The battle over right to repair is a fight over your car’s data

Curated on Posted on by Stefaan Verhulst

Article by Ofer Tur-Sinai: “Cars are no longer just a means of transportation. They have become rolling hubs of data communication. Modern vehicles regularly transmit information wirelessly to their manufacturers.

However, as cars grow “smarter,” the right to repair them is under siege.

As legal scholars, we find that the question of whether you and your local mechanic can tap into your car’s data to diagnose and repair spans issues of property rights, trade secrets, cybersecurity, data privacy and consumer rights. Policymakers are forced to navigate this complex legal landscape and ideally are aiming for a balanced approach that upholds the right to repair, while also ensuring the safety and privacy of consumers…

Until recently, repairing a car involved connecting to its standard on-board diagnostics port to retrieve diagnostic data. The ability for independent repair shops – not just those authorized by the manufacturer – to access this information was protected by a state law in Massachusetts, approved by voters on Nov. 6, 2012, and by a nationwide memorandum of understanding between major car manufacturers and the repair industry signed on Jan. 15, 2014.

However, with the rise of telematics systems, which combine computing with telecommunications, these dynamics are shifting. Unlike the standardized onboard diagnostics ports, telematics systems vary across car manufacturers. These systems are often protected by digital locks, and circumventing these locks could be considered a violation of copyright law. The telematics systems also encrypt the diagnostic data before transmitting it to the manufacturer.

This reduces the accessibility of telematics information, potentially locking out independent repair shops and jeopardizing consumer choice – a lack of choice that can lead to increased costs for consumers….

One issue left unresolved by the legislation is the ownership of vehicle data. A vehicle generates all sorts of data as it operates, including location, diagnostic, driving behavior, and even usage patterns of in-car systems – for example, which apps you use and for how long.

In recent years, the question of data ownership has gained prominence. In 2015, Congress legislated that the data stored in event data recorders belongs to the vehicle owner. This was a significant step in acknowledging the vehicle owner’s right over specific datasets. However, the broader issue of data ownership in today’s connected cars remains unresolved…(More)”.

Democratic Policy Development using Collective Dialogues and AI

Curated on Posted on by Stefaan Verhulst

Paper by Andrew Konya, Lisa Schirch, Colin Irwin, Aviv Ovadya: “We design and test an efficient democratic process for developing policies that reflect informed public will. The process combines AI-enabled collective dialogues that make deliberation democratically viable at scale with bridging-based ranking for automated consensus discovery. A GPT4-powered pipeline translates points of consensus into representative policy clauses from which an initial policy is assembled. The initial policy is iteratively refined with the input of experts and the public before a final vote and evaluation. We test the process three times with the US public, developing policy guidelines for AI assistants related to medical advice, vaccine information, and wars & conflicts. We show the process can be run in two weeks with 1500+ participants for around $10,000, and that it generates policy guidelines with strong public support across demographic divides. We measure 75-81% support for the policy guidelines overall, and no less than 70-75% support across demographic splits spanning age, gender, religion, race, education, and political party. Overall, this work demonstrates an end-to-end proof of concept for a process we believe can help AI labs develop common-ground policies, governing bodies break political gridlock, and diplomats accelerate peace deals…(More)”.

Assessing and Suing an Algorithm

Curated on Posted on by Stefaan Verhulst

Report by Elina Treyger, Jirka Taylor, Daniel Kim, and Maynard A. Holliday: “Artificial intelligence algorithms are permeating nearly every domain of human activity, including processes that make decisions about interests central to individual welfare and well-being. How do public perceptions of algorithmic decisionmaking in these domains compare with perceptions of traditional human decisionmaking? What kinds of judgments about the shortcomings of algorithmic decisionmaking processes underlie these perceptions? Will individuals be willing to hold algorithms accountable through legal channels for unfair, incorrect, or otherwise problematic decisions?

Answers to these questions matter at several levels. In a democratic society, a degree of public acceptance is needed for algorithms to become successfully integrated into decisionmaking processes. And public perceptions will shape how the harms and wrongs caused by algorithmic decisionmaking are handled. This report shares the results of a survey experiment designed to contribute to researchers’ understanding of how U.S. public perceptions are evolving in these respects in one high-stakes setting: decisions related to employment and unemployment…(More)”.

Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias

Curated on Posted on by Stefaan Verhulst

Paper by S. Lee et all: “Large language models (LLMs) have demonstrated their potential in social science research by emulating human perceptions and behaviors, a concept referred to as algorithmic fidelity. This study assesses the algorithmic fidelity and bias of LLMs by utilizing two nationally representative climate change surveys. The LLMs were conditioned on demographics and/or psychological covariates to simulate survey responses. The findings indicate that LLMs can effectively capture presidential voting behaviors but encounter challenges in accurately representing global warming perspectives when relevant covariates are not included. GPT-4 exhibits improved performance when conditioned on both demographics and covariates. However, disparities emerge in LLM estimations of the views of certain groups, with LLMs tending to underestimate worry about global warming among Black Americans. While highlighting the potential of LLMs to aid social science research, these results underscore the importance of meticulous conditioning, model selection, survey question format, and bias assessment when employing LLMs for survey simulation. Further investigation into prompt engineering and algorithm auditing is essential to harness the power of LLMs while addressing their inherent limitations…(More)”.

Can Indigenous knowledge and Western science work together? New center bets yes

Curated on Posted on by Stefaan Verhulst

Article by Jeffrey Mervis: “For millennia, the Passamaquoddy people used their intimate understanding of the coastal waters along the Gulf of Maine to sustainably harvest the ocean’s bounty. Anthropologist Darren Ranco of the University of Maine hoped to blend their knowledge of tides, water temperatures, salinity, and more with a Western approach in a project to study the impact of coastal pollution on fish, shellfish, and beaches.

But the Passamaquoddy were never really given a seat at the table, says Ranco, a member of the Penobscot Nation, which along with the Passamaquoddy are part of the Wabanaki Confederacy of tribes in Maine and eastern Canada. The Passamaquoddy thought water quality and environmental protection should be top priority; the state emphasized forecasting models and monitoring. “There was a disconnect over who were the decision-makers, what knowledge would be used in making decisions, and what participation should look like,” Ranco says about the 3-year project, begun in 2015 and funded by the National Science Foundation (NSF).

Last month, NSF aimed to bridge such disconnects, with a 5-year, $30 million grant designed to weave together traditional ecological knowledge (TEK) and Western science. Based at the University of Massachusetts (UMass) Amherst, the Center for Braiding Indigenous Knowledges and Science (CBIKS) aims to fundamentally change the way scholars from both traditions select and carry out joint research projects and manage data…(More)”.

A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data

Curated on Posted on by Stefaan Verhulst

Report by Andrés F. Barrientos, Aaron R. Williams, Joshua Snoke, Claire McKay Bowen: “Federal administrative data, such as tax data, are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An alternative to sharing microlevel data is to allow individuals to query statistics without directly accessing the confidential data. This paper studies the feasibility of using differentially private (DP) methods to make certain queries while preserving privacy. We also include new methodological adaptations to existing DP regression methods for using new data types and returning standard error estimates. We define feasibility as the impact of DP methods on analyses for making public policy decisions and the queries accuracy according to several utility metrics. We evaluate the methods using Internal Revenue Service data and public-use Current Population Survey data and identify how specific data features might challenge some of these methods. Our findings show that DP methods are feasible for simple, univariate statistics but struggle to produce accurate regression estimates and confidence intervals. To the best of our knowledge, this is the first comprehensive statistical study of DP regression methodology on real, complex datasets, and the findings have significant implications for the direction of a growing research field and public policy…(More)”.

Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

The White House: “Today, President Biden is issuing a landmark Executive Order to ensure that America leads the way in seizing the promise and managing the risks of artificial intelligence (AI). The Executive Order establishes new standards for AI safety and security, protects Americans’ privacy, advances equity and civil rights, stands up for consumers and workers, promotes innovation and competition, advances American leadership around the world, and more.

