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The 2002 sci-fi thriller Minority Report depicts a dystopian future where a specialized police unit is tasked with arresting people for crimes they have not yet committed. Directed by Steven Spielberg and based on a short story by Philip K. Dick, the drama revolves around PreCrimea system informed by a trio of psychics, or precogs, who anticipate future homicides, allowing police officers to intervene and prevent would-be assailants from claiming their targets lives. The film probes at hefty ethical questions: How can someone be guilty of a crime they havent yet committed? And what happens when the system gets it wrong? While there is no such thing as an all-seeing precog, key components of the future that Minority Report envisions have become reality even faster than its creators imagined. For more than a decade, police departments across the globe have been using data-driven systems geared toward predicting when and where crimes might occur and who might commit them. Far from an abstract or futuristic conceit, predictive policing is a reality. And market analysts are predicting a boom for the technology. Given the challenges in using predictive machine learning effectively and fairly, predictive policing raises significant ethical concerns. Absent technological fixes on the horizon, there is an approach to addressing these concerns: Treat government use of the technology as a matter of democratic accountability. Troubling history Predictive policing relies on artificial intelligence and data analytics to anticipate potential criminal activity before it happens. It can involve analyzing large datasets drawn from crime reports, arrest records and social or geographic information to identify patterns and forecast where crimes might occur or who may be involved. Law enforcement agencies have used data analytics to track broad trends for many decades. Todays powerful AI technologies, however, take in vast amounts of surveillance and crime report data to provide much finer-grained analysis. Police departments use these techniques to help determine where they should concentrate their resources. Place-based prediction focuses on identifying high-risk locations, also known as hot spots, where crimes are statistically more likely to happen. Person-based prediction, by contrast, attempts to flag individuals who are considered at high risk of committing or becoming victims of crime. These types of systems have been the subject of significant public concern. Under a so-called intelligence-led policing program in Pasco County, Florida, the sheriffs department compiled a list of people considered likely to commit crimes and then repeatedly sent deputies to their homes. More than 1,000 Pasco residents, including minors, were subject to random visits from police officers and were cited for things such as missing mailbox numbers and overgrown grass. Four residents sued the county in 2021, and last year they reached a settlement in which the sheriffs office admitted that it had violated residents constitutional rights to privacy and equal treatment under the law. The program has since been discontinued. This is not just a Florida problem. In 2020, Chicago decommissioned its Strategic Subject List, a system where police used analytics to predict which prior offenders were likely to commit new crimes or become victims of future shootings. In 2021, the Los Angeles Police Department discontinued its use of PredPol, a software program designed to forecast crime hot spots but was criticized for low accuracy rates and reinforcing racial and socioeconomic biases. Necessary innovations or dangerous overreach? The failure of these high-profile programs highlights a critical tension: Even though law enforcement agencies often advocate for AI-driven tools for public safety, civil rights groups and scholars have raised concerns over privacy violations, accountability issues, and the lack of transparency. And despite these high-profile retreats from predictive policing, many smaller police departments are using the technology. Most American police departments lack clear policies on algorithmic decision-making and provide little to no disclosure about how the predictive models they use are developed, trained, or monitored for accuracy or bias. A Brookings Institution analysis found that in many cities, local governments had no public documentation on how predictive policing software functioned, what data was used, or how outcomes were evaluated. This opacity is whats known in the industry as a black box. It prevents independent oversight and raises serious questions about the structures surrounding AI-driven decision-making. If a citizen is flagged as high-risk by an algorithm, what recourse do they have? Who oversees the fairness of these systems? What independent oversight mechanisms are available? These questions are driving contentious debates in communities about whether predictive policing as a method should be reformed, more tightly regulated, or abandoned altogether. Some people view these tools as necessary innovations, while others see them as dangerous overreach. A better way in San Jose But there is evidence that data-driven tools grounded in democratic values of due process, transparency, and accountability may offer a stronger alternative to todays predictive policing systems. What if the public could understand how these algorithms function, what data they rely on, and what safeguards exist to prevent discriminatory outcomes and misuse of the technology? The city of San Jose, California, has embarked on a process that is intended to increase transparency and accountability aroud its use of AI systems. San Jose maintains a set of AI principles requiring that any AI tools used by city government be effective, transparent to the public, and equitable in their effects on peoples lives. City departments also are required to assess the risks of AI systems before integrating them into their operations. If taken correctly, these measures can effectively open the black box, dramatically reducing the degree to which AI companies can hide their code or their data behind things such as protections for trade secrets. Enabling public scrutiny of training data can reveal problems such as racial or economic bias, which can be mitigated but are extremely difficult if not impossible to eradicate. Research has shown that when citizens feel that government institutions act fairly and transparently, they are more likely to engage in civic life and support public policies. Law enforcement agencies are likely to have stronger outcomes if they treat technology as a toolrather than a substitutefor justice. Maria Lungu is a postdoctoral researcher of law and public administration at the University of Virginia. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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E-Commerce
