thomas J. Greitens \nPel Abbott \nPhiladelphia Unemployment \nProject \nProject On Government Oversight \nRecording Industry Association of \nAmerica \nRobert Wilkens \nRon Hedges \nScience, Technology, and Public \nPolicy Program at University of \nMichigan Ann Arbor \nSecurity Industry Association \nSheila Dean \nSoftware & Information Industry \nAssociation \nStephanie Dinkins and the Future \nHistories Studio at Stony Brook \nUniversity \nTechNet \nThe Alliance for Media Arts and \nCulture, MIT Open Documentary \nLab and Co-Creation Studio, and \nImmerse \nThe International Brotherhood of \nTeamsters \nThe Leadership Conference on \nCivil and Human Rights \nThorn \nU.S. Chamber of Commerce’s \nTechnology Engagement Center \nUber Technologies \nUniversity of Pittsburgh \nUndergraduate Student \nCollaborative \nUpturn \nUS Technology Policy Committee \nof the Association of Computing \nMachinery \nVirginia Puccio \nVisar Berisha and Julie Liss \nXR Association \nXR Safety Initiative \n• As an additional effort to reach out to stakeholders regarding the RFI, OSTP conducted two listening sessions\nfor members of the public. The listening sessions together drew upwards of 300 participants. The Science and\nTechnology Policy Institute produced a synopsis of both the RFI submissions and the feedback at the listening\nsessions.115\n61\n"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