As part of the Biden-Harris Administration’s comprehensive strategy for responsible innovation, the Executive Order builds on previous actions the President has taken, including work that led to voluntary commitments from 15 leading companies to drive safe, secure, and trustworthy development of AI…(More)”.

Predictive Policing Software Terrible At Predicting Crimes

Curated on Posted on by Stefaan Verhulst

Article by Aaron Sankin and Surya Mattu: “A software company sold a New Jersey police department an algorithm that was right less than 1% of the time

Crime predictions generated for the police department in Plainfield, New Jersey, rarely lined up with reported crimes, an analysis by The Markup has found, adding new context to the debate over the efficacy of crime prediction software.

Geolitica, known as PredPol until a 2021 rebrand, produces software that ingests data from crime incident reports and produces daily predictions on where and when crimes are most likely to occur.

We examined 23,631 predictions generated by Geolitica between Feb. 25 to Dec. 18, 2018 for the Plainfield Police Department (PD). Each prediction we analyzed from the company’s algorithm indicated that one type of crime was likely to occur in a location not patrolled by Plainfield PD. In the end, the success rate was less than half a percent. Fewer than 100 of the predictions lined up with a crime in the predicted category, that was also later reported to police.

Diving deeper, we looked at predictions specifically for robberies or aggravated assaults that were likely to occur in Plainfield and found a similarly low success rate: 0.6 percent. The pattern was even worse when we looked at burglary predictions, which had a success rate of 0.1 percent.

“Why did we get PredPol? I guess we wanted to be more effective when it came to reducing crime. And having a prediction where we should be would help us to do that. I don’t know that it did that,” said Captain David Guarino of the Plainfield PD. “I don’t believe we really used it that often, if at all. That’s why we ended up getting rid of it.”…(More)’.

Facilitating Data Flows through Data Collaboratives

Curated on Posted on by Stefaan Verhulst

A Practical Guide “to Designing Valuable, Accessible, and Responsible Data Collaboratives” by Uma Kalkar, Natalia González Alarcón, Arturo Muente Kunigami and Stefaan Verhulst: “Data is an indispensable asset in today’s society, but its production and sharing are subject to well-known market failures. Among these: neither economic nor academic markets efficiently reward costly data collection and quality assurance efforts; data providers cannot easily supervise the appropriate use of their data; and, correspondingly, users have weak incentives to pay for, acknowledge, and protect data that they receive from providers. Data collaboratives are a potential non-market solution to this problem, bringing together data providers and users to address these market failures. The governance frameworks for these collaboratives are varied and complex and their details are not widely known. This guide proposes a methodology and a set of common elements that facilitate experimentation and creation of collaborative environments. It offers guidance to governments on implementing effective data collaboratives as a means to promote data flows in Latin America and the Caribbean, harnessing their potential to design more effective services and improve public policies…(More)”.

Artificial Intelligence and the Labor Force

Curated on Posted on by Stefaan Verhulst

Report by by Tobias Sytsma, and Éder M. Sousa: “The rapid development of artificial intelligence (AI) has the potential to revolutionize the labor force with new generative AI tools that are projected to contribute trillions of dollars to the global economy by 2040. However, this opportunity comes with concerns about the impact of AI on workers and labor markets. As AI technology continues to evolve, there is a growing need for research to understand the technology’s implications for workers, firms, and markets. This report addresses this pressing need by exploring the relationship between occupational exposure and AI-related technologies, wages, and employment.

Using natural language processing (NLP) to identify semantic similarities between job task descriptions and U.S. technology patents awarded between 1976 and 2020, the authors evaluate occupational exposure to all technology patents in the United States, as well as to specific AI technologies, including machine learning, NLP, speech recognition, planning control, AI hardware, computer vision, and evolutionary computation.

The authors’ findings suggest that exposure to both general technology and AI technology patents is not uniform across occupational groups, over time, or across technology categories. They estimate that up to 15 percent of U.S. workers were highly exposed to AI technology patents by 2019 and find that the correlation between technology exposure and employment growth can depend on the routineness of the occupation. This report contributes to the growing literature on the labor market implications of AI and provides insights that can inform policy discussions around this emerging issue…(More)”