Given President Donald Trump’s well-established penchant for golden objects, it was not a surprise to many when images of his administration’s decorative choices in the Oval Office started appearing. The space now abounds in gold. There are gold picture frames, gold statues, gold trophies, gold crown molding, and gold coasters. The Wall Street Journal reported that Trump called in his Mar-a-Lago “gold guy” to assist with the redesign, adding custom-made and gilded carvings. Compared to those of presidents past, Trump’s Oval Office decor is a maximalist and glistening tour de force. To some, the decoration is all a bit much. New York magazine called the overall decorative approach in the White House “tacky and trollish.” An opinion piece in The Washington Post called the decoration “gaudy-awful.” Another drew a direct line between Trump’s decorative leanings and the over-the-top opulence of the Palace of Versailles. From left: President George W. Bush, circa 2008, and Treasury Secretary Scott Bessent under the recently gilded entry, 2025 [Photo: Jim Watson/AFP/Getty Images, Chip Somodevilla/Getty Images] “In order to gain a certain kind of reputational notoriety he emulates this style that’s connected with the elite. But he does so in a very pastiche way,” says Robert Wellington, a professor of art history at the Australian National University and a specialist in the arts in France during the reign of Louis XIV. Trump, who has called “the look and feel of Louis XIV” his “favorite style,” has interpreted this period mainly through items that are, or look like, gold. “Perhaps he’s trying to create a sense of material splendor around him that gives a sense of power and buttresses his claims to the success that his administration is having,” Wellington says. “He wants to give that illusion of success.” From left: Oval Office seating area, circa 2010, and 2025 [Photo: Brendan Smialowski/Getty Images, Brendan Smialowski/AFP/Getty Images] What are those things? According to information from the White House, some items in Trump’s Oval Office actually do have legitimate value, both materially and historically. On top of the mantle, there is a line of seven historic items from the White House collection dating back to the early and mid-19th century. This is the lineup, according to details from the White House curator’s office, that was provided by a source in the White House. On the outside edges there are two gilded silver dessert stands made around 1810. Next to those are two gilded silver figurative centerpieces made around 1843. Next to those are two gilded bronze vases made around 1817 and associated with James Monroe, the fifth president of the United States. And in the center is a gilded bronze basket made between 1815 and 1820. All the items originated in either England or France. [Photo: Brendan Smialowski/AFP/Getty Images] The provenance of these pieces may have some subversive significance for those who read between the lines. The four outermost pieces were bequests from Margaret Thompson Biddle, heir to a diamond- and copper-mining fortune and one of the richest American women of the mid-20th century. Once married to a diplomat, she lived for many of the pre- and post-World War II years in Europe, and hosted famous salons in her Paris home with the leading lights of American and French society. The centerpiece was a gift of Gifford B. Pinchot, an early trustee of the Natural Resources Defense Council, an organization that by its own accounting sued the first Trump administration 163 times. Pinchot, who donated the piece in 1973 and died in 1989, was the son of Gifford Pinchot, the first head of the U.S. Forest Service and a close ally of Theodore Roosevelt, the 26th U.S. president, with whom he helped formulate the federal government’s approach to resource conservation. Neither of these people would seem ideologically connected to the current administration’s policies. Made in the USA? Not in the White House More notable, perhaps, is the fact that none of the items on the mantle in Trump’s Oval Office were made in the U.S., which contrasts with the administration’s present focus on imposing tariffs on foreign-produced goods and services. “There is a long passion for French decorative arts in America, through the Gilded Age patrons but also in the White House itself. So it’s not completely outrageous to imagine these French styles coming into the White House,” Wellington says. The Hall of Mirrors in Versailles [Photo: Jessica Kantak Bailey/Unsplash] But Wellington also sees a deep irony in Trump’s affection for Louis XIV and the Palace of Versailles, which he explores in a forthcoming book, Versailles Mirrored: The Power of Luxury, Louis XIV to Donald Trump. Wellington notes that Versailles was built as a kind of advertising program, establishing France as the center for luxury production by putting its finest craftsmanship in furniture, metalwork, mirrors, silks, and paintings on display. The palace’s decorative approach was also a form of protectionism, meant to stop people from importing luxury products from other countries. “It was state-sponsored luxury production which led to France being seen as the place where the very finest things could be made,” Wellington says. Trump’s version of Louis XIV’s approach is more surface than substance, Wellington says. In contrast with the industry-boosting decoration at Versailles, the White House decor undermines one of the administration’s key policies. “If Trump wanted to be a Louis XIV, I think he would be well placed to support the arts and culture. Instead, there’s very regressive ideas about arts and culture being supported under the Trump administration,” he says. “To be a great model of patronage you would be looking to the greatest minds of the day, the greatest artists of the day to create an image of America, to make America great again,” Wellington adds. “The way that you would do that is to think to the future, not to lock into some old idea.”
