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)’.

Our Planet Powered by AI: How We Use Artificial Intelligence to Create a Sustainable Future for Humanity

Curated on Posted on by Stefaan Verhulst

Book by Mark Minevich: “…You’ll learn to create sustainable, effective competitive advantage by introducing previously unheard-of levels of adaptability, resilience, and innovation into your company.

Using real-world case studies from a variety of well-known industry leaders, the author explains the strategic archetypes, technological infrastructures, and cultures of sustainability you’ll need to ensure your firm’s next-level digital transformation takes root. You’ll also discover:

  • How AI can enable new business strategies, models, and ecosystems of innovation and growth
  • How to develop societal impact and powerful organizational benefits with ethical AI implementations that incorporate transparency, fairness, privacy, and reliability
  • What it means to enable all-inclusive artificial intelligence

An engaging and hands-on exploration of how to take your firm to new levels of dynamism and growth, Our Planet Powered by AI will earn a place in the libraries of managers, executives, directors, and other business and technology leaders seeking to distinguish their companies in a new age of astonishing technological advancement and fierce competition….(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)”

How Americans View Data Privacy

Curated on Posted on by Stefaan Verhulst

Pew Research: “…Americans – particularly Republicans – have grown more concerned about how the government uses their data. The share who say they are worried about government use of people’s data has increased from 64% in 2019 to 71% today. That reflects rising concern among Republicans (from 63% to 77%), while Democrats’ concern has held steady. (Each group includes those who lean toward the respective party.)

The public increasingly says they don’t understand what companies are doing with their data. Some 67% say they understand little to nothing about what companies are doing with their personal data, up from 59%.

Most believe they have little to no control over what companies or the government do with their data. While these shares have ticked down compared with 2019, vast majorities feel this way about data collected by companies (73%) and the government (79%).

We’ve studied Americans’ views on data privacy for years. The topic remains in the national spotlight today, and it’s particularly relevant given the policy debates ranging from regulating AI to protecting kids on social media. But these are far from abstract concepts. They play out in the day-to-day lives of Americans in the passwords they choose, the privacy policies they agree to and the tactics they take – or not – to secure their personal information. We surveyed 5,101 U.S. adults using Pew Research Center’s American Trends Panel to give voice to people’s views and experiences on these topics.

In addition to the key findings covered on this page, the three chapters of this report provide more detail on:

How to share data — not just equally, but equitably

Curated on Posted on by Stefaan Verhulst

Editorial in Nature: “Two decades ago, scientists asked more than 150,000 people living in Mexico City to provide medical data for research. Each participant gave time, blood and details of their medical history. For the researchers, who were based at the National Autonomous University of Mexico in Mexico City and the University of Oxford, UK, this was an opportunity to study a Latin American population for clues about factors contributing to disease and health. For the participants, it was a chance to contribute to science so that future generations might one day benefit from access to improved health care. Ultimately, the Mexico City Prospective Study was an exercise in trust — scientists were trusted with some of people’s most private information because they promised to use it responsibly.

Over the years, the researchers have repaid the communities through studies investigating the effects of tobacco and other risk factors on participants’ health. They have used the data to learn about the impact of diabetes on mortality rates, and they have found that rare forms of a gene called GPR75 lower the risk of obesity. And on 11 October, researchers added to the body of knowledge on the population’s ancestry.

But this project also has broader relevance — it can be seen as a model of trust and of how the power structures of science can be changed to benefit the communities closest to it.

Mexico’s population is genetically wealthy. With a complex history of migration and mixing of several populations, the country’s diverse genetic resources are valuable to the study of the genetic roots of diseases. Most genetic databases are stocked with data from people with European ancestry. If genomics is to genuinely benefit the global community — and especially under-represented groups — appropriately diverse data sets are needed. These will improve the accuracy of genetic tests, such as those for disease risk, and will make it easier to unearth potential drug targets by finding new genetic links to medical conditions…(More)”.

Generative AI is set to transform crisis management

Curated on Posted on by Stefaan Verhulst

Article by Ben Ellencweig, Mihir Mysore, Jon Spaner: “…Generative AI presents transformative potential, especially in disaster preparedness and response, and recovery. As billion-dollar disasters become more frequent – “billion-dollar disasters” typically costing the U.S. roughly $120 billion each – and “polycrises”, or multiple crises at once proliferate (e.g. hurricanes combined with cyber disruptions), the significant impact that Generative AI can have, especially with proper leadership focus, is a focal point of interest.

Generative AI’s speed is crucial in emergencies, as it enhances information access, decision-making capabilities, and early warning systems. Beyond organizational benefits for those who adopt Generative AI, its applications include real-time data analysis, scenario simulations, sentiment analysis, and simplifying complex information access. Generative AI’s versatility offers a wide variety of promising applications in disaster relief, and opens up facing real time analyses with tangible applications in the real world. 

Early warning systems and sentiment analysis: Generative AI excels in early warning systems and sentiment analysis, by scanning accurate real-time data and response clusters. By enabling connections between disparate systems, Generative AI holds the potential to provide more accurate early warnings. Integrated with traditional and social media, Generative AI can also offer precise sentiment analysis, empowering leaders to understand public sentiment, detect bad actors, identify misinformation, and tailor communications for accurate information dissemination.

Scenario simulations: Generative AI holds the potential to enhance catastrophe modeling for better crisis assessment and resource allocation. It creates simulations for emergency planners, improving modeling for various disasters (e.g., hurricanes, floods, wildfires) using historical data such as location, community impact, and financial consequence. Often, simulators perform work “so large that it exceeds human capacity (for example, finding flooded or unusable roads across a large area after a hurricane).” …(More)”

Evidence-Based Government Is Alive and Well

Curated on Posted on by Stefaan Verhulst

Article by Zina Hutton: “A desire to discipline the whimsical rule of despots.” That’s what Gary Banks, a former chairman of Australia’s Productivity Commission, attributed the birth of evidence-based policy to back in the 14th century in a speech from 2009. Evidence-based policymaking isn’t a new style of government, but it’s one with well-known roadblocks that elected officials have been working around in order to implement it more widely.

