Does every tool have to be certified as compliant?
U. of Iowa Issues Reminder About Use of artificial technology (AI) and HIPAA
From the U. of Iowa, and kudos to them for educating and warning their employees:
As artificial intelligence (AI) continues to evolve, it is important to remember that most AI tools and services, including ChatGPT, are not HIPAA compliant. Therefore, it is not appropriate to use these tools or services in conjunction with patient protected health information (PHI).
In order to use application that processes, transmits, or stores patient information, such as an AI service, a proper security review, contracting, and business associate agreement must be completed. If you have a request to be able to use an AI system, please speak with your department director on how to pursue the request and initiate a security review.
Imputing patient information into an AI system could result in a HIPAA violation if the above conditions have not been met. For example, using ChatGPT to draft a patient letter or using an unapproved AI transcription service requires sharing of PHI with the application. Beware of these types of situations.
For additional information, please see IT Security Guidelines for the secure and ethical use of Artificial Intelligence.
Please contact the Joint Office for Compliance with questions at compliance[at]healthcare.uiowa.edu or 319-384-8282.
Watch the right hand while the left does something completely unexpected...
https://www.wired.com/story/meta-artificial-intelligence-data-deletion/
Artists Allege Meta’s AI Data Deletion Request Process Is a ‘Fake PR Stunt’
This summer, Meta began taking requests to delete data from its AI training. Artists say this new system is broken and fake. Meta says there is no opt-out program.
Why do I get the feeling that this might be “biased” in the UK’s favor?
Sunak to launch AI chatbot for Britons to pay taxes and access pensions
Rishi Sunak is planning to launch an AI chatbot to help the public pay taxes and access pensions in what would be the biggest use of advanced artificial intelligence by Whitehall to date...
Is it possible to gather/identify exculpatory evidence at the same time? Yes, Bob was in the area, but it looks like he was just driving through.
https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/pra2.835
Geofence Warrants, Geospatial Innovation, and Implications for Data Privacy
Geospatial technologies collect, analyze, and produce information about earth, humans, and objects through a convergence of geographic information systems, remote sensors, and global positioning systems. A microanalysis of Google's U.S. Patent 9,420,426 Inferring a current location based on a user location history (Duleba et al., 2016) reveals how geospatial innovation employs artificial intelligence (AI) to train computer-vision models, infer, and impute geospatial data. The technical disclosures in patents offer a view within black-boxed digital technologies to examine potential privacy implications of datafied citizens in a networked society. I n patented geospatial innovation, user agency is subverted through AI and anonymous knowledge production.
Presently, the Fourth Amendment does not adequately protect citizens in a networked society. Data privacy legal cases are interpreted through a lens of inescapability (Tokson, 2020), which assumes perpetual agency to consent to sharing data. In short, agency-centered privacy models are insufficient where AI can anonymously produce knowledge about an individual. Privacy implications are exemplified in geofence warrants—an investigative technique that searches location history to identify suspects in a geofenced region in the absence of evidence. This analysis demonstrates that digital privacy rights must expand to datafication models (Mai, 2016) centered on knowledge production.
Another step on the long road to eliminate lawyers?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4582753
Generative Contracts
This Article examines how consumers can use generative artificial intelligence to write their own contracts. Popularized by “chatbots” such as OpenAI’s ChatGPT, generative AI is a form of artificial intelligence that uses statistical models trained on massive amounts of data to generate human-like content such as text, images, music, and more. Generative AI is already being integrated into the practice of law and the legal profession. In the context of contracting and transactional law, most generative AI tools are focused on reviewing and managing large volumes of business contracts. Thus far, little attention has been given to using generative AI to create entire contracts from scratch. This Article aims to fill this gap by exploring the use of “generative contracts”: contracts that are written entirely by a generative AI system based on prompts from the user. For example, a user could ask a generative AI model to, “Write me a contract to sell my used car.” The Article uses OpenAI’s GPT-4 to generate drafts of a wide range of contracts from an employment agreement to a residential lease to a bill of sale. While relatively simple, the contracts written by GPT-4 are functional and enforceable. These results suggest that generative contracts present an opportunity to improve access to justice for consumers who are currently underserved by the legal system. To examine how consumers might use generative contracts in practice, the Article engages in a proof-of-concept case study of two hypothetical consumers who use GPT-4 to write and modify their own car sale contract. Drawing on this case study, the Article analyzes the implications of generative contracts for consumers, lawyers, and the practice of law. While generative AI holds great promise for consumers and access to justice, it threatens to disrupt the legal profession and poses numerous technological, privacy, and regulatory challenges. The Article maps the benefits and risks of generative contracts as the world approaches a future of automated contracting.
What’s in your dataset? Would corrections change your ‘reality?’
https://arxiv.org/abs/2310.15848
On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms
Artificial Intelligence (AI) has made its way into various scientific fields, providing astonishing improvements over existing algorithms for a wide variety of tasks. In recent years, there have been severe concerns over the trustworthiness of AI technologies. The scientific community has focused on the development of trustworthy AI algorithms. However, machine and deep learning algorithms, popular in the AI community today, depend heavily on the data used during their development. These learning algorithms identify patterns in the data, learning the behavioral objective. Any flaws in the data have the potential to translate directly into algorithms. In this study, we discuss the importance of Responsible Machine Learning Datasets and propose a framework to evaluate the datasets through a responsible rubric. While existing work focuses on the post-hoc evaluation of algorithms for their trustworthiness, we provide a framework that considers the data component separately to understand its role in the algorithm. We discuss responsible datasets through the lens of fairness, privacy, and regulatory compliance and provide recommendations for constructing future datasets. After surveying over 100 datasets, we use 60 datasets for analysis and demonstrate that none of these datasets is immune to issues of fairness, privacy preservation, and regulatory compliance. We provide modifications to the “datasheets for datasets" with important additions for improved dataset documentation. With governments around the world regularizing data protection laws, the method for the creation of datasets in the scientific community requires revision. We believe this study is timely and relevant in today's era of AI.
Perspective.
https://link.springer.com/chapter/10.1007/978-981-99-6327-0_8
Issues that May Arise from Usage of AI Technologies in Criminal Justice and Law Enforcement
Due to the constant and swift technological advancements, artificial intelligence technologies have become an integral part of our daily lives and as a result, have started to impact various areas of our society. Legal systems proved to be no exception as many countries took steps to implement AI technologies to their legal systems in order to improve the law enforcement and criminal justice systems, making changes in various processes including but not limited to preventing crimes, locating perpetrators, accelerating judicial processes, and improving the accuracy of judicial decisions. While the usage of AI technologies provided improvements to criminal justice and law enforcement processes in various aspects, concerning instances demonstrated that AI technologies may reach to biased, discriminatory, or simply inaccurate conclusions that may cause harm to people. This realization becomes even more alarming considering that criminal justice and law enforcement consist of extremely critical and fragile processes where a wrong decision may cost someone their freedom, or in some cases, life. In addition to discrimination and bias, automated decision-making processes also have a number of other issues such as lack of transparency and accountability, jeopardization of the presumption of innocence principle, and concerns regarding personal data protection, cyber-attacks, and technical challenges. Implementing AI technologies to legal processes should be encouraged since criminal justice and law enforcement could benefit from recent advancements in technology and it is possible that more accurate, more just, and faster judicial processes can be created. However, it should be carefully considered that implementing AI systems which are in their infancy to legal processes that could lead to severe consequences may cause incredible and, in some cases, irrevocable damages. This study aims to address current and possible issues in usage of AI technologies in criminal justice and law enforcement, providing possible solutions when possible.
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