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E-Commerce
Generative AI is radically reshaping the job marketcreating new roles, changing some, and phasing out others. But heres one effect of the transformative technology thats not as widely talked about: Its deepening long-standing workplace gender gaps. A double disadvantage According to a recent report from the World Economic Forum and LinkedIn, women systematically face a two-part problem in the ongoing AI transformation. Relatively fewer women are currently in jobs that are being augmented by generative AI, and relatively more are in roles that are being disrupted. According to LinkedIn data for the US, 24.1% of men work in augmented occupations, while 20.5% of women do. At the same time, 33.7% of women work in occupations that are being disrupted, compared to 25.5% of men. Related research by LinkedIn shows that the pattern of mens higher representation in augmented roles holds for 95% of the 74 countries with available data. Examples of occupations that look set to be disrupted in the US include medical administrative assistant (91% female) and office manager (88% female). Augmented fields, meanwhile, include electrical engineer (94% male) and mechanical engineer (89% male). The STEM Gap The data align with broader AI-related disparities in STEM education and employment. Already, too many women are lost in the transition from STEM degrees to their first job in the STEM workforce. Women who graduated in 2021 accounted for 38.5% of STEM graduates, but only 31.6% of STEM job entrants in 2022. This decline in representation continues across the hierarchy once women are in the workforce: in 2024, women held 29% of STEM entry-level positions and 24.4% of STEM managerial positions in STEM, but only 12.2% of STEM C-suite level roles. Women are also underrepresented in AI-related academic and leadership roles.To ensure that those who have the right skills have a fair chance to succeed and advance in the workplace, regardless of their gender, business leaders need to review and rethink their hiring practices, performance evaluation methods, and promotion processes. Generative AI itself can both help and hinder efforts to create a more level playing field. Relying on historical employment patterns to make predictions about future performance has too often overlooked womens potential to succeed in jobs where they have not traditionally been represented. On the other hand, using generative AI to predict future success based on current skills is a powerful way to deploy the latest technology to debias hiring processes and create a more level playing field. Some positive news When it comes to AI skills, there are encouraging signs that women are catching up on both AI literacy and AI engineering skills. In 2018, 23.5% of AI engineering skill-listers on LinkedIn were women; in early 2025, this number had risen to 29.4%. Over the past five years, the gap narrowed in 74 of the 75 economies with available data. At the same time, research by LinkedIn suggests that women are more likely to underreport AI skills in their professional profiles. Disparity among inventors Currently, no economy is fully leveraging all of the available talent to drive innovation, but some are doing better than others. In a race where every competitive edge counts, this is significant. High-level data on the gender breakdown among inventors, named as such on patent applications, reveals that East Asian economies are drawing on a more extensive talent pool, with more than 25% of inventors being women in China (26.8% in 2019) and South Korea (28.3% in 2019), which is around 10 percentage points higher than in the European Union (EU) and the United States. Around the world, the generative AI boom is being shaped in ways that dont fully reflect the diversity of society, leaving women underrepresented in the jobs and leadership roles of the future. Yet this moment offers a rare opportunity to course-correct. By investing in skills, using AI in a way that makes hiring and promotions more equitable, and ensuring technology is built by and for a broader range of people, we can create a more competitive future that expands economic opportunity and promotes fairness. Without such action, generative AI will reinforce inequality instead of driving meaningful progress.
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E-Commerce
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