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Last week marked Trump’s first 100 days in officeand when it comes to climate policy, things are already pretty grim. In just three months, the president has elevated fossil fuels, pulled back billions from green investments, and implemented tariffs that make it harder to import renewable infrastructure. And that’s just the start. But despite Trumps relentless attacks on the environment, there are still some bright spotsfrom climate startups, from cities and states, and from nonprofits that are taking these actions to court. In a recent conversation, Fast Company senior editor Aimee Rawlins and executive editor Morgan Clendaniel discussed some of Trump’s most significant attacks on the environmentas well as the ways that people are pushing back. If you missed the subscriber-only event, you can catch the whole conversation in the video above.
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At a recent fundraising event, I stood backstage with a young woman waiting to give a speech in front of 550 people to honor her alma mater. She was visibly nervous; I watched as she paced, taking deep breaths to calm the adrenaline that was flooding her body, twisting her hands, and looking toward the stage door as if she might try to make a run for it. As a charity auctioneer who has spent more than two decades on stages around the world commanding rooms filled with thousands of people, I know that feeling. There were so many nights in the beginning of my career when I felt the same way. But spending 1,000-plus nights onstage has given me plenty of practice to learn what it takes to cultivate confidenceand keep itin my professional and personal life. Here are two easy mindset shifts that anyone can use to boost their confidence walking into any room. Find Your Strike Method Every time I walk onstage as the auctioneer, I bring a small gavel to bang down three times to get the attention of the crowd. After many years of hearing the gavel crack three times against the podium, I associate the habit with the most confident version of me. On nights when I forgot my gavel, I started to use anything within reach to mimic the motion and sounda saltshaker from the table, a tube of lipstick, even a hockey puck when I was taking an auction at a hockey arena. I also found I could use this offstage as well. Whenever I am about to step into a difficult meeting or a big presentation and find my confidence starting to recede, I visualize the gavel strike. The familiar method reminds me to throw my shoulders back, put a smile on my face, and walk in like I own the room. For my book The Most Powerful Woman in the Room Is You, I interviewed professional athletes, CEOs, and people at the top of their industry and realized that most successful people have their own version of a strike method. Whether it be a mantra, an action, or a physical item, there is something that helps them conjure strength and confidence in the moments when they dont feel it. To cultivate confidence, spend time thinking about the moments when you feel your most powerful. Come up with your own mantra or action that you can use anytime you arent feeling your most confident, and let your strike method empower you to move forward. Nail Your Sales Pitch When it comes to shifting your confidence mindset, inner work is as important as anything you do in your personal or professional life. You may dream about building the biggest business in the world and spend hours designing your dream life, but if you havent mastered your sales pitch, you will always find yourself fumbling when asked a question about yourself. Sit down with a piece of paper in a quiet place and write down a list of everything you want people to know about you: what you are good at, what you do, where you are headed. If you find that you have written about things that you no longer want to be known for, simply cross them off the list. Once that list is complete, spend some time writing a 30-second sales pitch that describes these key details: who you are, what you are doing, and, most importantly, where you are headed. Articulating this vision and putting it into a concise sales pitch will put you in control the next time someone asks, So tell me about you. A few minutes before the nervous young woman stepped onstage, I walked over and gave her an unsolicited hype speech to help her own the moment. You are going to rock this speech, and everyone in the audience is ready to watch you command the stage. I saw her stand up taller, still nervous but now ready to take up space on the stage. Confidence ebbs and flows, so dont forget to surround yourself with people who believe in youand give you a hype speech on the days when you arent feeling the most confident.
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E-Commerce
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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