Evidence-based policymaking relies on evidence — facts, data, expert analysis — to shape aspects of long- and short-term policy decisions. It’s not just about collecting data, but also applying it and experts’ analysis to shape future policy. Whether it’s using school enrollment numbers to justify building a new park in a neighborhood or scientists collaborating on analysis of wastewater to try to “catch” illness spread in a community before it becomes unmanageable, evidence-based policy uses facts to help elected and appointed officials decide what funds and other resources to allocate in their communities.

Problems with evidence-based governing have been around for years. They range from a lack of communication between the people designing the policy and its related programs and the people implementing them, to the way that local government struggles to recruit and maintain employees. Resource allocation also shapes the decisions some cities make when it comes to seeking out and using data. This can be seen in the way larger cities, with access to proportionately larger budgets, research from state universities within city limits and a larger workforce, have had more success with evidence-based policymaking.
“The largest cities have more personnel, more expertise, more capacity, whether that’s for collecting administrative data and monitoring it, whether that’s doing open data portals, or dashboards, or whether that’s doing things like policy analysis or program evaluation,” says Karen Mossberger, the Frank and June Sackton Professor in the School of Public Affairs at Arizona State University. “It takes expert personnel, it takes people within government with the skills and the capacity, it takes time.”

Roadblocks aside, state and local governments are finding innovative ways to collaborate with one another on data-focused projects and policy, seeking ways to make up for the problems that impacted early efforts at evidence-based governance. More state and local governments now recruit data experts at every level to collect, analyze and explain the data generated by residents, aided by advances in technology and increased access to researchers…(More)”.

Who owns data about you?

Curated on Posted on by Stefaan Verhulst

Article by Wendy Wong: “The ascendancy of artificial intelligence hinges on vast data accrued from our daily activities. In turn, data train advanced algorithms, fuelled by massive amounts of computing power. Together, they form the critical trio driving AI’s capabilities. Because of its human sources, data raise an important question: who owns data, and how do the data add up when they’re about our mundane, routine choices?

It often helps to think through modern problems with historical anecdotes. The case of Henrietta Lacks, a Black woman living in Baltimore stricken with cervical cancer, and her everlasting cells, has become well-known because of Rebecca Skloot’s book, The Immortal Life of Henrietta Lacks,and a movie starring Oprah Winfrey. Unbeknownst to her, Lacks’s medical team removed her cancer cells and sent them to a lab to see if they would grow. While Lacks died of cancer in 1951, her cells didn’t. They kept going, in petri dishes in labs, all the way through to the present day.

The unprecedented persistence of Lacks’s cells led to the creation of the HeLa cell line. Her cells underpin various medical technologies, from in-vitro fertilization to polio and COVID-19 vaccines, generating immense wealth for pharmaceutical companies. HeLa is a co-creation. Without Lacks or scientific motivation, there would be no HeLa.

The case raises questions about consent and ownership. That her descendants recently settled a lawsuit against Thermo Fisher Scientific, a pharmaceutical company that monetized products made from HeLa cells, echoes the continuing discourse surrounding data ownership and rights. Until the settlement, just one co-creator was reaping all the financial benefits of that creation.

The Lacks family’s legal battle centred on a human-rights claim. Their situation was rooted in the impact of Lacks’s cells on medical science and the intertwined racial inequalities that lead to disparate medical outcomes. Since Lacks’s death, the family had struggled while biotech companies profited.

These “tissue issues” often don’t favour the individuals providing the cells or body parts. The U.S. Supreme Court case Moore v. Regents of the University of California deemed body parts as “garbage” once separated from the individual. The ruling highlights a harsh legal reality: Individuals don’t necessarily retain rights of parts of their body, financial or otherwise. Another federal case, Washington University v. Catalona, invalidated ownership claims based upon the “feeling” it belongs to the person it came from.

We can liken this characterization of body parts to how we often think about data taken from people. When we call data “detritus” or “exhaust,” we dehumanize the thoughts, behaviours and choices that generate those data. Do we really want to say that data, once created, is a resource for others’ exploitation?…(More)”.

When is a Decision Automated? A Taxonomy for a Fundamental Rights Analysis

Curated on Posted on by Stefaan Verhulst

Paper by Francesca Palmiotto: “This paper addresses the pressing issues surrounding the use of automated systems in public decision-making, with a specific focus on the field of migration, asylum, and mobility. Drawing on empirical research conducted for the AFAR project, the paper examines the potential and limitations of the General Data Protection Regulation and the proposed Artificial Intelligence Act in effectively addressing the challenges posed by automated decision making (ADM). The paper argues that the current legal definitions and categorizations of ADM fail to capture the complexity and diversity of real-life applications, where automated systems assist human decision-makers rather than replace them entirely. This discrepancy between the legal framework and practical implementation highlights the need for a fundamental rights approach to legal protection in the automation age. To bridge the gap between ADM in law and practice, the paper proposes a taxonomy that provides theoretical clarity and enables a comprehensive understanding of ADM in public decision-making. This taxonomy not only enhances our understanding of ADM but also identifies the fundamental rights at stake for individuals and the sector-specific legislation applicable to ADM. The paper finally calls for empirical observations and input from experts in other areas of public law to enrich and refine the proposed taxonomy, thus ensuring clearer conceptual frameworks to safeguard individuals in our increasingly algorithmic society…(More)”.